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DEEP LEARNING FOR SYMBOLIC MATHEMATICS

Comments

Sep 26 2019 Ilya Sutskever

Really enjoyed the (non OpenAI) ICLR submission https://openreview.net/pdf?id=S1eZYeHFDS that trained a transformer on symbolic math. The surprise: it beat Mathematica on symbolic integration and diff eq solving by a _very_ big margin!
12 replies, 1096 likes


Sep 26 2019 Erik Bernhardsson

This paper uses seq2seq to do symbolic integrals by training it on (f’(x), f(x)) pairs and it’s such an obviously great idea that I wish I thought of first! I can’t be the only one having that feeling. https://openreview.net/pdf?id=S1eZYeHFDS
10 replies, 157 likes


Sep 27 2019 Christian Szegedy

Approximate mathematical reasoning is possible in the latent space alone. We created semantic embedding of formulas and performed complicated multi-step reasoning on them, then we compared it with the symbolic results: http://arxiv.org/pdf/1909.11851v1
3 replies, 154 likes


Sep 27 2019 algoritmic

Deep Learning for Symbolic Mathematics https://openreview.net/pdf?id=S1eZYeHFDS #math https://t.co/5CxO0LR1Hn
1 replies, 109 likes


Sep 26 2019 Hacker News

Deep Learning for Symbolic Mathematics https://openreview.net/pdf?id=S1eZYeHFDS
0 replies, 38 likes


Nov 03 2019 Stephen Diehl

This is one of the wilder applications I've seen for deep learning. Page 8: "This suggest that some deeper understanding of [differential equation] mathematics has been achieved by the model." https://openreview.net/pdf?id=S1eZYeHFDS
1 replies, 25 likes


Sep 27 2019 P. Oscar Boykin

What I like about this is that it feels like how people solve inverse problems: use ✨intuition✨ to guess a solution, then check that it is correct (and maybe apply a delta to solve rest). Seems like this paper show how to implement a computer algebra system to do that.
0 replies, 17 likes


Sep 26 2019 Timothy Gowers

Trying to decide where this appears on the spectrum that runs from quite amusing at one end to game changer at the other. At a first glance it seems pretty impressive.
3 replies, 15 likes


Sep 26 2019 Xander Steenbrugge

This is brilliant & absolutely stunning! After all these years of Deep Learning I'm still amazed at what a neural net can do when you provide the right input representations & throw stupendous amounts of computation at it... 😲👏
0 replies, 14 likes


Oct 02 2019 Cam DP

This but for deconvolution problems, too.
0 replies, 6 likes


Sep 26 2019 Eclipse DL4J

DEEP LEARNING FOR SYMBOLIC MATHEMATICS https://openreview.net/pdf?id=S1eZYeHFDS
0 replies, 4 likes


Sep 26 2019 Peter Bloem

With just 6 layers. You could probably fit the whole model in 12Gb of GPU memory.
0 replies, 3 likes


Sep 27 2019 Barney Pell

Wow! Deep learning performing much better than symbolic math solvers.
0 replies, 3 likes


Sep 26 2019 Melanie Mitchell

Fascinating!!
0 replies, 1 likes


Sep 26 2019 mats-erik pistol, the man in the middle.

For a while I thought it was April 1st. Transformer networks beat Mathematica by a large margin when doing symbolic integration and finding solutions to differential equations. The singularity is near: https://openreview.net/pdf?id=S1eZYeHFDS
1 replies, 0 likes


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