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We develop a general approach to distill symbolic representations of a learned deep model by introducing strong inductive biases.
The unreasonable effectiveness of mathematics in the natural sciences
Eugene P. Wigner · 1960
Earlier work this paper cites.
Geometry from a time series
Norman H Packard, James P Crutchfield, J Doyne Farmer, and Robert S Shaw · 1980
Earlier work this paper cites.
The Formation of Dark Halos in a Universe Dominated by Cold Dark Matter
Carlos S. Frenk, Simon D. M. White, Marc Davis, and George Efstathiou · 1988
Earlier work this paper cites.
Modern cosmology
Scott Dodelson · 2003
Earlier work this paper cites.
First-year Wilkinson Microwave Anisotropy Probe (WMAP)* observations: determination of cosmological parameters
David N Spergel, Licia Verde, Hiranya V Peiris, E Komatsu, MR Nolta, CL Bennett, M Halpern, G Hinshaw, N Jarosik, A Kogut, et al · 2003
Earlier work this paper cites.
Matplotlib: A 2D Graphics Environment
J. D. Hunter · 2007
Earlier work this paper cites.
Distilling free-form natural laws from experimental data
Michael Schmidt and Hod Lipson · 2009
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
Earlier work this paper cites.
Data Structures for Statistical Computing in Python
Wes McKinney · 2010
Earlier work this paper cites.
The NumPy Array: A Structure for Efficient Numerical Computation
S. van der Walt, S. C. Colbert, and G. Varoquaux · 2011
Earlier work this paper cites.
Scikit-learn: Machine Learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay · 2011
Earlier work this paper cites.
DEAP: Evolutionary algorithms made easy
Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, and Christian Gagné · 2012
Earlier work this paper cites.
Prioritized Grammar Enumeration: Symbolic Regression by Dynamic Programming
Tony Worm and Kenneth Chiu · 2013
Earlier work this paper cites.
On the number of linear regions of deep neural networks
Guido F Montufar, Razvan Pascanu, Kyunghyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
A method for stochastic optimization
Diederik P Kingma and J Adam Ba · 2014
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
Earlier work this paper cites.
A compositional object-based approach to learning physical dynamics
Michael B Chang, Tomer Ullman, Antonio Torralba, and Joshua B Tenenbaum · 2016
Earlier work this paper cites.
A structural approach to relaxation in glassy liquids
Samuel S Schoenholz, Ekin D Cubuk, Daniel M Sussman, Efthimios Kaxiras, and Andrea J Liu · 2016
Earlier work this paper cites.
Extrapolation and learning equations, 2016
Georg Martius and Christoph H. Lampert · 2016
Earlier work this paper cites.
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2016
Earlier work this paper cites.
Jupyter Notebooks – a publishing format for reproducible computational workflows
Thomas Kluyver, Benjamin Ragan-Kelley, Fernando Pérez, Brian Granger, Matthias Bussonnier, Jonathan Frederic, Kyle Kelley, Jessica Hamrick, Jason Grout, Sylvain Corlay, Paul Ivanov, Damián Avila, Safia Abdalla, and Carol Willing · 2016
Earlier work this paper cites.
Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Earlier work this paper cites.
Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
Cited alongside, same era.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Cited alongside, same era.
Grammar Variational Autoencoder, 2017
Matt J. Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
Cited alongside, same era.
Graph networks as learnable physics engines for inference and control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, and Peter Battaglia · 2018
Cited alongside, same era.
Fast Graph Representation Learning with PyTorch Geometric
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Later among the works it cites.
Hamiltonian graph networks with ode integrators
Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer, and Peter Battaglia · 2019
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Hamiltonian neural networks
Samuel Greydanus, Misko Dzamba, and Jason Yosinski · 2019
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Hamiltonian generative networks
Peter Toth, Danilo Jimenez Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, and Irina Higgins · 2019
Later among the works it cites.
Machine learning of coarse-grained molecular dynamics force fields
Jiang Wang, Simon Olsson, Christoph Wehmeyer, Adrià Pérez, Nicholas E Charron, Gianni De Fabritiis, Frank Noé, and Cecilia Clementi · 2019
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Yunzhu Li, Jiajun Wu, Russ Tedrake, Joshua B Tenenbaum, and Antonio Torralba · 2018
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Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard Zemel · 2018
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Learning Equations for Extrapolation and Control
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Planck 2018 results. VI. Cosmological parameters
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