Utile distinction hidden Markov models. - Daan Wierstra; Marco A. Wiering (ICML, 2004)

Modeling systems with internal state using evolino. - Daan Wierstra; Faustino J. Gomez; Jürgen Schmidhuber (GECCO, 2005)

Evolino: Hybrid Neuroevolution/Optimal Linear Search for Sequence Learning. - Jürgen Schmidhuber; Daan Wierstra; Faustino J. Gomez (IJCAI, 2005)

Evolino for recurrent support vector machines. - Jürgen Schmidhuber; Matteo Gagliolo; Daan Wierstra; Faustino J. Gomez (ESANN, 2006)

Policy Gradient Critics. - Daan Wierstra; Jürgen Schmidhuber (ECML, 2007)

Solving Deep Memory POMDPs with Recurrent Policy Gradients. - Daan Wierstra; Alexander Förster; Jan Peters 001; Jürgen Schmidhuber (ICANN (1), 2007)

Training Recurrent Networks by Evolino. - Jürgen Schmidhuber; Daan Wierstra; Matteo Gagliolo; Faustino J. Gomez (Neural Comput., 2007)

Natural Evolution Strategies. - Daan Wierstra; Tom Schaul; Jan Peters 001; Jürgen Schmidhuber (IEEE Congress on Evolutionary Computation, 2008)

Episodic Reinforcement Learning by Logistic Reward-Weighted Regression. - Daan Wierstra; Tom Schaul; Jan Peters 001; Jürgen Schmidhuber (ICANN (1), 2008)

Fitness Expectation Maximization. - Daan Wierstra; Tom Schaul; Jan Peters 001; Jürgen Schmidhuber (PPSN, 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 002; Alois C. Knoll; Jürgen Schmidhuber (Adv. Robotics, 2008)

Robust player imitation using multiobjective evolution. - Niels van Hoorn; Julian Togelius; Daan Wierstra; Jürgen Schmidhuber (IEEE Congress on Evolutionary Computation, 2009)

Efficient natural evolution strategies. - Yi Sun 003; Daan Wierstra; Tom Schaul; Jürgen Schmidhuber (GECCO, 2009)

Evolving Memory Cell Structures for Sequence Learning. - Justin Bayer; Daan Wierstra; Julian Togelius; Jürgen Schmidhuber (ICANN (2), 2009)

Stochastic search using the natural gradient. - Yi Sun 003; Daan Wierstra; Tom Schaul; Jürgen Schmidhuber (ICML, 2009)

Ontogenetic and Phylogenetic Reinforcement Learning. - Tom Schaul; Daan Wierstra; Faustino J. Gomez; Jürgen Schmidhuber; Christian Igel; Julian Togelius (Künstliche Intell., 2009)

Exponential natural evolution strategies. - Tobias Glasmachers; Tom Schaul; Yi Sun 003; Daan Wierstra; Jürgen Schmidhuber (GECCO, 2010)

Recurrent policy gradients. - Daan Wierstra; Alexander Förster; Jan Peters 001; Jürgen Schmidhuber (Log. J. IGPL, 2010)

PyBrain. - Tom Schaul; Justin Bayer; Daan Wierstra; Yi Sun 003; Martin Felder; Frank Sehnke; Thomas Rückstieß; Jürgen Schmidhuber (J. Mach. Learn. Res., 2010)

Exploring parameter space in reinforcement learning. - Thomas Rückstieß; Frank Sehnke; Tom Schaul; Daan Wierstra; Yi Sun 003; Jürgen Schmidhuber (Paladyn J. Behav. Robotics, 2010)

A study in direct policy search. - Daniel Pieter Wierstra (2010)

Curiosity-driven optimization. - Tom Schaul; Yi Sun 003; Daan Wierstra; Faustino J. Gomez; Jürgen Schmidhuber (IEEE Congress on Evolutionary Computation, 2011)

Variational Learning for Recurrent Spiking Networks. - Danilo Jimenez Rezende; Daan Wierstra; Wulfram Gerstner (NIPS, 2011)

Natural Evolution Strategies - Daan Wierstra; Tom Schaul; Tobias Glasmachers; Yi Sun 003; Jürgen Schmidhuber (CoRR, 2011)

Deep AutoRegressive Networks. - Karol Gregor; Andriy Mnih; Daan Wierstra (CoRR, 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; Ivo Danihelka; Andriy Mnih; Charles Blundell; Daan Wierstra (ICML, 2014)

Stochastic Backpropagation and Approximate Inference in Deep Generative Models. - Danilo Jimenez Rezende; Shakir Mohamed; Daan Wierstra (ICML, 2014)

Deterministic Policy Gradient Algorithms. - David Silver; Guy Lever; Nicolas Heess; Thomas Degris; Daan Wierstra; Martin A. Riedmiller (ICML, 2014)

Stochastic Back-propagation and Variational Inference in Deep Latent Gaussian Models. - Danilo Jimenez Rezende; Shakir Mohamed; Daan Wierstra (CoRR, 2014)

Natural evolution strategies. - Daan Wierstra; Tom Schaul; Tobias Glasmachers; Yi Sun 003; Jan Peters 001; Jürgen Schmidhuber (J. Mach. Learn. Res., 2014)

Weight Uncertainty in Neural Network. - Charles Blundell; Julien Cornebise; Koray Kavukcuoglu; Daan Wierstra (ICML, 2015)

DRAW: A Recurrent Neural Network For Image Generation. - Karol Gregor; Ivo Danihelka; Alex Graves; Danilo Jimenez Rezende; Daan Wierstra (ICML, 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; Daan Wierstra (CoRR, 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; et al. (Nat., 2015)

Convolution by Evolution: Differentiable Pattern Producing Networks. - Chrisantha Fernando; Dylan Banarse; Malcolm Reynolds; Frederic Besse; David Pfau; Max Jaderberg; Marc Lanctot; Daan Wierstra (GECCO, 2016)

One-Shot Generalization in Deep Generative Models. - Danilo Jimenez Rezende; Shakir Mohamed; Ivo Danihelka; Karol Gregor; Daan Wierstra (ICML, 2016)

Meta-Learning with Memory-Augmented Neural Networks. - Adam Santoro; Sergey Bartunov; Matthew M. Botvinick; Daan Wierstra; Timothy P. Lillicrap (ICML, 2016)

Towards Conceptual Compression. - Karol Gregor; Frederic Besse; Danilo Jimenez Rezende; Ivo Danihelka; Daan Wierstra (NIPS, 2016)

Matching Networks for One Shot Learning. - Oriol Vinyals; Charles Blundell; Tim Lillicrap; Koray Kavukcuoglu; Daan Wierstra (NIPS, 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)

Continuous control with deep reinforcement learning. - Timothy P. Lillicrap; Jonathan J. Hunt; Alexander Pritzel; Nicolas Heess; Tom Erez; Yuval Tassa; David Silver; Daan Wierstra (ICLR (Poster), 2016)

One-shot Learning with Memory-Augmented Neural Networks. - Adam Santoro; Sergey Bartunov; Matthew M. Botvinick; Daan Wierstra; Timothy P. Lillicrap (CoRR, 2016)

Recurrent Environment Simulators. - Silvia Chiappa; Sébastien Racanière; Daan Wierstra; Shakir Mohamed (ICLR (Poster), 2017)

Variational Intrinsic Control. - Karol Gregor; Danilo Jimenez Rezende; Daan Wierstra (ICLR (Workshop), 2017)

Neural Episodic Control. - Alexander Pritzel; Benigno Uria; Sriram Srinivasan 005; Adrià Puigdomènech Badia; Oriol Vinyals; Demis Hassabis; Daan Wierstra; Charles Blundell (ICML, 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 001; Razvan Pascanu; Peter W. Battaglia; et al. (NIPS, 2017)

Comparison of Maximum Likelihood and GAN-based training of Real NVPs. - Ivo Danihelka; Balaji Lakshminarayanan; Benigno Uria; Daan Wierstra; Peter Dayan (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)

Learning model-based planning from scratch. - Razvan Pascanu; Yujia Li 001; Oriol Vinyals; Nicolas Heess; Lars Buesing; Sébastien Racanière; David P. Reichert; Theophane Weber; Daan Wierstra; Peter W. Battaglia (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 001; Razvan Pascanu; Peter W. Battaglia; et al. (CoRR, 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; et al. (CoRR, 2017)

Learning to Search with MCTSnets. - Arthur Guez; Theophane Weber; Ioannis Antonoglou; Karen Simonyan; Oriol Vinyals; Daan Wierstra; Rémi Munos; David Silver (ICML, 2018)

Relational recurrent neural networks. - Adam Santoro; Ryan Faulkner 001; David Raposo; Jack W. Rae; Mike Chrzanowski; Theophane Weber; Daan Wierstra; Oriol Vinyals; Razvan Pascanu; Timothy P. Lillicrap (NeurIPS, 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 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 001; Çaglar Gülçehre; H. Francis Song; et al. (CoRR, 2018)

Towards Interpretable Reinforcement Learning Using Attention Augmented Agents. - Alexander Mott; Daniel Zoran; Mike Chrzanowski; Daan Wierstra; Danilo Jimenez Rezende (NeurIPS, 2019)

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; et al. (CoRR, 2024)