ICLR 2021poster40 citations
Learning advanced mathematical computations from examples
Francois Charton, Amaury Hayat, Guillaume Lample
Abstract
Using transformers over large generated datasets, we train models to learn mathematical properties of differential systems, such as local stability, behavior at infinity and controllability. We achieve near perfect prediction of qualitative characteristics, and good approximations of numerical features of the system. This demonstrates that neural networks can learn to perform complex computations, grounded in advanced theory, from examples, without built-in mathematical knowledge.
differential equationscomputationtransformersdeep learning
BibTeX
@inproceedings{
charton2021learning,
title={Learning advanced mathematical computations from examples},
author={Francois Charton and Amaury Hayat and Guillaume Lample},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=-gfhS00XfKj}
}