Daan Wierstra - RankMe

71 papers A* 15A 6B 4Journal 36Unranked 10

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Scaling Instructable Agents Across Many Simulated Worlds. CoRR 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
2019	A*	`conf`	Towards Interpretable Reinforcement Learning Using Attention Augmented Agents. NeurIPS Alexander Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, Danilo Jimenez Rezende
2019	J	`jnl`	Towards Interpretable Reinforcement Learning Using Attention Augmented Agents. CoRR Alex Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, Danilo J. Rezende
2018	J	`jnl`	Learning and Querying Fast Generative Models for Reinforcement Learning. CoRR 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
2018	A*	`conf`	Learning to Search with MCTSnets. ICML Arthur Guez, Theophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver
2018	J	`jnl`	Learning to Search with MCTSnets. CoRR Arthur Guez, Théophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver
2018	J	`jnl`	Relational inductive biases, deep learning, and graph networks. CoRR 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
2018	A*	`conf`	Relational recurrent neural networks. NeurIPS Adam Santoro, Ryan Faulkner, David Raposo, Jack W. Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy P. Lillicrap
2018	J	`jnl`	Relational recurrent neural networks. CoRR Adam Santoro, Ryan Faulkner, David Raposo, Jack W. Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy P. Lillicrap
2017	J	`jnl`	Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. CoRR 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
2017	J	`jnl`	Comparison of Maximum Likelihood and GAN-based training of Real NVPs. CoRR Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria, Daan Wierstra, Peter Dayan
2017	—	`conf`	Imagination-Augmented Agents for Deep Reinforcement Learning. NIPS 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
2017	J	`jnl`	Imagination-Augmented Agents for Deep Reinforcement Learning. CoRR 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
2017	J	`jnl`	Learning model-based planning from scratch. CoRR Razvan Pascanu, Yujia Li, Oriol Vinyals, Nicolas Heess, Lars Buesing, Sébastien Racanière, David P. Reichert, Theophane Weber, Daan Wierstra, Peter W. Battaglia
2017	A*	`conf`	Neural Episodic Control. ICML Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech Badia, Oriol Vinyals, Demis Hassabis, Daan Wierstra, Charles Blundell
2017	J	`jnl`	Neural Episodic Control. CoRR Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech Badia, Oriol Vinyals, Demis Hassabis, Daan Wierstra, Charles Blundell
2017	J	`jnl`	PathNet: Evolution Channels Gradient Descent in Super Neural Networks. CoRR Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A. Rusu, Alexander Pritzel, Daan Wierstra
2017	—	`conf`	Recurrent Environment Simulators. ICLR (Poster) Silvia Chiappa, Sébastien Racanière, Daan Wierstra, Shakir Mohamed
2017	J	`jnl`	Recurrent Environment Simulators. CoRR Silvia Chiappa, Sébastien Racanière, Daan Wierstra, Shakir Mohamed
2017	—	`conf`	Variational Intrinsic Control. ICLR (Workshop) Karol Gregor, Danilo Jimenez Rezende, Daan Wierstra
2016	A*	`conf`	Continuous control with deep reinforcement learning. ICLR (Poster) Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra
2016	A	`conf`	Convolution by Evolution: Differentiable Pattern Producing Networks. GECCO Chrisantha Fernando, Dylan Banarse, Malcolm Reynolds, Frederic Besse, David Pfau, Max Jaderberg, Marc Lanctot, Daan Wierstra
2016	J	`jnl`	Convolution by Evolution: Differentiable Pattern Producing Networks. CoRR Chrisantha Fernando, Dylan Banarse, Malcolm Reynolds, Frederic Besse, David Pfau, Max Jaderberg, Marc Lanctot, Daan Wierstra
2016	—	`conf`	Matching Networks for One Shot Learning. NIPS Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, Daan Wierstra
2016	J	`jnl`	Matching Networks for One Shot Learning. CoRR Oriol Vinyals, Charles Blundell, Timothy P. Lillicrap, Koray Kavukcuoglu, Daan Wierstra
2016	A*	`conf`	Meta-Learning with Memory-Augmented Neural Networks. ICML Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, Timothy P. Lillicrap
2016	J	`jnl`	Model-Free Episodic Control. CoRR Charles Blundell, Benigno Uria, Alexander Pritzel, Yazhe Li, Avraham Ruderman, Joel Z. Leibo, Jack W. Rae, Daan Wierstra, Demis Hassabis
2016	A*	`conf`	One-Shot Generalization in Deep Generative Models. ICML Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra
2016	J	`jnl`	One-Shot Generalization in Deep Generative Models. CoRR Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra
2016	J	`jnl`	One-shot Learning with Memory-Augmented Neural Networks. CoRR Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, Timothy P. Lillicrap
2016	—	`conf`	Towards Conceptual Compression. NIPS Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra
2016	J	`jnl`	Towards Conceptual Compression. CoRR Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra
2016	J	`jnl`	Variational Intrinsic Control. CoRR Karol Gregor, Danilo Jimenez Rezende, Daan Wierstra
2015	A*	`conf`	DRAW: A Recurrent Neural Network For Image Generation. ICML Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra
2015	J	`jnl`	DRAW: A Recurrent Neural Network For Image Generation. CoRR Karol Gregor, Ivo Danihelka, Alex Graves, Daan Wierstra
2015	J	`jnl`	Human-level control through deep reinforcement learning. Nat. 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
2015	A*	`conf`	Weight Uncertainty in Neural Network. ICML Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
2015	J	`jnl`	Weight Uncertainty in Neural Networks. CoRR Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
2014	A*	`conf`	Deep AutoRegressive Networks. ICML Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, Daan Wierstra
2014	A*	`conf`	Deterministic Policy Gradient Algorithms. ICML David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin A. Riedmiller
2014	J	`jnl`	Natural evolution strategies. J. Mach. Learn. Res. Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, Jürgen Schmidhuber
2014	J	`jnl`	Stochastic Back-propagation and Variational Inference in Deep Latent Gaussian Models. CoRR Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra
2014	A*	`conf`	Stochastic Backpropagation and Approximate Inference in Deep Generative Models. ICML Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra
2013	J	`jnl`	Deep AutoRegressive Networks. CoRR Karol Gregor, Andriy Mnih, Daan Wierstra
2013	J	`jnl`	Playing Atari with Deep Reinforcement Learning. CoRR Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin A. Riedmiller
2012	J	`jnl`	Efficient Natural Evolution Strategies CoRR Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber
2011	B	`conf`	Curiosity-driven optimization. IEEE Congress on Evolutionary Computation Tom Schaul, Yi Sun, Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber
2011	J	`jnl`	Natural Evolution Strategies CoRR Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jürgen Schmidhuber
2011	—	`conf`	Variational Learning for Recurrent Spiking Networks. NIPS Danilo Jimenez Rezende, Daan Wierstra, Wulfram Gerstner
2010	J	`jnl`	Exploring parameter space in reinforcement learning. Paladyn J. Behav. Robotics Thomas Rückstieß, Frank Sehnke, Tom Schaul, Daan Wierstra, Yi Sun, Jürgen Schmidhuber
2010	A	`conf`	Exponential natural evolution strategies. GECCO Tobias Glasmachers, Tom Schaul, Yi Sun, Daan Wierstra, Jürgen Schmidhuber
2010	J	`jnl`	PyBrain. J. Mach. Learn. Res. Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber
2010	J	`jnl`	Recurrent policy gradients. Log. J. IGPL Daan Wierstra, Alexander Förster, Jan Peters, Jürgen Schmidhuber
2009	A	`conf`	Efficient natural evolution strategies. GECCO Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber
2009	—	`conf`	Evolving Memory Cell Structures for Sequence Learning. ICANN (2) Justin Bayer, Daan Wierstra, Julian Togelius, Jürgen Schmidhuber
2009	J	`jnl`	Ontogenetic and Phylogenetic Reinforcement Learning. Künstliche Intell. Tom Schaul, Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber, Christian Igel, Julian Togelius
2009	B	`conf`	Robust player imitation using multiobjective evolution. IEEE Congress on Evolutionary Computation Niels van Hoorn, Julian Togelius, Daan Wierstra, Jürgen Schmidhuber
2009	A*	`conf`	Stochastic search using the natural gradient. ICML Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber
2008	J	`jnl`	A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks. Adv. Robotics Hermann Georg Mayer, Faustino J. Gomez, Daan Wierstra, Istvan Nagy, Alois C. Knoll, Jürgen Schmidhuber
2008	—	`conf`	Episodic Reinforcement Learning by Logistic Reward-Weighted Regression. ICANN (1) Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber
2008	A	`conf`	Fitness Expectation Maximization. PPSN Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber
2008	B	`conf`	Natural Evolution Strategies. IEEE Congress on Evolutionary Computation Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber
2007	—	`conf`	Policy Gradient Critics. ECML Daan Wierstra, Jürgen Schmidhuber
2007	—	`conf`	Solving Deep Memory POMDPs with Recurrent Policy Gradients. ICANN (1) Daan Wierstra, Alexander Förster, Jan Peters, Jürgen Schmidhuber
2007	J	`jnl`	Training Recurrent Networks by Evolino. Neural Comput. Jürgen Schmidhuber, Daan Wierstra, Matteo Gagliolo, Faustino J. Gomez
2006	A	`conf`	A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks. IROS Hermann Georg Mayer, Faustino J. Gomez, Daan Wierstra, Istvan Nagy, Alois C. Knoll, Jürgen Schmidhuber
2006	B	`conf`	Evolino for recurrent support vector machines. ESANN Jürgen Schmidhuber, Matteo Gagliolo, Daan Wierstra, Faustino J. Gomez
2005	J	`jnl`	Evolino for recurrent support vector machines CoRR Jürgen Schmidhuber, Matteo Gagliolo, Daan Wierstra, Faustino J. Gomez
2005	A*	`conf`	Evolino: Hybrid Neuroevolution/Optimal Linear Search for Sequence Learning. IJCAI Jürgen Schmidhuber, Daan Wierstra, Faustino J. Gomez
2005	A	`conf`	Modeling systems with internal state using evolino. GECCO Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber
2004	A*	`conf`	Utile distinction hidden Markov models. ICML Daan Wierstra, Marco A. Wiering