arXiv:1912.05752cs.LG
The Use of Deep Learning for Symbolic Integration: A Review of (Lample and Charton, 2019)
Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.
Abstract
Lample and Charton (2019) describe a system that uses deep learning technology to compute symbolic, indefinite integrals, and to find symbolic solutions to first- and second-order ordinary differential equations, when the solutions are elementary functions. They found that, over a particular test set, the system could find solutions more successfully than sophisticated packages for symbolic mathematics such as Mathematica run with a long time-out. This is an impressive accomplishment, as far as it goes. However, the system can handle only a quite limited subset of the problems that Mathematica deals with, and the test set has significant built-in biases. Therefore the claim that this outperforms Mathematica on symbolic integration needs to be very much qualified.
Here's a somewhat more technical critique of the approach (and of the evaluation cases the authors used): https://arxiv.org/pdf/1912.05752.pdf
microtherionView on HNParent context