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ORCIDDaan Wierstra
Daniel Pieter Wierstra
Person information
- affiliation: University of Lugano, Dalle Molle Institute for Artificial Intelligence
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2020 – today
- 2024
[i28]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:
Scaling Instructable Agents Across Many Simulated Worlds. CoRR abs/2404.10179 (2024)
2010 – 2019
- 2019
- [c35]Alexander Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, Danilo Jimenez Rezende:
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents. NeurIPS 2019: 12329-12338 - [i27]Alex Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, Danilo J. Rezende:
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents. CoRR abs/1906.02500 (2019) - 2018
- [c34]Arthur Guez, Theophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver:
Learning to Search with MCTSnets. ICML 2018: 1817-1826 - [c33]Adam Santoro, Ryan Faulkner, David Raposo, Jack W. Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy P. Lillicrap:
Relational recurrent neural networks. NeurIPS 2018: 7310-7321 - [i26]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:
Learning and Querying Fast Generative Models for Reinforcement Learning. CoRR abs/1802.03006 (2018) - [i25]Arthur Guez, Théophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver:
Learning to Search with MCTSnets. CoRR abs/1802.04697 (2018) - [i24]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:
Relational inductive biases, deep learning, and graph networks. CoRR abs/1806.01261 (2018) - [i23]Adam Santoro, Ryan Faulkner, David Raposo, Jack W. Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy P. Lillicrap:
Relational recurrent neural networks. CoRR abs/1806.01822 (2018) - 2017
- [c32]Silvia Chiappa, Sébastien Racanière, Daan Wierstra, Shakir Mohamed:
Recurrent Environment Simulators. ICLR (Poster) 2017 - [c31]Karol Gregor, Danilo Jimenez Rezende, Daan Wierstra:
Variational Intrinsic Control. ICLR (Workshop) 2017 - [c30]Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech Badia, Oriol Vinyals, Demis Hassabis, Daan Wierstra, Charles Blundell:
Neural Episodic Control. ICML 2017: 2827-2836 - [c29]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:
Imagination-Augmented Agents for Deep Reinforcement Learning. NIPS 2017: 5690-5701 - [i22]Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A. Rusu, Alexander Pritzel, Daan Wierstra:
PathNet: Evolution Channels Gradient Descent in Super Neural Networks. CoRR abs/1701.08734 (2017) - [i21]Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech Badia, Oriol Vinyals, Demis Hassabis, Daan Wierstra, Charles Blundell:
Neural Episodic Control. CoRR abs/1703.01988 (2017) - [i20]Silvia Chiappa, Sébastien Racanière, Daan Wierstra, Shakir Mohamed:
Recurrent Environment Simulators. CoRR abs/1704.02254 (2017) - [i19]Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria, Daan Wierstra, Peter Dayan:
Comparison of Maximum Likelihood and GAN-based training of Real NVPs. CoRR abs/1705.05263 (2017) - [i18]Razvan Pascanu, Yujia Li, Oriol Vinyals, Nicolas Heess, Lars Buesing, Sébastien Racanière, David P. Reichert, Theophane Weber, Daan Wierstra, Peter W. Battaglia:
Learning model-based planning from scratch. CoRR abs/1707.06170 (2017) - [i17]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:
Imagination-Augmented Agents for Deep Reinforcement Learning. CoRR abs/1707.06203 (2017) - [i16]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:
Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. CoRR abs/1711.08378 (2017) - 2016
- [c28]Chrisantha Fernando, Dylan Banarse, Malcolm Reynolds, Frederic Besse, David Pfau, Max Jaderberg, Marc Lanctot, Daan Wierstra:
Convolution by Evolution: Differentiable Pattern Producing Networks. GECCO 2016: 109-116 - [c27]Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra:
One-Shot Generalization in Deep Generative Models. ICML 2016: 1521-1529 - [c26]Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, Timothy P. Lillicrap:
Meta-Learning with Memory-Augmented Neural Networks. ICML 2016: 1842-1850 - [c25]Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra:
Towards Conceptual Compression. NIPS 2016: 3549-3557 - [c24]Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, Daan Wierstra:
Matching Networks for One Shot Learning. NIPS 2016: 3630-3638 - [c23]Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra:
Continuous control with deep reinforcement learning. ICLR (Poster) 2016 - [i15]Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra:
One-Shot Generalization in Deep Generative Models. CoRR abs/1603.05106 (2016) - [i14]Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, Daan Wierstra:
Towards Conceptual Compression. CoRR abs/1604.08772 (2016) - [i13]Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, Timothy P. Lillicrap:
One-shot Learning with Memory-Augmented Neural Networks. CoRR abs/1605.06065 (2016) - [i12]Chrisantha Fernando, Dylan Banarse, Malcolm Reynolds, Frederic Besse, David Pfau, Max Jaderberg, Marc Lanctot, Daan Wierstra:
Convolution by Evolution: Differentiable Pattern Producing Networks. CoRR abs/1606.02580 (2016) - [i11]Oriol Vinyals, Charles Blundell, Timothy P. Lillicrap, Koray Kavukcuoglu, Daan Wierstra:
Matching Networks for One Shot Learning. CoRR abs/1606.04080 (2016) - [i10]Charles Blundell, Benigno Uria, Alexander Pritzel, Yazhe Li, Avraham Ruderman, Joel Z. Leibo, Jack W. Rae, Daan Wierstra, Demis Hassabis:
Model-Free Episodic Control. CoRR abs/1606.04460 (2016) - [i9]Karol Gregor, Danilo Jimenez Rezende, Daan Wierstra:
Variational Intrinsic Control. CoRR abs/1611.07507 (2016) - 2015
- [j8]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:
Human-level control through deep reinforcement learning. Nat. 518(7540): 529-533 (2015) - [c22]Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra:
DRAW: A Recurrent Neural Network For Image Generation. ICML 2015: 1462-1471 - [c21]Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra:
Weight Uncertainty in Neural Network. ICML 2015: 1613-1622 - [i8]Karol Gregor, Ivo Danihelka, Alex Graves, Daan Wierstra:
DRAW: A Recurrent Neural Network For Image Generation. CoRR abs/1502.04623 (2015) - [i7]Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra:
Weight Uncertainty in Neural Networks. CoRR abs/1505.05424 (2015) - 2014
- [j7]Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, Jürgen Schmidhuber:
Natural evolution strategies. J. Mach. Learn. Res. 15(1): 949-980 (2014) - [c20]David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin A. Riedmiller:
Deterministic Policy Gradient Algorithms. ICML 2014: 387-395 - [c19]Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, Daan Wierstra:
Deep AutoRegressive Networks. ICML 2014: 1242-1250 - [c18]Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra:
Stochastic Backpropagation and Approximate Inference in Deep Generative Models. ICML 2014: 1278-1286 - [i6]Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra:
Stochastic Back-propagation and Variational Inference in Deep Latent Gaussian Models. CoRR abs/1401.4082 (2014) - 2013
- [i5]Karol Gregor, Andriy Mnih, Daan Wierstra:
Deep AutoRegressive Networks. CoRR abs/1310.8499 (2013) - [i4]Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin A. Riedmiller:
Playing Atari with Deep Reinforcement Learning. CoRR abs/1312.5602 (2013) - 2012
- [i3]Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber:
Efficient Natural Evolution Strategies. CoRR abs/1209.5853 (2012) - 2011
- [c17]Tom Schaul, Yi Sun, Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber:
Curiosity-driven optimization. IEEE Congress on Evolutionary Computation 2011: 1343-1349 - [c16]Danilo Jimenez Rezende, Daan Wierstra, Wulfram Gerstner:
Variational Learning for Recurrent Spiking Networks. NIPS 2011: 136-144 - [i2]Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jürgen Schmidhuber:
Natural Evolution Strategies. CoRR abs/1106.4487 (2011) - 2010
- [b1]Daniel Pieter Wierstra:
A study in direct policy search. Technical University Munich, 2010, pp. 1-97 - [j6]Daan Wierstra, Alexander Förster
, Jan Peters, Jürgen Schmidhuber:
Recurrent policy gradients. Log. J. IGPL 18(5): 620-634 (2010) - [j5]Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber:
PyBrain. J. Mach. Learn. Res. 11: 743-746 (2010) - [j4]Thomas Rückstieß, Frank Sehnke, Tom Schaul, Daan Wierstra, Yi Sun, Jürgen Schmidhuber:
Exploring parameter space in reinforcement learning. Paladyn J. Behav. Robotics 1(1): 14-24 (2010) - [c15]Tobias Glasmachers, Tom Schaul, Yi Sun, Daan Wierstra, Jürgen Schmidhuber:
Exponential natural evolution strategies. GECCO 2010: 393-400
2000 – 2009
- 2009
- [j3]Tom Schaul, Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber, Christian Igel, Julian Togelius:
Ontogenetic and Phylogenetic Reinforcement Learning. Künstliche Intell. 23(3): 30-33 (2009) - [c14]Niels van Hoorn, Julian Togelius, Daan Wierstra, Jürgen Schmidhuber:
Robust player imitation using multiobjective evolution. IEEE Congress on Evolutionary Computation 2009: 652-659 - [c13]Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber:
Efficient natural evolution strategies. GECCO 2009: 539-546 - [c12]Justin Bayer, Daan Wierstra, Julian Togelius, Jürgen Schmidhuber:
Evolving Memory Cell Structures for Sequence Learning. ICANN (2) 2009: 755-764 - [c11]Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber:
Stochastic search using the natural gradient. ICML 2009: 1161-1168 - 2008
- [j2]Hermann Georg Mayer, Faustino J. Gomez, Daan Wierstra, Istvan Nagy, Alois C. Knoll, Jürgen Schmidhuber:
A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks. Adv. Robotics 22(13-14): 1521-1537 (2008) - [c10]Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber:
Natural Evolution Strategies. IEEE Congress on Evolutionary Computation 2008: 3381-3387 - [c9]Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber:
Episodic Reinforcement Learning by Logistic Reward-Weighted Regression. ICANN (1) 2008: 407-416 - [c8]Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber:
Fitness Expectation Maximization. PPSN 2008: 337-346 - 2007
- [j1]Jürgen Schmidhuber, Daan Wierstra, Matteo Gagliolo, Faustino J. Gomez:
Training Recurrent Networks by Evolino. Neural Comput. 19(3): 757-779 (2007) - [c7]Daan Wierstra, Jürgen Schmidhuber:
Policy Gradient Critics. ECML 2007: 466-477 - [c6]Daan Wierstra, Alexander Förster, Jan Peters, Jürgen Schmidhuber:
Solving Deep Memory POMDPs with Recurrent Policy Gradients. ICANN (1) 2007: 697-706 - 2006
- [c5]Jürgen Schmidhuber, Matteo Gagliolo, Daan Wierstra, Faustino J. Gomez:
Evolino for recurrent support vector machines. ESANN 2006: 593-598 - [c4]Hermann Georg Mayer, Faustino J. Gomez, Daan Wierstra, Istvan Nagy, Alois C. Knoll, Jürgen Schmidhuber:
A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks. IROS 2006: 543-548 - 2005
- [c3]Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber:
Modeling systems with internal state using evolino. GECCO 2005: 1795-1802 - [c2]Jürgen Schmidhuber, Daan Wierstra, Faustino J. Gomez:
Evolino: Hybrid Neuroevolution/Optimal Linear Search for Sequence Learning. IJCAI 2005: 853-858 - [i1]Jürgen Schmidhuber, Matteo Gagliolo, Daan Wierstra, Faustino J. Gomez:
Evolino for recurrent support vector machines. CoRR abs/cs/0512062 (2005) - 2004
- [c1]Daan Wierstra, Marco A. Wiering:
Utile distinction hidden Markov models. ICML 2004
Coauthor Index
Danilo Jimenez Rezende
aka: Danilo J. Rezende
aka: Danilo J. Rezende
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