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Memory Mosaics (2405.06394v3)

Published 10 May 2024 in cs.LG, cs.AI, and cs.NE

Abstract: Memory Mosaics are networks of associative memories working in concert to achieve a prediction task of interest. Like transformers, memory mosaics possess compositional capabilities and in-context learning capabilities. Unlike transformers, memory mosaics achieve these capabilities in comparatively transparent way ("predictive disentanglement"). We illustrate these capabilities on a toy example and also show that memory mosaics perform as well or better than transformers on medium-scale LLMing tasks.

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References (33)
  1. In-context language learning: Arhitectures and algorithms. arXiv preprint arXiv:2401.12973, 2024.
  2. Multiple kernel learning, conic duality, and the smo algorithm. In Proceedings of the twenty-first international conference on Machine learning, page 6, 2004.
  3. Neural machine translation by jointly learning to align and translate. In Yoshua Bengio and Yann LeCun, editors, 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015.
  4. Yoshua Bengio. Deep learning of representations: Looking forward. In Statistical Language and Speech Processing: First International Conference, SLSP 2013, Tarragona, volume 7978, page 1. Springer, 2013.
  5. A meta-transfer objective for learning to disentangle causal mechanisms. arXiv preprint arXiv:1901.10912, 2019.
  6. Birth of a transformer: A memory viewpoint. Advances in Neural Information Processing Systems, 36, 2024.
  7. Slide: In defense of smart algorithms over hardware acceleration for large-scale deep learning systems. Proceedings of Machine Learning and Systems, 2:291–306, 2020.
  8. Pierre Comon. Independent Component Analysis, a new concept? Signal Processing, 36:287–314, April 1994.
  9. Tinystories: How small can language models be and still speak coherent english? arXiv preprint arXiv:2305.07759, 2023.
  10. The fast Gauss transform. SIAM Journal on Scientific and Statistical Computing, 12(1):79–94, 1991.
  11. Hippo: Recurrent memory with optimal polynomial projections. Advances in neural information processing systems, 33:1474–1487, 2020.
  12. Adaptive mixtures of local experts. Neural computation, 3(1):79–87, 1991.
  13. Mixtral of experts, 2024.
  14. Inferring algorithmic patterns with stack-augmented recurrent nets. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 28. Curran Associates, Inc., 2015.
  15. Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks. In International conference on machine learning, pages 2873–2882. PMLR, 2018.
  16. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017.
  17. In-context learning and induction heads, 2022.
  18. Train short, test long: Attention with linear biases enables input length extrapolation. In International Conference on Learning Representations, 2022.
  19. Improving language understanding by generative pre-training, 2018.
  20. Language models are unsupervised multitask learners, 2019.
  21. Disentanglement of correlated factors via hausdorff factorized support. In The Eleventh International Conference on Learning Representations, 2022.
  22. Learning internal representations by error propagation. In Parallel distributed processing: Explorations in the microstructure of cognition, volume I, pages 318–362. Bradford Books, Cambridge, MA, 1986.
  23. Dynamic routing between capsules. Advances in neural information processing systems, 30, 2017.
  24. A new unbiased and efficient class of lsh-based samplers and estimators for partition function computation in log-linear models, 2017.
  25. Roformer: Enhanced transformer with rotary position embedding. Neurocomputing, 568:127063, 2024.
  26. End-to-end memory networks. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 28. Curran Associates, Inc., 2015.
  27. Augmenting self-attention with persistent memory, 2019.
  28. Disentangling the independently controllable factors of variation by interacting with the world. arXiv preprint arXiv:1802.09484, 2018.
  29. Attention is all you need. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017.
  30. Legendre memory units: Continuous-time representation in recurrent neural networks. Advances in neural information processing systems, 32, 2019.
  31. Memory networks. arXiv preprint arXiv:1410.3916, 2014.
  32. Efficient streaming language models with attention sinks, 2023.
  33. Improved fast Gauss transform and efficient kernel density estimation. In Proceedings ninth IEEE International Conference on Computer Cision, pages 664–671. IEEE, 2003.
Citations (2)

Summary

  • The paper introduces Memory Mosaics as a transparent alternative to transformers by leveraging associative memory networks for clearer data processing.
  • The paper demonstrates that Memory Mosaics perform comparably to traditional transformers on medium-scale language tasks through predictive disentanglement.
  • The paper reveals that decomposing prediction tasks into manageable sub-tasks enhances both interpretability and the potential for broader applications.

Understanding Memory Mosaics: A Transparent Alternative to Transformers

Overview of Memory Mosaics

Memory Mosaics represent a novel architecture in machine learning that mirrors the functionalities of transformers but offers a markedly transparent way of processing data. At their core, Memory Mosaics utilize associative memory networks that collaborate to enhance prediction capabilities. This is similar to transformers which are revered for their compositional and in-context learning abilities. However, Memory Mosaics distinguish themselves by their clarity in executing these tasks, making the underlying processes more interpretable.

Key Contributions

The paper highlights several significant contributions from the memory mosaic architecture:

  1. Enhanced Transparency: Unlike the often opaque internal mechanisms of transformers, Memory Mosaics provide a clearer insight into how input data is processed and how predictions are formulated. This transparency stems from their associative memory-based structure which is easier to dissect than the self-attention mechanisms in transformers.
  2. Comparative Performance: In testing against medium-scale LLMing tasks, Memory Mosaics perform on par with, and sometimes better than, traditional transformer models. This shows that enhancing transparency does not come at the cost of performance efficacy.
  3. Predictive Disentanglement: A novel concept unveiled in this architecture is predictive disentanglement, where the prediction tasks are simplified into smaller, manageable sub-tasks. This not only simplifies the learning process but can potentially lead to models that generalize better to new, unseen data.

Understanding Associative Memories

Associative memory is a key component of Memory Mosaics. Here’s a breakdown of how they function:

  • Basics: Associative memories store and retrieve data as key-value pairs. These memories are adept at handling approximate matches and can operate without considering the temporal order of data — a property known as exchangeability.
  • Storage and Retrieval: They estimate a conditional probability distribution based on stored data to retrieve information. This process closely relates to kernel smoothing, a technique also foundational to self-attention mechanisms in transformers but is applied here in a way that enhances interpretability.

Practical Implications and Theoretical Insights

  • Advanced Interpretability: By simplifying the internal operations, Memory Mosaics make it easier for researchers and practitioners to understand and improve upon the model’s decision-making processes.
  • Flexibility and Disentanglement: The architecture facilitates a flexible decomposition of prediction tasks into simpler units that can be managed more efficiently and recombined dynamically to handle complex predictions.
  • Potential for Broad Applications: Initially proven on LLMs, the potential applications of Memory Mosaics span across various domains where transformers are currently utilized, including but not limited to, automated text generation, computational biology, and more.

Future Directions

Given their promising initial results and transparent operational nature, Memory Mosaics might spur continued research focusing on:

  • Scalability: Testing how well Memory Mosaics scale with increasingly large datasets and more complex prediction tasks, typical of scenarios handled by transformers.
  • Expanded Applications: Exploring other domains beyond LLMing to fully understand the breadth of applications for Memory Mosaics.
  • Evolution of Associative Memories: Innovating further on the architecture of associative memories might yield even more efficient and interpretable models.

Concluding Thoughts

Memory Mosaics represent a significant stride toward making complex AI models like transformers more interpretable without sacrificing performance. With their capability to disentangle and simplify prediction tasks, they not only make the internal workings of AI models less of a "black box" but also potentially enhance the ability of AI systems to generalize across various domains. The ongoing research and adaptation of this architecture could very well redefine the standards of model transparency and efficiency in the field.

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