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Discovering Symbolic Models from Deep Learning with Inductive Biases


Miles Cranmer: Very excited to share our new paper "Discovering Symbolic Models from Deep Learning with Inductive Biases"! We describe an approach to convert a deep model into an equivalent symbolic equation. Blog/code: Paper: Thread👇 1/n

29 replies, 2009 likes

hardmaru: Their network rediscovers known equations like force laws and Hamiltonians, and finds new interpretable closed-form expression for astrophysics. Cool work! I wonder if this type of approach can be applied outside the physical sciences, to social sciences, politics and economics.

12 replies, 520 likes

Miles Cranmer: Happy to announce that our work on converting deep models to symbolic equations has been accepted to NeurIPS! 🍾 @PeterWBattaglia @cosmo_shirley @DavidSpergel @KyleCranmer

3 replies, 329 likes

Shirley Ho: Thanks to @ykilcher for explaining our paper with a youtube video! Paper led by @MilesCranmer w/ @PeterWBattaglia @KyleCranmer @DavidSpergel

2 replies, 308 likes

Miles Cranmer: Here's a talk I gave at on (1) how to convert a neural network into a symbolic equation, and (2) why I think symbolic regression should be a primary ML algorithm in astrophysics. Lots of insightful questions from the audience!

2 replies, 135 likes

Yann LeCun: Awesome paper.

0 replies, 126 likes

ScriptOnRoblox: People are sleeping on this right now, but this is AMAZING. They're using neural networks to figure out mathematical formulas to explain the universe in a way that humans can understand. These are like cheat codes for figuring out science

3 replies, 95 likes

Miles Cranmer: Some slides for my talk later today on the main ideas from our neural network=>symbolic model paper! ( Astro/ML seminar series link:

4 replies, 94 likes

Ricard Solé: Can deep learning networks uncover mathematical expressions describing unknown models of physical processes? I think this study might be the start for a major leap in that direction. A great paper by @MilesCranmer & co. here: @rogertgn

4 replies, 84 likes

Bindu Reddy 🔥❤️: Fascinating work that uses ML to accelerate scientific research GNNs are used to discover equations in Astrophysics Imagine if you could extract symbolic equations from NNs and then use math to operate on them to further discover how the universe works:

1 replies, 67 likes

Miles Cranmer: I'm giving an astrophysics seminar tomorrow on neural nets, symbolic regression, and dark matter. Should be an interesting discussion!

1 replies, 61 likes

Kyle Cranmer: Some fun follow up work on graph networks / symbolic regression hybrid approaches to learn physics. Great work led by @MilesCranmer (no relation). Check out the thread 👇

2 replies, 42 likes

Sourabh Katoch: Discovering Symbolic Models from #DeepLearning with Inductive #Biases Paper- #100DaysOfCode #100DaysOfMLCode #Machinelearning #javascript30 #RStats #hourofcode #womenintech #code #CodeNewbie #DataScience #ArtificialInteligence #AI #Python

0 replies, 16 likes

Socially Distant Brian Bucklew ₑͤ>∿<ₑͤ ∞🌮: caves of qud

1 replies, 14 likes

Jelle Zuidema: Interesting parallels between the uses of deep learning in astrophysics and in linguistics. This work uses Graph NNs, building on a paper (Battaglia et al. 2018) that starts its discussion with Von Humboldt and Chomsky!

0 replies, 14 likes

Dagmar Monett: #AI hybrids have been always the oft unreservedly forgotten, unsung heroes. Here 👇, they excel once more. #GeneticAlgorithms & #NeuralNetworks #symbolic & #connectionist #AI #GOFAI #NN #DL

1 replies, 12 likes

Song Huang: Sounds exciting to me for now, but probably not so much when a neural network replaces me as a researcher in 10 yrs...

1 replies, 10 likes

Jude Gomila: It's exciting how AI may find new physical laws. Symbolic modeling using graph neural networks may allow us to short cut finding useful laws and formula for practical use e.g. this method discovered a new dark matter formula

1 replies, 9 likes

arxiv: Discovering Symbolic Models from Deep Learning with Inductive Biases.

0 replies, 8 likes

MONTREAL.AI: Discovering Symbolic Models from Deep Learning with Inductive Biases Cranmer et al.: Blog and code: #Cosmology #Astrophysics #ComputationalPhysics

0 replies, 8 likes

Dr. Abhay Alok Singh: Congrats Miles for this excellent research.

0 replies, 4 likes

Fabian Offert: This is all over ML Twitter already but retweeting it anyway because it is so cool.

0 replies, 3 likes

higgsinocat: Discovering Symbolic Models from Deep Learning with Inductive Biases. (arXiv:2006.11287v1 [cs.LG]) relevance:100% #darkmatter @StatRui @KyleCranmer @DavidSpergel

0 replies, 2 likes

Shlomo Engelson Argamon: Fascinating.

1 replies, 1 likes

DelocalizedDanny: Interesting approach to #NeuralNetworks providing access to equations. Stems my phycisist 🧡 hopeful for the future of #MachineLearning .🙌

0 replies, 1 likes

Parisa Rashidi: Converting deep learning models to equivalent symbolic equations in physics.

0 replies, 1 likes

Brant Robertson: Excellent talk just now by @MilesCranmer, seems like symbolic regression + deep learning has lots of potential in the physical sciences.

0 replies, 1 likes

Marco Manca: Thread... & this ->

0 replies, 1 likes


Found on Jun 23 2020 at

PDF content of a computer science paper: Discovering Symbolic Models from Deep Learning with Inductive Biases