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Distilling Wikipedia mathematical knowledge into neural network models
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abstract
Machine learning applications to symbolic mathematics are becoming increasingly popular, yet there lacks a centralized source of real-world symbolic expressions to be used as training data. In contrast, the field of natural language processing leverages resources like Wikipedia that provide enormous amounts of real-world textual data. Adopting the philosophy of "mathematics as language," we bridge this gap by introducing a pipeline for distilling mathematical expressions embedded in Wikipedia into symbolic encodings to be used in downstream machine learning tasks. We demonstrate that a $\textit{mathematical}$ $\textit{language}$ $\textit{model}$ trained on this "corpus" of expressions can be used as a prior to improve the performance of neural-guided search for the task of symbolic regression.
Forward citations
Cited by 3 Pith papers
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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks
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DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces
A generative optimization method that jointly samples discrete and continuous design variables outperforms decoupled skeleton-then-optimize baselines in sample efficiency across bitstring, decision-tree, and symbolic ...
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Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics
DSO uses an autoregressive model trained with a risk-seeking policy gradient to search symbolic expressions, and its unified version outperforms baselines on symbolic regression benchmarks.
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