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Neural Erosion: Emulating Controlled Neurodegeneration and Aging in AI Systems (2403.10596v1)
Published 15 Mar 2024 in cs.CL, cs.AI, and q-bio.NC
Abstract: Creating controlled methods to simulate neurodegeneration in AI is crucial for applications that emulate brain function decline and cognitive disorders. We use IQ tests performed by LLMs and, more specifically, the LLaMA 2 to introduce the concept of ``neural erosion." This deliberate erosion involves ablating synapses or neurons, or adding Gaussian noise during or after training, resulting in a controlled progressive decline in the LLMs' performance. We are able to describe the neurodegeneration in the IQ tests and show that the LLM first loses its mathematical abilities and then its linguistic abilities, while further losing its ability to understand the questions. To the best of our knowledge, this is the first work that models neurodegeneration with text data, compared to other works that operate in the computer vision domain. Finally, we draw similarities between our study and cognitive decline clinical studies involving test subjects. We find that with the application of neurodegenerative methods, LLMs lose abstract thinking abilities, followed by mathematical degradation, and ultimately, a loss in linguistic ability, responding to prompts incoherently. These findings are in accordance with human studies.
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Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural computation 9, 1735–1780 (1997). [4] Mozer, M. C. The neural network house: An environment hat adapts to its inhabitants. Proc. AAAI Spring Symp. Intelligent Environments 58 (1998). [5] Raghavendra, U., Acharya, U. R. & Adeli, H. Artificial intelligence techniques for automated diagnosis of neurological disorders. European neurology 82, 41–64 (2020). [6] Xu, Y. et al. Artificial intelligence: A powerful paradigm for scientific research. The Innovation 2 (2021). [7] Tuladhar, A., Moore, J. A., Ismail, Z. & Forkert, N. D. Modeling neurodegeneration in silico with deep learning. Frontiers in Neuroinformatics 15, 748370 (2021). [8] Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mozer, M. C. The neural network house: An environment hat adapts to its inhabitants. Proc. AAAI Spring Symp. Intelligent Environments 58 (1998). [5] Raghavendra, U., Acharya, U. R. & Adeli, H. Artificial intelligence techniques for automated diagnosis of neurological disorders. European neurology 82, 41–64 (2020). [6] Xu, Y. et al. Artificial intelligence: A powerful paradigm for scientific research. The Innovation 2 (2021). [7] Tuladhar, A., Moore, J. A., Ismail, Z. & Forkert, N. D. Modeling neurodegeneration in silico with deep learning. Frontiers in Neuroinformatics 15, 748370 (2021). [8] Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Raghavendra, U., Acharya, U. R. & Adeli, H. Artificial intelligence techniques for automated diagnosis of neurological disorders. European neurology 82, 41–64 (2020). [6] Xu, Y. et al. Artificial intelligence: A powerful paradigm for scientific research. The Innovation 2 (2021). [7] Tuladhar, A., Moore, J. A., Ismail, Z. & Forkert, N. D. Modeling neurodegeneration in silico with deep learning. Frontiers in Neuroinformatics 15, 748370 (2021). [8] Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Xu, Y. et al. Artificial intelligence: A powerful paradigm for scientific research. The Innovation 2 (2021). [7] Tuladhar, A., Moore, J. A., Ismail, Z. & Forkert, N. D. Modeling neurodegeneration in silico with deep learning. Frontiers in Neuroinformatics 15, 748370 (2021). [8] Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Tuladhar, A., Moore, J. A., Ismail, Z. & Forkert, N. D. Modeling neurodegeneration in silico with deep learning. Frontiers in Neuroinformatics 15, 748370 (2021). [8] Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. 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Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
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Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. 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Artificial intelligence techniques for automated diagnosis of neurological disorders. European neurology 82, 41–64 (2020). [6] Xu, Y. et al. Artificial intelligence: A powerful paradigm for scientific research. The Innovation 2 (2021). [7] Tuladhar, A., Moore, J. A., Ismail, Z. & Forkert, N. D. Modeling neurodegeneration in silico with deep learning. Frontiers in Neuroinformatics 15, 748370 (2021). [8] Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mozer, M. C. The neural network house: An environment hat adapts to its inhabitants. Proc. AAAI Spring Symp. Intelligent Environments 58 (1998). [5] Raghavendra, U., Acharya, U. R. & Adeli, H. Artificial intelligence techniques for automated diagnosis of neurological disorders. European neurology 82, 41–64 (2020). [6] Xu, Y. et al. Artificial intelligence: A powerful paradigm for scientific research. The Innovation 2 (2021). [7] Tuladhar, A., Moore, J. A., Ismail, Z. & Forkert, N. D. Modeling neurodegeneration in silico with deep learning. Frontiers in Neuroinformatics 15, 748370 (2021). [8] Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Raghavendra, U., Acharya, U. R. & Adeli, H. Artificial intelligence techniques for automated diagnosis of neurological disorders. European neurology 82, 41–64 (2020). [6] Xu, Y. et al. Artificial intelligence: A powerful paradigm for scientific research. The Innovation 2 (2021). [7] Tuladhar, A., Moore, J. A., Ismail, Z. & Forkert, N. D. Modeling neurodegeneration in silico with deep learning. Frontiers in Neuroinformatics 15, 748370 (2021). [8] Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Xu, Y. et al. Artificial intelligence: A powerful paradigm for scientific research. The Innovation 2 (2021). [7] Tuladhar, A., Moore, J. A., Ismail, Z. & Forkert, N. D. Modeling neurodegeneration in silico with deep learning. Frontiers in Neuroinformatics 15, 748370 (2021). [8] Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Tuladhar, A., Moore, J. A., Ismail, Z. & Forkert, N. D. Modeling neurodegeneration in silico with deep learning. Frontiers in Neuroinformatics 15, 748370 (2021). [8] Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. 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Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
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Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Raghavendra, U., Acharya, U. R. & Adeli, H. Artificial intelligence techniques for automated diagnosis of neurological disorders. European neurology 82, 41–64 (2020). [6] Xu, Y. et al. Artificial intelligence: A powerful paradigm for scientific research. The Innovation 2 (2021). [7] Tuladhar, A., Moore, J. A., Ismail, Z. & Forkert, N. D. Modeling neurodegeneration in silico with deep learning. Frontiers in Neuroinformatics 15, 748370 (2021). [8] Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Xu, Y. et al. Artificial intelligence: A powerful paradigm for scientific research. The Innovation 2 (2021). [7] Tuladhar, A., Moore, J. A., Ismail, Z. & Forkert, N. D. Modeling neurodegeneration in silico with deep learning. Frontiers in Neuroinformatics 15, 748370 (2021). [8] Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. 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[11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. 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Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. 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Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. 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Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. 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Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. 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Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. 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[7] Tuladhar, A., Moore, J. A., Ismail, Z. & Forkert, N. D. Modeling neurodegeneration in silico with deep learning. Frontiers in Neuroinformatics 15, 748370 (2021). [8] Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. 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Tuladhar, A., Moore, J. A., Ismail, Z. & Forkert, N. D. Modeling neurodegeneration in silico with deep learning. Frontiers in Neuroinformatics 15, 748370 (2021). [8] Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. 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Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. 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Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. 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[20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. 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[19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. 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Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
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[17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Xu, Y. et al. Artificial intelligence: A powerful paradigm for scientific research. The Innovation 2 (2021). [7] Tuladhar, A., Moore, J. A., Ismail, Z. & Forkert, N. D. Modeling neurodegeneration in silico with deep learning. Frontiers in Neuroinformatics 15, 748370 (2021). [8] Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Tuladhar, A., Moore, J. A., Ismail, Z. & Forkert, N. D. Modeling neurodegeneration in silico with deep learning. Frontiers in Neuroinformatics 15, 748370 (2021). [8] Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. 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Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. 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Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. 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Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
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Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Moore, J. A. et al. Dementia in convolutional neural networks: Using deep learning models to simulate neurodegeneration of the visual system. Neuroinformatics 21, 45–55 (2023). [9] Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Alexos, A., Panousis, K. P. & Chatzis, S. Local competition and uncertainty for adversarial robustness in deep learning. arXiv preprint arXiv:2006.10620 (2020). [10] Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
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Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. 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Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. 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[16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
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Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. 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[20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. 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[16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
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Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science 1, 417–446 (2015). [11] Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
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Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Khatami, S. G., Mubeen, S. & Hofmann-Apitius, M. Data science in neurodegenerative disease: its capabilities, limitations, and perspectives. Current opinion in neurology 33, 249 (2020). [12] Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Peraza-Goicolea, J. A., Martínez-Montes, E., Aubert, E., Valdés-Hernández, P. A. & Mulet, R. Modeling functional resting-state brain networks through neural message passing on the human connectome. Neural Networks 123, 52–69 (2020). [13] Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. Communications biology 4, 301 (2021). [14] Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Vanasse, T. J. et al. Brain pathology recapitulates physiology: A network meta-analysis. 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[19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. 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Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
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Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Horn, D., Ruppin, E., Usher, M. & Herrmann, M. Neural network modeling of memory deterioration in alzheimer’s disease. Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. 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Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. 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Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
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Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
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Neural computation 5, 736–749 (1993). [15] Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Ma, X., Fang, G. & Wang, X. LLM-pruner: On the structural pruning of large language models. Thirty-seventh Conference on Neural Information Processing Systems (2023). [16] Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. 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Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
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[19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
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Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sun, M., Liu, Z., Bair, A. & Kolter, J. Z. A simple and effective pruning approach for large language models. arXiv preprint arXiv:2306.11695 (2023). [17] Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
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Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kim, Y. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 1746–1751 (2014). [18] Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. 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Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
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Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
- Touvron, H. et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023). [19] Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
- Kazanova, M. Sentiment140 dataset with 1.6 million tweets. https://www.kaggle.com/datasets/kazanova/sentiment140. [20] https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). https://crawling-dugong-7fa.notion.site/LLM-IQ-cfe70674bff34107a4d22e0f68b9cfe3. [21] Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
- Sperling, R. A. et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292 (2011). [22] Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Banovic, S., Zunic, L. J. & Sinanovic, O. Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
- Communication difficulties as a result of dementia. Materia socio-medica 30, 221 (2018). [23] Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018). Mueller, K. D., Koscik, R. L., Hermann, B. P., Johnson, S. C. & Turkstra, L. S. Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
- Declines in connected language are associated with very early mild cognitive impairment: Results from the wisconsin registry for alzheimer’s prevention. Frontiers in Aging Neuroscience 9, 437 (2018).
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