Fetching the paper…
Reading the bibliography…
Unsupervised representation learning has succeeded with excellent results in many applications.
A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition
Lawrence R Rabiner · 1989
Earlier work this paper cites.
A Survey of Algorithmic Methods for Partially Observed Markov Decision Processes
William S. Lovejoy · 1991
Earlier work this paper cites.
Curious Model-Building Control Systems
Jürgen Schmidhuber · 1991
Earlier work this paper cites.
Inference of Finite Automata Using Homing Sequences
Ronald L. Rivest and Robert E. Schapire · 1993
Earlier work this paper cites.
Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems
Tommi Jaakkola, Satinder P Singh, and Michael I Jordan · 1995
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Exact and Approximate Algorithms for Partially Observable Markov Decision Processes
Anthony Rocco Cassandra · 1998
Earlier work this paper cites.
Gradient-Based Learning Applied to Document Recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto · 1998
Earlier work this paper cites.
Decision-Theoretic Planning: Structural Assumptions and Computational Leverage
Craig Boutilier, Thomas Dean, and Steve Hanks · 1999
Earlier work this paper cites.
Predictive Representations of State
Michael L. Littman, Richard S. Sutton, and Satinder Singh · 2002
Earlier work this paper cites.
Generalizing Plans to New Environments in Relational MDPs
Carlos Guestrin, Daphne Koller, Chris Gearhart, and Neal Kanodia · 2003
Earlier work this paper cites.
Predictive state representations: A new theory for modeling dynamical systems
Satinder Singh, Michael R James, and Matthew R Rudary · 2004
Earlier work this paper cites.
Greedy Layer-wise Training of Deep Networks
Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle · 2007
Earlier work this paper cites.
Multi-Task Reinforcement Learning: A Hierarchical Bayesian Approach
Aaron Wilson, Alan Fern, Soumya Ray, and Prasad Tadepalli · 2007
Earlier work this paper cites.
An Object-Oriented Representation for Efficient Reinforcement Learning
Carlos Diuk, Andre Cohen, and Michael L. Littman · 2008
Earlier work this paper cites.
Near-Bayesian Exploration in Polynomial Time
J Zico Kolter and Andrew Y Ng · 2009
Earlier work this paper cites.
Why Does Unsupervised Pre-Training Help Deep Learning?
Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio · 2010
Cited alongside, same era.
Noise-Contrastive Estimation: A New Estimation Principle for Unnormalized Statistical Models
Michael Gutmann and Aapo Hyvärinen · 2010
Cited alongside, same era.
Variance-Based Rewards for Approximate Bayesian Reinforcement Learning
Jonathan Sorg, Satinder Singh, and Richard L. Lewis · 2010
Cited alongside, same era.
Closing the Learning-Planning Loop with Predictive State Representations
Byron Boots, Sajid M Siddiqi, and Geoffrey J Gordon · 2011
Cited alongside, same era.
Horde: A Scalable Real-Time Architecture for Learning Knowledge from Unsupervised Sensorimotor Interaction
Richard S Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M Pilarski, Adam White, and Doina Precup · 2011
Cited alongside, same era.
Convolutional LSTM network: A machine learning approach for precipitation nowcasting
SHI Xingjian, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo · 2015
Later among the works it 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
Later among the works it cites.
Charles Beattie, Joel Z Leibo, Denis Teplyashin, Tom Ward, Marcus Wainwright, Heinrich Küttler, Andrew Lefrancq, Simon Green, Víctor Valdés, Amir Sadik, et al · 2016
Later among the works it cites.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Later among the works it cites.
Reinforcement Learning with Unsupervised Auxiliary Tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, and Koray Kavukcuoglu · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
John Asmuth and Michael L Littman · 2012
Cited alongside, same era.
Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics
Michael U Gutmann and Aapo Hyvärinen · 2012
Cited alongside, same era.
Representation Learning: A Review and New Perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
Cited alongside, same era.
Tensor Decompositions for Learning Latent Variable Models
Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M Kakade, and Matus Telgarsky · 2014
Cited alongside, same era.
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
Cited alongside, same era.
Generative Adversarial Nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Cited alongside, same era.
Later among the works it cites.
End-to-End Training of Deep Visuomotor Policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
Later among the works it 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
Later among the works it cites.
Learning to Act by Predicting the Future
Alexey Dosovitskiy and Vladlen Koltun · 2017
Later among the works it cites.
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
Later among the works it cites.
Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gómez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, and Misha Denil · 2018
Closest in time.
Understanding Disentangling in β \beta -VAE
Christopher P. Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
Closest in time.
Neural Scene Representation and Rendering
SM Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S Morcos, Marta Garnelo, Avraham Ruderman, Andrei A Rusu, Ivo Danihelka, Karol Gregor, et al · 2018
Closest in time.
Recurrent predictive state policy networks
Ahmed Hefny, Zita Marinho, Wen Sun, Siddhartha Srinivasa, and Geoffrey Gordon · 2018
Closest in time.
Deep Variational Reinforcement Learning for POMDPs
Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood, and Shimon Whiteson · 2018
Closest in time.
Integrating Algorithmic Planning and Deep Learning for Partially Observable Navigation
Peter Karkus, David Hsu, and Wee Sun Lee · 2018
Closest in time.
Representation Learning with Contrastive Predictive Coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Closest in time.
Unsupervised Predictive Memory in a Goal-Directed Agent
Greg Wayne, Chia-Chun Hung, David Amos, Mehdi Mirza, Arun Ahuja, Agnieszka Grabska-Barwinska, Jack Rae, Piotr Mirowski, Joel Z Leibo, Adam Santoro, et al · 2018
Closest in time.