MMSR: Symbolic Regression is a Multimodal Task

• URL: http://arxiv.org/abs/2402.18603v4
• Date: Thu, 14 Mar 2024 12:10:43 GMT
• Title: MMSR: Symbolic Regression is a Multimodal Task
• Authors: Yanjie Li, Jingyi Liu, Weijun Li, Lina Yu, Min Wu, Wenqiang Li, Meilan Hao, Su Wei, Yusong Deng,
• Abstract summary: Symbolic regression was originally formulated as a optimization problem, and GP and reinforcement learning algorithms were used to solve it. To solve this problem, researchers treat the mapping from data to expressions as a translation problem. In this paper, we propose MMSR, which achieves the most advanced results on multiple mainstream datasets.
• Score: 12.660401635672967
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mathematical formulas are the crystallization of human wisdom in exploring the laws of nature for thousands of years. Describing the complex laws of nature with a concise mathematical formula is a constant pursuit of scientists and a great challenge for artificial intelligence. This field is called symbolic regression. Symbolic regression was originally formulated as a combinatorial optimization problem, and GP and reinforcement learning algorithms were used to solve it. However, GP is sensitive to hyperparameters, and these two types of algorithms are inefficient. To solve this problem, researchers treat the mapping from data to expressions as a translation problem. And the corresponding large-scale pre-trained model is introduced. However, the data and expression skeletons do not have very clear word correspondences as the two languages do. Instead, they are more like two modalities (e.g., image and text). Therefore, in this paper, we proposed MMSR. The SR problem is solved as a pure multimodal problem, and contrastive learning is also introduced in the training process for modal alignment to facilitate later modal feature fusion. It is worth noting that in order to better promote the modal feature fusion, we adopt the strategy of training contrastive learning loss and other losses at the same time, which only needs one-step training, instead of training contrastive learning loss first and then training other losses. Because our experiments prove training together can make the feature extraction module and feature fusion module running-in better. Experimental results show that compared with multiple large-scale pre-training baselines, MMSR achieves the most advanced results on multiple mainstream datasets including SRBench.

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