Learning advanced mathematical computations from examplesDownload PDF

Published: 12 Jan 2021, Last Modified: 05 May 2023ICLR 2021 PosterReaders: Everyone
Keywords: differential equations, computation, transformers, deep learning
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.
One-sentence Summary: We train transformers to predict qualitative and numerical properties of differential equations
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Supplementary Material: zip
Code: [![github](/images/github_icon.svg) facebookresearch/MathsFromExamples](https://github.com/facebookresearch/MathsFromExamples)
Data: [Action Recognition in the Dark](https://paperswithcode.com/dataset/action-recognition-in-the-dark)
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