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Deep Learning for Symbolic Mathematics

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arxiv 1912.01412 v1 pith:ISCUR4AY submitted 2019-12-02 cs.SC cs.LG

Guillaume Lample , François Charton This is my paper · ORCID
classification cs.SCcs.LG
keywords symbolicmathematicsproblemssolvingachievealgebraapproximatebetter
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Neural networks have a reputation for being better at solving statistical or approximate problems than at performing calculations or working with symbolic data. In this paper, we show that they can be surprisingly good at more elaborated tasks in mathematics, such as symbolic integration and solving differential equations. We propose a syntax for representing mathematical problems, and methods for generating large datasets that can be used to train sequence-to-sequence models. We achieve results that outperform commercial Computer Algebra Systems such as Matlab or Mathematica.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Neuro-Symbolic ODE Discovery with Latent Grammar Flow

    cs.LG 2026-04 unverdicted novelty 7.0 of 10

    Latent Grammar Flow embeds grammar-based ODE representations into a discrete latent space with a behavioural loss and samples candidate equations via discrete flow to fit observed data.

  2. Learning neuro-symbolic convergent term rewriting systems

    cs.AI 2025-07 conditional novelty 6.0 of 10

    Two modular neuro-symbolic systems learn to simplify formulas by imitating term rewriting steps, and the new FastNRS variant generalizes to deeper formulas while being far faster than the original.

  3. A Better Multi-Objective GP-GOMEA -- But do we Need it?

    cs.NE 2025-07 conditional novelty 6.0 of 10

    For accuracy vs expression size, single-objective GP-GOMEA with an archive-only multi-objective log outperforms true multi-objective GP-GOMEA, despite new clustering improvements.

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