Fetching the paper…
Reading the bibliography…
State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of tasks.
Integrative activity of the brain; an interdisciplinary approach
Jerzy Konorski · 1967
Earlier work this paper cites.
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
David Marr · 1982
Earlier work this paper cites.
Asymptotic evaluation of certain markov process expectations for large time. iv
Monroe D Donsker and SR Srinivasa Varadhan · 1983
Earlier work this paper cites.
What’s in an object file? evidence from priming studies
Robert D Gordon and David E Irwin · 1996
Earlier work this paper cites.
Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
Rajesh PN Rao and Dana H Ballard · 1999
Earlier work this paper cites.
Predictive information
William Bialek and Naftali Tishby · 1999
Earlier work this paper cites.
Nonlinear independent component analysis: Existence and uniqueness results
Aapo Hyvärinen and Petteri Pajunen · 1999
Earlier work this paper cites.
The im algorithm: A variational approach to information maximization
David Barber and Felix Agakov · 2003
Earlier work this paper cites.
Independent component analysis , volume 46
Aapo Hyvärinen, Juha Karhunen, and Erkki Oja · 2004
Earlier work this paper cites.
A theory of cortical responses
Karl Friston · 2005
Earlier work this paper cites.
Playing the past
Zach Whalen and Laurie N Taylor · 2008
Earlier work this paper cites.
Learning deep architectures for AI
Yoshua Bengio · 2009
Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
Earlier work this paper cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Embed to control: A locally linear latent dynamics model for control from raw images
Manuel Watter, Jost Springenberg, Joschka Boedecker, and Martin Riedmiller · 2015
Earlier work this paper cites.
Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L Lewis, and Satinder Singh · 2015
Earlier work this paper cites.
Predictive information in a sensory population
Stephanie E Palmer, Olivier Marre, Michael J Berry, and William Bialek · 2015
Earlier work this paper cites.
Learning state representations with robotic priors
Rico Jonschkowski and Oliver Brock · 2015
Earlier work this paper cites.
Deep speech 2: End-to-end speech recognition in english and mandarin
Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, et al · 2016
Earlier work this paper cites.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al · 2016
Earlier work this paper cites.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Earlier work this paper cites.
Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krähenbühl, Jeff Donahue, Trevor Darrell, and Alexei A. Efros · 2016
Earlier work this paper cites.
Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
Cited alongside, same era.
Improved deep metric learning with multi-class n-pair loss objective
Kihyuk Sohn · 2016
Cited alongside, same era.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Cited alongside, same era.
Stable reinforcement learning with autoencoders for tactile and visual data
Herke van Hoof, Nutan Chen, Maximilian Karl, Patrick van der Smagt, and Jan Peters · 2016
Cited alongside, same era.
Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman · 2017
Cited alongside, same era.
Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville · 2017
Towards a definition of disentangled representations
Irina Higgins, David Amos, David Pfau, Sebastien Racaniere, Loic Matthey, Danilo Rezende, and Alexander Lerchner · 2018
Later among the works it cites.
Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
Later among the works it cites.
Playing hard exploration games by watching youtube
Yusuf Aytar, Tobias Pfaff, David Budden, Thomas Paine, Ziyu Wang, and Nando de Freitas · 2018
Later among the works it cites.
Contingency-aware exploration in reinforcement learning
Jongwook Choi, Yijie Guo, Marcin Moczulski, Junhyuk Oh, Neal Wu, Mohammad Norouzi, and Honglak Lee · 2018
Later among the works it cites.
Unsupervised control through non-parametric discriminative rewards
David Warde-Farley, Tom Van de Wiele, Tejas Kulkarni, Catalin Ionescu, Steven Hansen, and Volodymyr Mnih · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
Cited alongside, same era.
Multi-task self-supervised visual learning
Carl Doersch and Andrew Zisserman · 2017
Cited alongside, same era.
Darla: Improving zero-shot transfer in reinforcement learning
Irina Higgins, Arka Pal, Andrei Rusu, Loic Matthey, Christopher Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, and Alexander Lerchner · 2017
Cited alongside, same era.
Learning state representations for robotic control: Information disentangling and multi-modal learning
Wuyang Duan · 2017
Cited alongside, same era.
Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2017
Cited alongside, same era.
Pves: Position-velocity encoders for unsupervised learning of structured state representations
Rico Jonschkowski, Roland Hafner, Jonathan Scholz, and Martin Riedmiller · 2017
Cited alongside, same era.
Later among the works it cites.
Object-oriented dynamics predictor
Guangxiang Zhu, Zhiao Huang, and Chongjie Zhang · 2018
Later among the works it cites.
Zero-shot learning - a comprehensive evaluation of the good, the bad and the ugly
Yongqin Xian, Christoph H. Lampert, Bernt Schiele, and Zeynep Akata · 2018
Later among the works it cites.
Senteval: An evaluation toolkit for universal sentence representations
Alexis Conneau and Douwe Kiela · 2018
Later among the works it cites.
An atari model zoo for analyzing, visualizing, and comparing deep reinforcement learning agents
Felipe Petroski Such, Vashisht Madhavan, Rosanne Liu, Rui Wang, Pablo Samuel Castro, Yulun Li, Ludwig Schubert, Marc Bellemare, Jeff Clune, and Joel Lehman · 2018
Later among the works it cites.
Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
Later among the works it cites.
On the learning dynamics of deep neural networks
Remi Tachet des Combes, Mohammad Pezeshki, Samira Shabanian, Aaron Courville, and Yoshua Bengio · 2018
Later among the works it cites.
Pytorch implementations of reinforcement learning algorithms
Ilya Kostrikov · 2018
Later among the works it cites.
Natural environment benchmarks for reinforcement learning
Amy Zhang, Yuxin Wu, and Joelle Pineau · 2018
Later among the works it cites.
Revisiting self-supervised visual representation learning
Alexander Kolesnikov, Xiaohua Zhai, and Lucas Beyer · 2019
Closest in time.
Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2019
Closest in time.
Wasserstein dependency measure for representation learning
Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aaron Van den Oord, Sergey Levine, and Pierre Sermanet · 2019
Closest in time.
Learning representations by maximizing mutual information across views
Philip Bachman, R Devon Hjelm, and William Buchwalter · 2019
Closest in time.
On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aäron Van den Oord, Alexander A Alemi, and George Tucker · 2019
Closest in time.
Challenging common assumptions in the unsupervised learning of disentangled representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2019
Closest in time.
BJARS.com Atari Archives
Steve Engelhardt · 2019
Closest in time.
Atariage atari 2600 forums, 2019
Thomas Jentzsch and CPUWIZ · 2019
Closest in time.
Playing atari with six neurons
Giuseppe Cuccu, Julian Togelius, and Philippe Cudré-Mauroux · 2019
Closest in time.
Emi: Exploration with mutual information
Hyoungseok Kim, Jaekyeom Kim, Yeonwoo Jeong, Sergey Levine, and Hyun Oh Song · 2019
Closest in time.
Monet: Unsupervised scene decomposition and representation
Christopher P Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, and Alexander Lerchner · 2019
Closest in time.
Multi-object representation learning with iterative variational inference
Klaus Greff, Raphaël Lopez Kaufmann, Rishab Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, and Alexander Lerchner · 2019
Closest in time.
GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman · 2019
Closest in time.
Large-scale study of curiosity-driven learning
Yuri Burda, Harri Edwards, Deepak Pathak, Amos Storkey, Trevor Darrell, and Alexei A Efros · 2019
Closest in time.