Corpus ID: 209324010

The Use of Deep Learning for Symbolic Integration: A Review of (Lample and Charton, 2019)

@article{Davis2019TheUO,
  title={The Use of Deep Learning for Symbolic Integration: A Review of (Lample and Charton, 2019)},
  author={Ernest Davis},
  journal={ArXiv},
  year={2019},
  volume={abs/1912.05752}
}
  • E. Davis
  • Published 12 December 2019
  • Computer Science
  • ArXiv
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… Expand
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It is shown that neural networks can be surprisingly good at more elaborated tasks in mathematics, such as symbolic integration and solving differential equations, and a syntax for representing these mathematical problems, and methods for generating large datasets that can be used to train sequence-to-sequence models. Expand
Some Undecidable Problems Involving Elementary Functions of a Real Variable
Let E be a set of expressions representing real, single valued, partially defined functions of one real variable. E * will be the set of functions represented by expressions in E . If A is anExpand