Deep Learning for Symbolic Mathematics | AISC

Discussion lead/authors: Francois Charton, Guillaume Lample

Abstract:
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.

Authors: Guillaume Lample, François Charton

[felipesuarez]

Nice Research. After you convert the equations to polish notation, do you create some kind of embedding for the different symbols, or just use some kind of hot encoding?