Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Karol Gregor

Karol Gregor

Self-Organizing Intelligent Matter: A blueprint for an AI generating algorithm


Jan 19, 2021
Karol Gregor, Frederic Besse

Figure 1 for Self-Organizing Intelligent Matter: A blueprint for an AI generating algorithm
Figure 2 for Self-Organizing Intelligent Matter: A blueprint for an AI generating algorithm
* 13 pages, 2 figures 
  Access Paper or Ask Questions

Finding online neural update rules by learning to remember


Mar 06, 2020
Karol Gregor

Figure 1 for Finding online neural update rules by learning to remember
* 11 Pages, 1 figure 
  Access Paper or Ask Questions

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

Figure 1 for Causally Correct Partial Models for Reinforcement Learning
Figure 2 for Causally Correct Partial Models for Reinforcement Learning
Figure 3 for Causally Correct Partial Models for Reinforcement Learning
Figure 4 for Causally Correct Partial Models for Reinforcement Learning

  Access Paper or Ask Questions

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

Figure 1 for Shaping Belief States with Generative Environment Models for RL
Figure 2 for Shaping Belief States with Generative Environment Models for RL
Figure 3 for Shaping Belief States with Generative Environment Models for RL
Figure 4 for Shaping Belief States with Generative Environment Models for RL
* pre-print 
  Access Paper or Ask Questions

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

Figure 1 for An investigation of model-free planning
Figure 2 for An investigation of model-free planning
Figure 3 for An investigation of model-free planning
Figure 4 for An investigation of model-free planning

  Access Paper or Ask Questions

Learning Attractor Dynamics for Generative Memory


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

Figure 1 for Learning Attractor Dynamics for Generative Memory
Figure 2 for Learning Attractor Dynamics for Generative Memory
Figure 3 for Learning Attractor Dynamics for Generative Memory
Figure 4 for Learning Attractor Dynamics for Generative Memory

  Access Paper or Ask Questions

Temporal Difference Variational Auto-Encoder


Jun 11, 2018
Karol Gregor, Frederic Besse

Figure 1 for Temporal Difference Variational Auto-Encoder
Figure 2 for Temporal Difference Variational Auto-Encoder
Figure 3 for Temporal Difference Variational Auto-Encoder
Figure 4 for Temporal Difference Variational Auto-Encoder
* 13 pages, 7 figures 
  Access Paper or Ask Questions

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

Figure 1 for Learning and Querying Fast Generative Models for Reinforcement Learning
Figure 2 for Learning and Querying Fast Generative Models for Reinforcement Learning
Figure 3 for Learning and Querying Fast Generative Models for Reinforcement Learning
Figure 4 for Learning and Querying Fast Generative Models for Reinforcement Learning

  Access Paper or Ask Questions

Variational Intrinsic Control


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

Figure 1 for Variational Intrinsic Control
Figure 2 for Variational Intrinsic Control
Figure 3 for Variational Intrinsic Control
Figure 4 for Variational Intrinsic Control
* 15 pages, 6 figures 
  Access Paper or Ask Questions

What is the Best Feature Learning Procedure in Hierarchical Recognition Architectures?


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

Figure 1 for What is the Best Feature Learning Procedure in Hierarchical Recognition Architectures?
Figure 2 for What is the Best Feature Learning Procedure in Hierarchical Recognition Architectures?
Figure 3 for What is the Best Feature Learning Procedure in Hierarchical Recognition Architectures?
Figure 4 for What is the Best Feature Learning Procedure in Hierarchical Recognition Architectures?
* 17 pages, 3 figures 
  Access Paper or Ask Questions

Neural Autoregressive Distribution Estimation


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

Figure 1 for Neural Autoregressive Distribution Estimation
Figure 2 for Neural Autoregressive Distribution Estimation
Figure 3 for Neural Autoregressive Distribution Estimation
Figure 4 for Neural Autoregressive Distribution Estimation

  Access Paper or Ask Questions

One-Shot Generalization in Deep Generative Models


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

Figure 1 for One-Shot Generalization in Deep Generative Models
Figure 2 for One-Shot Generalization in Deep Generative Models
Figure 3 for One-Shot Generalization in Deep Generative Models
Figure 4 for One-Shot Generalization in Deep Generative Models
* 8pgs, 1pg references, 1pg appendix, In Proceedings of the 33rd International Conference on Machine Learning, JMLR: W&CP volume 48, 2016 
  Access Paper or Ask Questions

Towards Conceptual Compression


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

Figure 1 for Towards Conceptual Compression
Figure 2 for Towards Conceptual Compression
Figure 3 for Towards Conceptual Compression
Figure 4 for Towards Conceptual Compression
* 14 pages, 13 figures 
  Access Paper or Ask Questions

Towards Principled Unsupervised Learning


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

Figure 1 for Towards Principled Unsupervised Learning
Figure 2 for Towards Principled Unsupervised Learning
Figure 3 for Towards Principled Unsupervised Learning
Figure 4 for Towards Principled Unsupervised Learning

  Access Paper or Ask Questions

MADE: Masked Autoencoder for Distribution Estimation


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

Figure 1 for MADE: Masked Autoencoder for Distribution Estimation
Figure 2 for MADE: Masked Autoencoder for Distribution Estimation
Figure 3 for MADE: Masked Autoencoder for Distribution Estimation
Figure 4 for MADE: Masked Autoencoder for Distribution Estimation
* 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 
  Access Paper or Ask Questions

DRAW: A Recurrent Neural Network For Image Generation


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

Figure 1 for DRAW: A Recurrent Neural Network For Image Generation
Figure 2 for DRAW: A Recurrent Neural Network For Image Generation
Figure 3 for DRAW: A Recurrent Neural Network For Image Generation
Figure 4 for DRAW: A Recurrent Neural Network For Image Generation

  Access Paper or Ask Questions

Neural Variational Inference and Learning in Belief Networks


Jun 04, 2014
Andriy Mnih, Karol Gregor

Figure 1 for Neural Variational Inference and Learning in Belief Networks
* Proceedings of the 31st International Conference on Machine Learning (ICML), JMLR: W&CP volume 32, 2014 pgs 1791-1799 
  Access Paper or Ask Questions

Deep AutoRegressive Networks


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

Figure 1 for Deep AutoRegressive Networks
Figure 2 for Deep AutoRegressive Networks
Figure 3 for Deep AutoRegressive Networks
Figure 4 for Deep AutoRegressive Networks
* 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 
  Access Paper or Ask Questions

Fast approximations to structured sparse coding and applications to object classification


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

Figure 1 for Fast approximations to structured sparse coding and applications to object classification
Figure 2 for Fast approximations to structured sparse coding and applications to object classification
Figure 3 for Fast approximations to structured sparse coding and applications to object classification
Figure 4 for Fast approximations to structured sparse coding and applications to object classification

  Access Paper or Ask Questions

Learning Representations by Maximizing Compression


Aug 04, 2011
Karol Gregor, Yann LeCun

Figure 1 for Learning Representations by Maximizing Compression
Figure 2 for Learning Representations by Maximizing Compression
Figure 3 for Learning Representations by Maximizing Compression
Figure 4 for Learning Representations by Maximizing Compression
* 8 pages, 3 figures 
  Access Paper or Ask Questions

Efficient Learning of Sparse Invariant Representations


May 26, 2011
Karol Gregor, Yann LeCun

Figure 1 for Efficient Learning of Sparse Invariant Representations
Figure 2 for Efficient Learning of Sparse Invariant Representations
Figure 3 for Efficient Learning of Sparse Invariant Representations
Figure 4 for Efficient Learning of Sparse Invariant Representations
* 9 pages + 6 supplement pages 
  Access Paper or Ask Questions

Behavior and performance of the deep belief networks on image classification


Dec 03, 2009
Karol Gregor, Gregory Griffin

Figure 1 for Behavior and performance of the deep belief networks on image classification
Figure 2 for Behavior and performance of the deep belief networks on image classification
Figure 3 for Behavior and performance of the deep belief networks on image classification
Figure 4 for Behavior and performance of the deep belief networks on image classification
* 8 pages, 9 figures 
  Access Paper or Ask Questions