Deep Learning for Symbolic Mathematics

Channel:
Switzerland Yannic Kilcher
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Published on ● Video Link: https://www.youtube.com/watch?v=p3sAF3gVMMA


Duration: 24:16
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This model solves integrals and ODEs by doing seq2seq!

https://arxiv.org/abs/1912.01412
https://ai.facebook.com/blog/using-neural-networks-to-solve-advanced-mathematics-equations/

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

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Tags:
deep learning
machine learning
nlp
natural language processing
machine translation
arxiv
attention mechanism
attention
transformer
rnn
recurrent
seq2seq
facebook
fair
research
math
integral
ode