Fetching the paper…
Reading the bibliography…
Common-sense physical reasoning in the real world requires learning about the interactions of objects and their dynamics.
Monet: Unsupervised scene decomposition and representation
Burgess, C. P.; Matthey, L.; Watters, N.; Kabra, R.; Higgins, I.; Botvinick, M.; and Lerchner, A. 2019 · 1901
Earlier work this paper cites.
Spatial broadcast decoder: A simple architecture for learning disentangled representations in vaes
Watters, N.; Matthey, L.; Burgess, C. P.; and Lerchner, A. 2019b · 1901
Earlier work this paper cites.
Recurrent independent mechanisms
Goyal, A.; Lamb, A.; Hoffmann, J.; Sodhani, S.; Levine, S.; Bengio, Y.; and Schölkopf, B. 2019 · 1909
Earlier work this paper cites.
Generative Hierarchical Models for Parts, Objects, and Scenes
Deng, F.; Zhi, Z.; and Ahn, S. 2019 · 1910
Earlier work this paper cites.
Long short-term memory
Hochreiter, S.; and Schmidhuber, J. 1997 · 1997
Earlier work this paper cites.
Learning to simulate complex physics with graph networks
Sanchez-Gonzalez, A.; Godwin, J.; Pfaff, T.; Ying, R.; Leskovec, J.; and Battaglia, P. W. 2020 · 2002
Earlier work this paper cites.
Recognizing human actions: a local SVM approach
Schuldt, C.; Laptev, I.; and Caputo, B. 2004 · 2004
Earlier work this paper cites.
Core knowledge
Spelke, E. S.; and Kinzler, K. D. 2007 · 2007
Earlier work this paper cites.
The Graph Neural Network Model
Scarselli, F.; Gori, M.; Tsoi, A. C.; Hagenbuchner, M.; and Monfardini, G. 2009 · 2008
Earlier work this paper cites.
Multi-column deep neural networks for image classification
Ciregan, D.; Meier, U.; and Schmidhuber, J. 2012 · 2012
Earlier work this paper cites.
On the Binding Problem in Artificial Neural Networks
Greff, K.; van Steenkiste, S.; and Schmidhuber, J. 2020 · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
Earlier work this paper cites.
Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments
Ionescu, C.; Papava, D.; Olaru, V.; and Sminchisescu, C. 2013 · 2013
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Kingma, D. P.; and Welling, M. 2013 · 2013
Earlier work this paper cites.
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K.; van Merriënboer, B.; Gulcehre, C.; Bahdanau, D.; Bougares, F.; Schwenk, H.; and Bengio, Y. 2014 · 2014
Earlier work this paper cites.
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Rezende, D. J.; Mohamed, S.; and Wierstra, D. 2014 · 2014
Earlier work this paper cites.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S.; and Szegedy, C. 2015 · 2015
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2015 · 2015
Earlier work this paper cites.
Human-level concept learning through probabilistic program induction
Lake, B. M.; Salakhutdinov, R.; and Tenenbaum, J. B. 2015 · 2015
Earlier work this paper cites.
Unsupervised learning of video representations using lstms
Srivastava, N.; Mansimov, E.; and Salakhudinov, R. 2015 · 2015
Earlier work this paper cites.
End-to-end memory networks
Sukhbaatar, S.; Weston, J.; Fergus, R.; et al. 2015 · 2015
Cited alongside, same era.
Interaction networks for learning about objects, relations and physics
Battaglia, P.; Pascanu, R.; Lai, M.; Rezende, D. J.; et al. 2016 · 2016
Cited alongside, same era.
Attend, infer, repeat: Fast scene understanding with generative models
Eslami, S. A.; Heess, N.; Weber, T.; Tassa, Y.; Szepesvari, D.; Hinton, G. E.; et al. 2016 · 2016
Cited alongside, same era.
Unsupervised learning for physical interaction through video prediction
Finn, C.; Goodfellow, I.; and Levine, S. 2016 · 2016
Cited alongside, same era.
Learning Physical Intuition of Block Towers by Example
Lerer, A.; Gross, S.; and Fergus, R. 2016 · 2016
Cited alongside, same era.
To fall or not to fall: A visual approach to physical stability prediction
Neural Relational Inference for Interacting Systems
Kipf, T. N.; Fetaya, E.; Wang, K.; Welling, M.; and Zemel, R. S. 2018 · 2018
Later among the works it cites.
Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
Kosiorek, A. R.; Kim, H.; Posner, I.; and Teh, Y. W. 2018 · 2018
Later among the works it cites.
An intriguing failing of convolutional neural networks and the coordconv solution
Liu, R.; Lehman, J.; Molino, P.; Such, F. P.; Frank, E.; Sergeev, A.; and Yosinski, J. 2018 · 2018
Later among the works it cites.
Flexible neural representation for physics prediction
Mrowca, D.; Zhuang, C.; Wang, E.; Haber, N.; Fei-Fei, L. F.; Tenenbaum, J.; and Yamins, D. L. 2018 · 2018
Later among the works it cites.
Graph Networks as Learnable Physics Engines for Inference and Control
Sanchez-Gonzalez, A.; Heess, N.; Springenberg, J. T.; Merel, J.; Riedmiller, M. A.; Hadsell, R.; and Battaglia, P. 2018 · 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…
Li, W.; Azimi, S.; Leonardis, A.; and Fritz, M. 2016 · 2016
Cited alongside, same era.
Generating videos with scene dynamics
Vondrick, C.; Pirsiavash, H.; and Torralba, A. 2016 · 2016
Cited alongside, same era.
A Compositional Object-Based Approach to Learning Physical Dynamics
Chang, M.; Ullman, T.; Torralba, A.; and Tenenbaum, J. 2017 · 2017
Cited alongside, same era.
Neural message passing for quantum chemistry
Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; and Dahl, G. E. 2017 · 2017
Cited alongside, same era.
Neural expectation maximization
Greff, K.; van Steenkiste, S.; and Schmidhuber, J. 2017 · 2017
Cited alongside, same era.
Vain: Attentional multi-agent predictive modeling
Hoshen, Y. 2017 · 2017
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Jang, E.; Gu, S.; and Poole, B. 2017 · 2017
Cited alongside, same era.
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions
van Steenkiste, S.; Chang, M.; Greff, K.; and Schmidhuber, J. 2018 · 2018
Later among the works it cites.
Spatially invariant unsupervised object detection with convolutional neural networks
Crawford, E.; and Pineau, J. 2019 · 2019
Later among the works it cites.
Reconciling deep learning with symbolic artificial intelligence: representing objects and relations
Garnelo, M.; and Shanahan, M. 2019 · 2019
Later among the works it cites.
Multi-Object Representation Learning with Iterative Variational Inference
Greff, K.; Kaufman, R. L.; Kabra, R.; Watters, N.; Burgess, C.; Zoran, D.; Matthey, L.; Botvinick, M.; and Lerchner, A. 2019 · 2019
Later among the works it cites.
Stochastic Adversarial Video Prediction
Lee, A. X.; Zhang, R.; Ebert, F.; Abbeel, P.; Finn, C.; and Levine, S. 2019 · 2019
Later among the works it cites.
Modeling Parts, Structure, and System Dynamics via Predictive Learning
Liu, Z.; Wu, J.; Xu, Z.; Sun, C.; Murphy, K.; Freeman, W. T.; and Tenenbaum, J. B. 2019 · 2019
Later among the works it cites.
R-SQAIR: Relational Sequential Attend, Infer, Repeat
Stanić, A.; and Schmidhuber, J. 2019 · 2019
Later among the works it cites.
A Perspective on Objects and Systematic Generalization in Model-Based RL
van Steenkiste, S.; Greff, K.; and Schmidhuber, J. 2019 · 2019
Later among the works it cites.
Entity Abstraction in Visual Model-Based Reinforcement Learning
Veerapaneni, R.; Co-Reyes, J. D.; Chang, M.; Janner, M.; Finn, C.; Wu, J.; Tenenbaum, J. B.; and Levine, S. 2019 · 2019
Later among the works it cites.
Deep reinforcement learning with relational inductive biases
Zambaldi, V.; Raposo, D.; Santoro, A.; Bapst, V.; Li, Y.; Babuschkin, I.; Tuyls, K.; Reichert, D.; Lillicrap, T.; Lockhart, E.; Shanahan, M.; Langston, V.; Pascanu, R.; Botvinick, M.; Vinyals, O.; and Battaglia, P. 2019 · 2019
Later among the works it cites.
SCALOR: Generative World Models with Scalable Object Representations
Jiang, J.; Janghorbani, S.; Melo, G. D.; and Ahn, S. 2020 · 2020
Closest in time.
Videoflow: A flow-based generative model for video
Kumar, M.; Babaeizadeh, M.; Erhan, D.; Finn, C.; Levine, S.; Dinh, L.; and Kingma, D. 2020 · 2020
Closest in time.
Towards Curiosity-Driven Learning of Physical Dynamics
Lingelbach, M. J.; Mrowca, D.; Haber, N.; Fei-Fei, L.; and Yamins, D. L. K. 2020 · 2020
Closest in time.
Learning to combine top-down and bottom-up signals in recurrent neural networks with attention over modules
Mittal, S.; Lamb, A.; Goyal, A.; Voleti, V.; Shanahan, M.; Lajoie, G.; Mozer, M.; and Bengio, Y. 2020 · 2020
Closest in time.