Fetching the paper…
Reading the bibliography…
We present a method for unsupervised learning of equations of motion for objects in raw and optionally distorted unlabeled video.
Invariance in physical theory
Eugene P Wigner · 1949
Earlier work this paper cites.
Unknotting spheres
EC Zeeman · 1960
Earlier work this paper cites.
Equation of motion from a data series
James P Crutchfield and Bruce S McNamara · 1987
Earlier work this paper cites.
Auto-association by multilayer perceptrons and singular value decomposition
Hervé Bourlard and Yves Kamp · 1988
Earlier work this paper cites.
Backpropagation applied to handwritten zip code recognition
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel · 1989
Earlier work this paper cites.
Autoencoders, minimum description length and helmholtz free energy
Geoffrey E Hinton and Richard S Zemel · 1994
Earlier work this paper cites.
Discovering dynamics: from inductive logic programming to machine discovery
Saso Dzeroski and Ljupco Todorovski · 1995
Earlier work this paper cites.
Reasoning about nonlinear system identification
Elizabeth Bradley, Matthew Easley, and Reinhard Stolle · 2001
Earlier work this paper cites.
Computing the physical parameters of rigid-body motion from video
Kiran S Bhat, Steven M Seitz, Jovan Popović, and Pradeep K Khosla · 2002
Earlier work this paper cites.
Robust induction of process models from time-series data
Pat Langley, Dileep George, Stephen D Bay, and Kazumi Saito · 2003
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2006
Earlier work this paper cites.
Distilling free-form natural laws from experimental data
Michael Schmidt and Hod Lipson · 2009
Earlier work this paper cites.
Symbolic regression using nearest neighbor indexing
Randall K McRee · 2010
Earlier work this paper cites.
Gptips: an open source genetic programming toolbox for multigene symbolic regression
Dominic P Searson, David E Leahy, and Mark J Willis · 2010
Earlier work this paper cites.
Eureqa: software review
Renáta Dubčáková · 2011
Earlier work this paper cites.
Separating the wheat from the chaff: on feature selection and feature importance in regression random forests and symbolic regression
Sean Stijven, Wouter Minnebo, and Katya Vladislavleva · 2011
Earlier work this paper cites.
Automated refinement and inference of analytical models for metabolic networks
Michael D Schmidt, Ravishankar R Vallabhajosyula, Jerry W Jenkins, Jonathan E Hood, Abhishek S Soni, John P Wikswo, and Hod Lipson · 2011
Earlier work this paper cites.
Comment on the article” distilling free-form natural laws from experimental data”
Christopher Hillar and Friedrich Sommer · 2012
Earlier work this paper cites.
Unsupervised feature learning and deep learning: A review and new perspectives
Yoshua Bengio, Aaron C Courville, and Pascal Vincent · 2012
Earlier work this paper cites.
The Astronomical Revolution: Copernicus-Kepler-Borelli
Alexandre Koyré · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Deep learning of representations: Looking forward
Yoshua Bengio · 2013
Earlier work this paper cites.
Video (language) modeling: a baseline for generative models of natural videos
MarcAurelio Ranzato, Arthur Szlam, Joan Bruna, Michael Mathieu, Ronan Collobert, and Sumit Chopra · 2014
Earlier work this paper cites.
Modeling deep temporal dependencies with recurrent grammar cells””
Vincent Michalski, Roland Memisevic, and Kishore Konda · 2014
Earlier work this paper cites.
Automated adaptive inference of phenomenological dynamical models
Bryan C Daniels and Ilya Nemenman · 2015
Earlier work this paper cites.
Heuristic induction of rate-based process models
Pat Langley and Adam Arvay · 2015
Earlier work this paper cites.
Building predictive models via feature synthesis
Ignacio Arnaldo, Una-May O’Reilly, and Kalyan Veeramachaneni · 2015
Cited alongside, same era.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
Cited alongside, same era.
Unsupervised learning of video representations using lstms
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov · 2015
Cited alongside, same era.
Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L Lewis, and Satinder Singh · 2015
Cited alongside, same era.
Research priorities for robust and beneficial artificial intelligence
Stuart Russell, Daniel Dewey, and Max Tegmark · 2015
Cited alongside, same era.
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2016
A simple neural network module for relational reasoning
Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Timothy Lillicrap · 2017
Later among the works it cites.
Visual interaction networks: Learning a physics simulator from video
Nicholas Watters, Daniel Zoran, Theophane Weber, Peter Battaglia, Razvan Pascanu, and Andrea Tacchetti · 2017
Later among the works it cites.
beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Later among the works it cites.
Sparse identification of nonlinear dynamics for rapid model recovery
Markus Quade, Markus Abel, J Nathan Kutz, and Steven L Brunton · 2018
Later among the works it cites.
Mutual information, neural networks and the renormalization group
Maciej Koch-Janusz and Zohar Ringel · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
Cited alongside, same era.
Deep predictive coding networks for video prediction and unsupervised learning
William Lotter, Gabriel Kreiman, and David Cox · 2016
Cited alongside, same era.
Francesco Cricri, Xingyang Ni, Mikko Honkala, Emre Aksu, and Moncef Gabbouj · 2016
Cited alongside, same era.
Learning thermodynamics with boltzmann machines
Giacomo Torlai and Roger G Melko · 2016
Cited alongside, same era.
A compositional object-based approach to learning physical dynamics
Michael B Chang, Tomer Ullman, Antonio Torralba, and Joshua B Tenenbaum · 2016
Cited alongside, same era.
Concrete problems in ai safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
Cited alongside, same era.
A new dog learns old tricks: Rl finds classic optimization algorithms
Weiwei Kong, Christopher Liaw, Aranyak Mehta, and D Sivakumar · 2018
Later among the works it cites.
Discovering physical concepts with neural networks
Raban Iten, Tony Metger, Henrik Wilming, Lídia Del Rio, and Renato Renner · 2018
Later among the works it cites.
Folded recurrent neural networks for future video prediction
Marc Oliu, Javier Selva, and Sergio Escalera · 2018
Later among the works it cites.
Learning phase transitions from dynamics
Evert van Nieuwenburg, Eyal Bairey, and Gil Refael · 2018
Later among the works it cites.
Machine learning & artificial intelligence in the quantum domain: a review of recent progress
Vedran Dunjko and Hans J Briegel · 2018
Later among the works it cites.
Neurocomputational modeling of human physical scene understanding
Ilker Yildirim, Kevin A Smith, Mario Belledonne, Jiajun Wu, and Joshua B Tenenbaum · 2018
Later among the works it cites.
Unsupervised learning of latent physical properties using perception-prediction networks
David Zheng, Vinson Luo, Jiajun Wu, and Joshua B Tenenbaum · 2018
Later among the works it cites.
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
Later among the works it cites.
An intriguing failing of convolutional neural networks and the coordconv solution
Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, and Jason Yosinski · 2018
Later among the works it cites.
Relational inductive bias for physical construction in humans and machines
Jessica B Hamrick, Kelsey R Allen, Victor Bapst, Tina Zhu, Kevin R McKee, Joshua B Tenenbaum, and Peter W Battaglia · 2018
Later among the works it cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Later among the works it cites.
Information dropout: Learning optimal representations through noisy computation
Alessandro Achille and Stefano Soatto · 2018
Later among the works it cites.
Phillips-inspired machine learning for band gap and exciton binding energy prediction
Jiechun Liang and Xi Zhu · 2019
Later among the works it cites.
Toward an artificial intelligence physicist for unsupervised learning
Tailin Wu and Max Tegmark · 2019
Later among the works it cites.
Deep memory and prediction neural network for video prediction
Zhipeng Liu, Xiujuan Chai, and Xilin Chen · 2019
Later among the works it cites.
A general deep learning framework for network reconstruction and dynamics learning
Zhang Zhang, Yi Zhao, Jing Liu, Shuo Wang, Ruyi Tao, Ruyue Xin, and Jiang Zhang · 2019
Later among the works it cites.
Extracting interpretable physical parameters from spatiotemporal systems using unsupervised learning
Peter Y Lu, Samuel Kim, and Marin Soljačić · 2019
Later among the works it cites.
Hamiltonian neural networks
Samuel Greydanus, Misko Dzamba, and Jason Yosinski · 2019
Later among the works it cites.
AI Feynman: A physics-inspired method for symbolic regression
Silviu-Marian Udrescu and Max Tegmark · 2020
Closest in time.
Ai feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
Silviu-Marian Udrescu, Andrew Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, and Max Tegmark · 2020
Closest in time.