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Karol Gregor
Finding online neural update rules by learning to remember

Mar 06, 2020
Karol Gregor

* 11 Pages, 1 figure 
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Causally Correct Partial Models for Reinforcement Learning

Feb 07, 2020
Danilo J. Rezende, Ivo Danihelka, George Papamakarios, Nan Rosemary Ke, Ray Jiang, Theophane Weber, Karol Gregor, Hamza Merzic, Fabio Viola, Jane Wang, Jovana Mitrovic, Frederic Besse, Ioannis Antonoglou, Lars Buesing


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Shaping Belief States with Generative Environment Models for RL

Jun 24, 2019
Karol Gregor, Danilo Jimenez Rezende, Frederic Besse, Yan Wu, Hamza Merzic, Aaron van den Oord

* pre-print 
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An investigation of model-free planning

Jan 11, 2019
Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Théophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy Lillicrap


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Learning Attractor Dynamics for Generative Memory

Nov 23, 2018
Yan Wu, Greg Wayne, Karol Gregor, Timothy Lillicrap


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Temporal Difference Variational Auto-Encoder

Jun 11, 2018
Karol Gregor, Frederic Besse

* 13 pages, 7 figures 
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Learning and Querying Fast Generative Models for Reinforcement Learning

Feb 08, 2018
Lars Buesing, Theophane Weber, Sebastien Racaniere, S. M. Ali Eslami, Danilo Rezende, David P. Reichert, Fabio Viola, Frederic Besse, Karol Gregor, Demis Hassabis, Daan Wierstra


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Variational Intrinsic Control

Nov 22, 2016
Karol Gregor, Danilo Jimenez Rezende, Daan Wierstra

* 15 pages, 6 figures 
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What is the Best Feature Learning Procedure in Hierarchical Recognition Architectures?

Jun 05, 2016
Kevin Jarrett, Koray Kvukcuoglu, Karol Gregor, Yann LeCun

* 17 pages, 3 figures 
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Neural Autoregressive Distribution Estimation

May 27, 2016
Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray, Hugo Larochelle


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One-Shot Generalization in Deep Generative Models

May 25, 2016
Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra

* 8pgs, 1pg references, 1pg appendix, In Proceedings of the 33rd International Conference on Machine Learning, JMLR: W&CP volume 48, 2016 
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Towards Conceptual Compression

Apr 29, 2016
Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra

* 14 pages, 13 figures 
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Towards Principled Unsupervised Learning

Dec 03, 2015
Ilya Sutskever, Rafal Jozefowicz, Karol Gregor, Danilo Rezende, Tim Lillicrap, Oriol Vinyals


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MADE: Masked Autoencoder for Distribution Estimation

Jun 05, 2015
Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle

* Proceedings of the 32nd International Conference on Machine Learning, JMLR W&CP 37:881-889, 2015 
 * 9 pages and 1 page of supplementary material. Updated to match published version 
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DRAW: A Recurrent Neural Network For Image Generation

May 20, 2015
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra


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Neural Variational Inference and Learning in Belief Networks

Jun 04, 2014
Andriy Mnih, Karol Gregor

* Proceedings of the 31st International Conference on Machine Learning (ICML), JMLR: W&CP volume 32, 2014 pgs 1791-1799 
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Deep AutoRegressive Networks

May 20, 2014
Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, Daan Wierstra

* Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, Daan Wierstra. Deep AutoRegressive Networks. In Proceedings of the 31st International Conference on Machine Learning (ICML), JMLR: W&CP volume 32, 2014 
 * Appears in Proceedings of the 31st International Conference on Machine Learning (ICML), Beijing, China, 2014 
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Fast approximations to structured sparse coding and applications to object classification

Feb 28, 2012
Arthur Szlam, Karol Gregor, Yann LeCun


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Learning Representations by Maximizing Compression

Aug 04, 2011
Karol Gregor, Yann LeCun

* 8 pages, 3 figures 
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Efficient Learning of Sparse Invariant Representations

May 26, 2011
Karol Gregor, Yann LeCun

* 9 pages + 6 supplement pages 
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Behavior and performance of the deep belief networks on image classification

Dec 03, 2009
Karol Gregor, Gregory Griffin

* 8 pages, 9 figures 
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