Bootstrapping OTS-Funcimg Pre-training Model (Botfip) -- A Comprehensive Symbolic Regression Framework (2401.09748v1)
Abstract: In the field of scientific computing, many problem-solving approaches tend to focus only on the process and final outcome, even in AI for science, there is a lack of deep multimodal information mining behind the data, missing a multimodal framework akin to that in the image-text domain. In this paper, we take Symbolic Regression(SR) as our focal point and, drawing inspiration from the BLIP model in the image-text domain, propose a scientific computing multimodal framework based on Function Images (Funcimg) and Operation Tree Sequence (OTS), named Bootstrapping OTS-Funcimg Pre-training Model (Botfip). In SR experiments, we validate the advantages of Botfip in low-complexity SR problems, showcasing its potential. As a MED framework, Botfip holds promise for future applications in a broader range of scientific computing problems.
- Neural symbolic regression that scales. In International Conference on Machine Learning, pages 936–945. PMLR, 2021.
- Symbolic regression analysis. In Classification, clustering, and data analysis: recent advances and applications, pages 281–288. Springer, 2002.
- Operon c++ an efficient genetic programming framework for symbolic regression. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pages 1562–1570, 2020.
- A review on multimodal zero-shot learning. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(2):e1488, 2023.
- Rademacher complexity for enhancing the generalization of genetic programming for symbolic regression. IEEE transactions on cybernetics, 52(4):2382–2395, 2020.
- Miles Cranmer. Interpretable machine learning for science with pysr and symbolicregression.jl. arXiv preprint arXiv:2305.01582, 2023.
- Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
- Statistical genetic programming for symbolic regression. Applied Soft Computing, 60:447–469, 2017.
- End-to-end symbolic regression with transformers. Advances in Neural Information Processing Systems, 35:10269–10281, 2022.
- Maple: Multi-modal prompt learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19113–19122, 2023.
- Deep learning for symbolic mathematics. arXiv preprint arXiv:1912.01412, 2019.
- A unified framework for deep symbolic regression. Advances in Neural Information Processing Systems, 35:33985–33998, 2022.
- Multimodal prompting with missing modalities for visual recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14943–14952, 2023.
- Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning, pages 12888–12900. PMLR, 2022.
- Interpretable scientific discovery with symbolic regression: a review. arXiv preprint arXiv:2211.10873, 2022.
- Symbolic regression via deep reinforcement learning enhanced genetic programming seeding. Advances in Neural Information Processing Systems, 34:24912–24923, 2021.
- Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. arXiv preprint arXiv:1912.04871, 2019.
- Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PMLR, 2021.
- Gptips: an open source genetic programming toolbox for multigene symbolic regression. In Proceedings of the International multiconference of engineers and computer scientists, volume 1, pages 77–80. Citeseer, 2010.
- Ai feynman: A physics-inspired method for symbolic regression. Science Advances, 6(16):eaay2631, 2020.
- Semantically-based crossover in genetic programming: application to real-valued symbolic regression. Genetic Programming and Evolvable Machines, 12:91–119, 2011.
- Causal attention for vision-language tasks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9847–9857, 2021.
- Li Yujian and Liu Bo. A normalized levenshtein distance metric. IEEE transactions on pattern analysis and machine intelligence, 29(6):1091–1095, 2007.
- Ps-tree: A piecewise symbolic regression tree. Swarm and Evolutionary Computation, 71:101061, 2022.
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