Daan Wierstra

According to our database, Daan Wierstra authored at least 59 papers between 2004 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Scaling Instructable Agents Across Many Simulated Worlds.

SIMA Team, Maria Abi Raad, Arun Ahuja, Catarina Barros, Frederic Besse, Andrew Bolt, Adrian Bolton, Bethanie Brownfield, Gavin Buttimore, Max Cant, Sarah Chakera, Stephanie C. Y. Chan, Jeff Clune, Adrian Collister, Vikki Copeman, Alex Cullum, Ishita Dasgupta, Dario de Cesare, Julia Di Trapani, Yani Donchev, Emma Dunleavy, Martin Engelcke, Ryan Faulkner, Frankie Garcia, Charles Gbadamosi, Zhitao Gong, Lucy Gonzalez, Kshitij Gupta, Karol Gregor, Arne Olav Hallingstad, Tim Harley, Sam Haves, Felix Hill, Ed Hirst, Drew A. Hudson, Jony Hudson, Steph Hughes-Fitt, Danilo J. Rezende, Mimi Jasarevic, Laura Kampis, Nan Rosemary Ke, Thomas Keck, Junkyung Kim, Oscar Knagg, Kavya Kopparapu, Andrew K. Lampinen, Shane Legg, Alexander Lerchner, Marjorie Limont, Yulan Liu, Maria Loks-Thompson, Joseph Marino, Kathryn Martin Cussons, Loic Matthey, Siobhan Mcloughlin, Piermaria Mendolicchio, Hamza Merzic, Anna Mitenkova, Alexandre Moufarek, Valéria Oliveira, Yanko Gitahy Oliveira, Hannah Openshaw, Renke Pan, Aneesh Pappu, Alex Platonov, Ollie Purkiss, David P. Reichert, John Reid, Pierre Harvey Richemond, Tyson Roberts, Giles Ruscoe, Jaume Sanchez Elias, Tasha Sandars, Daniel P. Sawyer, Tim Scholtes, Guy Simmons, Daniel Slater, Hubert Soyer, Heiko Strathmann, Peter Stys, Allison C. Tam, Denis Teplyashin, Tayfun Terzi, Davide Vercelli, Bojan Vujatovic, Marcus Wainwright, Jane X. Wang, Zhengdong Wang, Daan Wierstra, Duncan Williams, Nathaniel Wong, Sarah York, Nick Young

CoRR, 2024

2019

Towards Interpretable Reinforcement Learning Using Attention Augmented Agents.

Alexander Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, Danilo Jimenez Rezende

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Relational inductive biases, deep learning, and graph networks.

Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Çaglar Gülçehre, H. Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey R. Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew M. Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu

CoRR, 2018

Learning and Querying Fast Generative Models for Reinforcement Learning.

Lars Buesing, Theophane Weber, Sébastien Racanière, S. M. Ali Eslami, Danilo Jimenez Rezende, David P. Reichert, Fabio Viola, Frederic Besse, Karol Gregor, Demis Hassabis, Daan Wierstra

CoRR, 2018

Relational recurrent neural networks.

Adam Santoro, Ryan Faulkner, David Raposo, Jack W. Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy P. Lillicrap

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning to Search with MCTSnets.

Arthur Guez, Theophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017.

Matthew M. Botvinick, David G. T. Barrett, Peter W. Battaglia, Nando de Freitas, Dharshan Kumaran, Joel Z. Leibo, Tim Lillicrap, Joseph Modayil, S. Mohamed, Neil C. Rabinowitz, Danilo Jimenez Rezende, Adam Santoro, Tom Schaul, Christopher Summerfield, Greg Wayne, Theophane Weber, Daan Wierstra, Shane Legg, Demis Hassabis

CoRR, 2017

Imagination-Augmented Agents for Deep Reinforcement Learning.

Theophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter W. Battaglia, David Silver, Daan Wierstra

CoRR, 2017

Learning model-based planning from scratch.

Razvan Pascanu, Yujia Li, Oriol Vinyals, Nicolas Heess, Lars Buesing, Sébastien Racanière, David P. Reichert, Theophane Weber, Daan Wierstra, Peter W. Battaglia

CoRR, 2017

PathNet: Evolution Channels Gradient Descent in Super Neural Networks.

Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A. Rusu, Alexander Pritzel, Daan Wierstra

CoRR, 2017

Comparison of Maximum Likelihood and GAN-based training of Real NVPs.

Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria, Daan Wierstra, Peter Dayan

CoRR, 2017

Imagination-Augmented Agents for Deep Reinforcement Learning.

Sébastien Racanière, Theophane Weber, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter W. Battaglia, Demis Hassabis, David Silver, Daan Wierstra

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Neural Episodic Control.

Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech Badia, Oriol Vinyals, Demis Hassabis, Daan Wierstra, Charles Blundell

Proceedings of the 34th International Conference on Machine Learning, 2017

Variational Intrinsic Control.

Karol Gregor, Danilo Jimenez Rezende, Daan Wierstra

Proceedings of the 5th International Conference on Learning Representations, 2017

Recurrent Environment Simulators.

Silvia Chiappa, Sébastien Racanière, Daan Wierstra, Shakir Mohamed

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

One-shot Learning with Memory-Augmented Neural Networks.

Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, Timothy P. Lillicrap

CoRR, 2016

Continuous control with deep reinforcement learning.

Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra

Proceedings of the 4th International Conference on Learning Representations, 2016

Model-Free Episodic Control.

Charles Blundell, Benigno Uria, Alexander Pritzel, Yazhe Li, Avraham Ruderman, Joel Z. Leibo, Jack W. Rae, Daan Wierstra, Demis Hassabis

CoRR, 2016

Matching Networks for One Shot Learning.

Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, Daan Wierstra

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Towards Conceptual Compression.

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

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Meta-Learning with Memory-Augmented Neural Networks.

Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, Timothy P. Lillicrap

Proceedings of the 33nd International Conference on Machine Learning, 2016

One-Shot Generalization in Deep Generative Models.

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

Proceedings of the 33nd International Conference on Machine Learning, 2016

Convolution by Evolution: Differentiable Pattern Producing Networks.

Chrisantha Fernando, Dylan Banarse, Malcolm Reynolds, Frederic Besse, David Pfau, Max Jaderberg, Marc Lanctot, Daan Wierstra

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20, 2016

2015

Human-level control through deep reinforcement learning.

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis

Nat., 2015

DRAW: A Recurrent Neural Network For Image Generation.

Karol Gregor, Ivo Danihelka, Alex Graves, Daan Wierstra

CoRR, 2015

Weight Uncertainty in Neural Networks.

Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra

CoRR, 2015

DRAW: A Recurrent Neural Network For Image Generation.

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

Proceedings of the 32nd International Conference on Machine Learning, 2015

Weight Uncertainty in Neural Network.

Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Natural evolution strategies.

Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, Jürgen Schmidhuber

J. Mach. Learn. Res., 2014

Stochastic Back-propagation and Variational Inference in Deep Latent Gaussian Models.

Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra

CoRR, 2014

Deterministic Policy Gradient Algorithms.

David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin A. Riedmiller

Proceedings of the 31th International Conference on Machine Learning, 2014

Stochastic Backpropagation and Approximate Inference in Deep Generative Models.

Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra

Proceedings of the 31th International Conference on Machine Learning, 2014

Deep AutoRegressive Networks.

Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, Daan Wierstra

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Playing Atari with Deep Reinforcement Learning.

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin A. Riedmiller

CoRR, 2013

Deep AutoRegressive Networks.

Karol Gregor, Andriy Mnih, Daan Wierstra

CoRR, 2013

2011

Natural Evolution Strategies

Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jürgen Schmidhuber

CoRR, 2011

Variational Learning for Recurrent Spiking Networks.

Danilo Jimenez Rezende, Daan Wierstra, Wulfram Gerstner

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Curiosity-driven optimization.

Tom Schaul, Yi Sun, Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber

Proceedings of the IEEE Congress on Evolutionary Computation, 2011

2010

A study in direct policy search.

Daniel Pieter Wierstra

PhD thesis, 2010

Exploring parameter space in reinforcement learning.

Thomas Rückstieß, Frank Sehnke, Tom Schaul, Daan Wierstra, Yi Sun, Jürgen Schmidhuber

Paladyn J. Behav. Robotics, 2010

PyBrain.

Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber

J. Mach. Learn. Res., 2010

Recurrent policy gradients.

Daan Wierstra, Alexander Förster, Jan Peters, Jürgen Schmidhuber

Log. J. IGPL, 2010

Exponential natural evolution strategies.

Tobias Glasmachers, Tom Schaul, Yi Sun, Daan Wierstra, Jürgen Schmidhuber

Proceedings of the Genetic and Evolutionary Computation Conference, 2010

2009

Ontogenetic and Phylogenetic Reinforcement Learning.

Tom Schaul, Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber, Christian Igel, Julian Togelius

Künstliche Intell., 2009

Stochastic search using the natural gradient.

Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Evolving Memory Cell Structures for Sequence Learning.

Justin Bayer, Daan Wierstra, Julian Togelius, Jürgen Schmidhuber

Proceedings of the Artificial Neural Networks, 2009

Efficient natural evolution strategies.

Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber

Proceedings of the Genetic and Evolutionary Computation Conference, 2009

Robust player imitation using multiobjective evolution.

Niels van Hoorn, Julian Togelius, Daan Wierstra, Jürgen Schmidhuber

Proceedings of the IEEE Congress on Evolutionary Computation, 2009

2008

A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks.

Hermann Georg Mayer, Faustino J. Gomez, Daan Wierstra, Istvan Nagy, Alois C. Knoll, Jürgen Schmidhuber

Adv. Robotics, 2008

Fitness Expectation Maximization.

Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber

Proceedings of the Parallel Problem Solving from Nature, 2008

Episodic Reinforcement Learning by Logistic Reward-Weighted Regression.

Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber

Proceedings of the Artificial Neural Networks, 2008

Natural Evolution Strategies.

Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber

Proceedings of the IEEE Congress on Evolutionary Computation, 2008

2007

Training Recurrent Networks by Evolino.

Jürgen Schmidhuber, Daan Wierstra, Matteo Gagliolo, Faustino J. Gomez

Neural Comput., 2007

Solving Deep Memory POMDPs with Recurrent Policy Gradients.

Daan Wierstra, Alexander Förster, Jan Peters, Jürgen Schmidhuber

Proceedings of the Artificial Neural Networks, 2007

Policy Gradient Critics.

Daan Wierstra, Jürgen Schmidhuber

Proceedings of the Machine Learning: ECML 2007, 2007

2006

Evolino for recurrent support vector machines.

Jürgen Schmidhuber, Matteo Gagliolo, Daan Wierstra, Faustino J. Gomez

Proceedings of the 14th European Symposium on Artificial Neural Networks, 2006

2005

Evolino: Hybrid Neuroevolution/Optimal Linear Search for Sequence Learning.

Jürgen Schmidhuber, Daan Wierstra, Faustino J. Gomez

Proceedings of the IJCAI-05, Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, Edinburgh, Scotland, UK, July 30, 2005

Modeling systems with internal state using evolino.

Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber

Proceedings of the Genetic and Evolutionary Computation Conference, 2005

2004

Utile distinction hidden Markov models.

Daan Wierstra, Marco A. Wiering

Proceedings of the Machine Learning, 2004