Timothy Lillicrap

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All published works

Action	Title	Year	Authors
+ PDF Chat	Can foundation models actively gather information in interactive environments to test hypotheses?	2024	Nan Rosemary Ke Danny P. Sawyer Hubert Soyer Martin Engelcke David Reichert Drew A. Hudson John Reid Alexander Lerchner Danilo Jimenez Rezende Timothy Lillicrap
+ PDF Chat	AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents	2024	C. Rawles Sarah Clinckemaillie Yifan Chang Jonathan Waltz Gabrielle Lau Marybeth Fair Alice Li William Bishop Wei Li Folawiyo Campbell-Ajala
+ PDF Chat	Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving	2024	Aniket Didolkar Anirudh Goyal Nan Rosemary Ke Siyuan Guo Michal Vaľko Timothy Lillicrap Danilo Jimenez Rezende Yoshua Bengio Michael C. Mozer Sanjeev Arora
+ PDF Chat	Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning	2024	Yuxi Xie Anirudh Goyal Wenyue Zheng Min‐Yen Kan Timothy Lillicrap Kenji Kawaguchi Michael Shieh
+ PDF Chat	Mixture-of-Depths: Dynamically allocating compute in transformer-based language models	2024	David Raposo Sam Ritter Blake A. Richards Timothy Lillicrap Peter C. Humphreys Adam Santoro
+ PDF Chat	Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context	2024	Gemini Team Machel Reid Nikolay Savinov Denis Teplyashin Dmitry Lepikhin Timothy Lillicrap Jean-baptiste Alayrac Radu Soricut Angeliki Lazaridou
+	Mastering Diverse Domains through World Models	2023	Danijar Hafner Jurgis Pašukonis Jimmy Ba Timothy Lillicrap
+	Android in the Wild: A Large-Scale Dataset for Android Device Control	2023	C. Rawles Alice Li Daniel Rodríguez Rodríguez Oriana Riva Timothy Lillicrap
+	Gemini: A Family of Highly Capable Multimodal Models	2023	Gemini Team Rohan Anil Sebastian Borgeaud Jean-Baptiste Alayrac Jiahui Yu Radu Soricut Johan Schalkwyk Andrew M. Dai Anja Hauth Katie Millican
+	Equilibrium Aggregation: Encoding Sets via Optimization	2022	Sergey Bartunov Fabian B. Fuchs Timothy Lillicrap
+	Retrieval-Augmented Reinforcement Learning	2022	Anirudh Goyal Abram L. Friesen Andrea Banino Théophane Weber Nan Rosemary Ke Adrià Puigdomènech Badia Arthur Guez Mehdi Mirza Peter C. Humphreys Ksenia Konyushkova
+	A data-driven approach for learning to control computers	2022	Peter C. Humphreys David Raposo Toby Pohlen Gregory Thornton Rachita Chhaparia Alistair Muldal Josh Abramson Petko Georgiev Alex Goldin Adam Santoro
+	Intra-agent speech permits zero-shot task acquisition	2022	Yan Chen Federico Carnevale Petko Georgiev Adam Santoro Aurelia Guy Alistair Muldal Chia‐Chun Hung Josh Abramson Timothy Lillicrap Gregory Wayne
+	Evaluating Multimodal Interactive Agents	2022	Josh Abramson Arun Ahuja Federico Carnevale Petko Georgiev Alex Goldin Alden Hung Jessica Landon Timothy Lillicrap Alistair Muldal Blake A. Richards
+	Large-Scale Retrieval for Reinforcement Learning	2022	Peter C. Humphreys Arthur Guez Olivier Tieleman Laurent Sifre Théophane Weber Timothy Lillicrap
+	Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution	2022	Anthony M. Zador Blake A. Richards Bence P. Ölveczky G. Sean Escola Yoshua Bengio Kwabena Boahen Matthew Botvinick Dmitri B. Chklovskii Anne K. Churchland Claudia Clopath
+	Evaluating Long-Term Memory in 3D Mazes	2022	Jurgis Pašukonis Timothy Lillicrap Danijar Hafner
+	Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback	2022	Josh Abramson Arun Ahuja Federico Carnevale Petko Georgiev Alex Goldin Alden Hung Jessica Landon Jirka Lhotka Timothy Lillicrap Alistair Muldal
+ PDF Chat	Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning	2021	DeepMind Interactive Agents Team Josh Abramson Arun Ahuja Arthur Brussee Federico Carnevale Mary Cassin Felix Fischer Petko Georgiev Alex Goldin Tim Harley
+	Towards Biologically Plausible Convolutional Networks	2021	Roman Pogodin Yash Mehta Timothy Lillicrap Peter E. Latham
+	Symbolic Behaviour in Artificial Intelligence	2021	Adam Santoro Andrew K. Lampinen Kory W. Mathewson Timothy Lillicrap David Raposo
+	Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning	2021	DeepMind Interactive Agents Team Josh Abramson Arun Ahuja Arthur Brussee Federico Carnevale Mary Cassin Felix R. Fischer Petko Georgiev Alex Goldin Mansi Gupta
+	Symbolic Behaviour in Artificial Intelligence	2021	Adam Santoro Andrew K. Lampinen Kory W. Mathewson Timothy Lillicrap David Raposo
+	Towards Biologically Plausible Convolutional Networks	2021	Roman Pogodin Yash Mehta Timothy Lillicrap Peter E. Latham
+ PDF Chat	Mastering Atari, Go, chess and shogi by planning with a learned model	2020	Julian Schrittwieser Ioannis Antonoglou Thomas Hubert Karen Simonyan Laurent Sifre Simon Schmitt Arthur Guez Edward Lockhart Demis Hassabis Thore Graepel
+ PDF Chat	Imitating Interactive Intelligence	2020	Josh Abramson Arun Ahuja Iain Barr Arthur Brussee Federico Carnevale Mary Cassin Rachita Chhaparia Stephen R. L. Clark Bogdan Damoc Andrew Dudzik
+	Mastering Atari with Discrete World Models	2020	Danijar Hafner Timothy Lillicrap Mohammad Norouzi Jimmy Ba
+	Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban.	2020	Péter Karkus Mehdi Mirza Arthur Guez Andrew Jaegle Timothy Lillicrap Lars Buesing Nicolas Heess Théophane Weber
+ PDF Chat	dm_control: Software and tasks for continuous control	2020	Saran Tunyasuvunakool Alistair Muldal Yotam Doron Siqi Liu Steven Bohez Josh Merel Tom Erez Timothy Lillicrap Nicolas Heess Yuval Tassa
+	Physically Embedded Planning Problems: New Challenges for Reinforcement Learning	2020	Mehdi Mirza Andrew Jaegle Jonathan J. Hunt Arthur Guez Saran Tunyasuvunakool Alistair Muldal Théophane Weber Péter Karkus Sébastien Racanière Lars Buesing
+	Training Generative Adversarial Networks by Solving Ordinary Differential Equations	2020	Chongli Qin Yan Wu Jost Tobias Springenberg Andrew Brock Jeff Donahue Timothy Lillicrap Pushmeet Kohli
+	Imitating Interactive Intelligence	2020	Josh Abramson Arun Ahuja Iain Barr Arthur Brussee Federico Carnevale Mary Cassin Rachita Chhaparia Stephen Clark Bogdan Damoc Andrew Dudzik
+	Mastering Atari with Discrete World Models	2020	Danijar Hafner Timothy Lillicrap Mohammad Norouzi Jimmy Ba
+	Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban	2020	Péter Karkus Mehdi Mirza Arthur Guez Andrew Jaegle Timothy Lillicrap Lars Buesing Nicolas Heess Théophane Weber
+	Dream to Control: Learning Behaviors by Latent Imagination.	2019	Danijar Hafner Timothy Lillicrap Jimmy Ba Mohammad Norouzi
+ PDF Chat	Optimizing agent behavior over long time scales by transporting value	2019	Chia-Chun Hung Timothy Lillicrap Josh Abramson Yan Wu Mehdi Mirza Federico Carnevale Arun Ahuja Greg Wayne
+	Compressive Transformers for Long-Range Sequence Modelling	2019	Jack W. Rae Anna Potapenko Siddhant M. Jayakumar Timothy Lillicrap
+	What does it mean to understand a neural network	2019	Timothy Lillicrap Konrad P. Körding
+	Meta-Learning Neural Bloom Filters	2019	Jack W. Rae Sergey Bartunov Timothy Lillicrap
+	Using Weight Mirrors to Improve Feedback Alignment	2019	Mohamed Akrout Collin Wilson Peter C. Humphreys Timothy Lillicrap Douglas Tweed
+	An investigation of model-free planning	2019	Arthur Guez Mehdi Mirza Karol Gregor Rishabh Kabra Sébastien Racanière Théophane Weber David Raposo Adam Santoro Laurent Orseau Tom Eccles
+	Learning to Make Analogies by Contrasting Abstract Relational Structure	2019	Felix Hill Adam Santoro David G. T. Barrett Ari S. Morcos Timothy Lillicrap
+	Deep Learning without Weight Transport	2019	Mohamed Akrout Collin Wilson Peter C. Humphreys Timothy Lillicrap Douglas Tweed
+	Is coding a relevant metaphor for building AI? A commentary on "Is coding a relevant metaphor for the brain?", by Romain Brette	2019	Adam Santoro Felix Hill David G. T. Barrett David Raposo Matthew Botvinick Timothy Lillicrap
+	Deep Compressed Sensing	2019	Yan Wu Mihaela Rosca Timothy Lillicrap
+	Experience Replay for Continual Learning	2019	David Rolnick Arun Ahuja Jonathan Schwarz Timothy Lillicrap Gregory Wayne
+	Deep Learning without Weight Transport	2019	Mohamed Akrout Collin Wilson Peter C. Humphreys Timothy Lillicrap Douglas Tweed
+	Automated curricula through setter-solver interactions	2019	Sébastien Racanière Andrew K. Lampinen Adam Santoro David Reichert Vlad Firoiu Timothy Lillicrap
+	Meta-Learning Deep Energy-Based Memory Models	2019	Sergey Bartunov Jack W. Rae Simon Osindero Timothy Lillicrap
+	LOGAN: Latent Optimisation for Generative Adversarial Networks	2019	Yan Wu Jeff Donahue David Balduzzi Karen Simonyan Timothy Lillicrap
+	Dream to Control: Learning Behaviors by Latent Imagination	2019	Danijar Hafner Timothy Lillicrap Jimmy Ba Mohammad Norouzi
+	Compressive Transformers for Long-Range Sequence Modelling	2019	Jack W. Rae Anna Potapenko Siddhant M. Jayakumar Timothy Lillicrap
+	What does it mean to understand a neural network?	2019	Timothy Lillicrap Konrad P. Körding
+	Meta-Learning Neural Bloom Filters	2019	Jack W. Rae Sergey Bartunov Timothy Lillicrap
+	An investigation of model-free planning	2019	Arthur Guez Mehdi Mirza Karol Gregor Rishabh Kabra Sébastien Racanière Théophane Weber David Raposo Adam Santoro Laurent Orseau Tom Eccles
+	Entropic Policy Composition with Generalized Policy Improvement and Divergence Correction.	2018	Jonathan J. Hunt André Barreto Timothy Lillicrap Nicolas Heess
+	Composing Entropic Policies using Divergence Correction.	2018	Jonathan J. Hunt André Barreto Timothy Lillicrap Nicolas Heess
+	Learning Attractor Dynamics for Generative Memory	2018	Yan Wu Gregory Wayne Karol Gregor Timothy Lillicrap
+	Learning Latent Dynamics for Planning from Pixels	2018	Danijar Hafner Timothy Lillicrap Ian Fischer Ruben Villegas David Ha Honglak Lee James Davidson
+	Optimizing Agent Behavior over Long Time Scales by Transporting Value	2018	Chia-Chun Hung Timothy Lillicrap Josh Abramson Yan Wu Mehdi Mirza Federico Carnevale Arun Ahuja Greg Wayne
+	Episodic Curiosity through Reachability	2018	Nikolay Savinov Anton Raichuk Raphaël Marinier Damien Vincent Marc Pollefeys Timothy Lillicrap Sylvain Gelly
+	Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors	2018	Danijar Hafner Dustin Tran Alex Irpan Timothy Lillicrap James Davidson
+	Relational recurrent neural networks	2018	Adam Santoro Ryan Faulkner David Raposo Jack W. Rae Mike Chrzanowski Théophane Weber Daan Wierstra Oriol Vinyals Razvan Pascanu Timothy Lillicrap
+	The Kanerva Machine: A Generative Distributed Memory	2018	Yan Wu Greg Wayne Alex Graves Timothy Lillicrap
+	Recall Traces: Backtracking Models for Efficient Reinforcement Learning	2018	Anirudh Goyal Philémon Brakel William Fedus Soumye Singhal Timothy Lillicrap Sergey Levine Hugo Larochelle Yoshua Bengio
+	Fast Parametric Learning with Activation Memorization	2018	Jack W. Rae Chris Dyer Peter Dayan Timothy Lillicrap
+	Distributed Distributional Deterministic Policy Gradients	2018	Gabriel Barth-Maron Matthew W. Hoffman David Budden Will Dabney Dan Horgan Dhruva Tb Alistair Muldal Nicolas Heess Timothy Lillicrap
+	DeepMind Control Suite	2018	Yuval Tassa Yotam Doron Alistair Muldal Tom Erez Yazhe Li Diego de Las Casas David Budden Abbas Abdolmaleki Josh Merel Andrew Lefrancq
+	Unsupervised Predictive Memory in a Goal-Directed Agent	2018	Greg Wayne Chia-Chun Hung Amos David Mehdi Mirza Arun Ahuja Agnieszka Grabska‐Barwińska Jack W. Rae Piotr Mirowski Joel Z. Leibo Adam Santoro
+	Distributed Distributional Deterministic Policy Gradients	2018	Gabriel Barth-Maron Matthew W. Hoffman David Budden Will Dabney Dan Horgan Dhruva Tb Alistair Muldal Nicolas Heess Timothy Lillicrap
+	Relational Deep Reinforcement Learning	2018	Vinícius Zambaldi David Raposo Adam Santoro Victor Bapst Yujia Li I. Babuschkin Karl Tuyls David Reichert Timothy Lillicrap Edward Lockhart
+	Episodic Curiosity through Reachability	2018	Nikolay Savinov Anton Raichuk Raphaël Marinier Damien Vincent Marc Pollefeys Timothy Lillicrap Sylvain Gelly
+	Experience Replay for Continual Learning	2018	David Rolnick Arun Ahuja Jonathan Schwarz Timothy Lillicrap Greg Wayne
+	Learning Attractor Dynamics for Generative Memory	2018	Yue Wu Greg Wayne Karol Gregor Timothy Lillicrap
+	Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures	2018	Sergey Bartunov Adam Santoro Blake A. Richards Luke Marris Geoffrey E. Hinton Timothy Lillicrap
+	Measuring abstract reasoning in neural networks	2018	David G. T. Barrett Felix Hill Adam Santoro Ari S. Morcos Timothy Lillicrap
+	Noise Contrastive Priors for Functional Uncertainty	2018	Danijar Hafner Dustin Tran Timothy Lillicrap Alex Irpan James Davidson
+	The Kanerva Machine: A Generative Distributed Memory	2018	Yan Wu Greg Wayne Alex Graves Timothy Lillicrap
+	Relational recurrent neural networks	2018	Adam Santoro Ryan Faulkner David Raposo Jack W. Rae Mike Chrzanowski Théophane Weber Daan Wierstra Oriol Vinyals Razvan Pascanu Timothy Lillicrap
+	Recall Traces: Backtracking Models for Efficient Reinforcement Learning	2018	Anirudh Goyal Philémon Brakel William Fedus Soumye Singhal Timothy Lillicrap Sergey Levine Hugo Larochelle Yoshua Bengio
+	Composing Entropic Policies using Divergence Correction	2018	Jonathan J. Hunt André Sales Barreto Timothy Lillicrap Nicolas Heess
+	Learning Latent Dynamics for Planning from Pixels	2018	Danijar Hafner Timothy Lillicrap Ian Fischer Ruben Villegas David Ha Honglak Lee James Davidson
+	Optimizing Agent Behavior over Long Time Scales by Transporting Value	2018	Chia‐Chun Hung Timothy Lillicrap Josh Abramson Yan Wu Mehdi Mirza Federico Carnevale Arun Ahuja Greg Wayne
+	Fast Parametric Learning with Activation Memorization	2018	Jack W. Rae Chris Dyer Peter Dayan Timothy Lillicrap
+	Towards deep learning with segregated dendrites	2017	Jordan Guerguiev Timothy Lillicrap Blake A. Richards
+	A simple neural network module for relational reasoning	2017	Adam Santoro David Raposo David G. T. Barrett Mateusz Malinowski Razvan Pascanu Peter Battaglia Timothy Lillicrap
+ PDF Chat	Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates	2017	Shixiang Gu Ethan Holly Timothy Lillicrap Sergey Levine
+	Data-efficient Deep Reinforcement Learning for Dexterous Manipulation	2017	Ivaylo Popov Nicolas Heess Timothy Lillicrap Roland Hafner Gabriel Barth-Maron Matej Vecerík Thomas Lampe Yuval Tassa Tom Erez Martin Riedmiller
+	Discovering objects and their relations from entangled scene representations	2017	David Raposo Adam Santoro David G. T. Barrett Razvan Pascanu Timothy Lillicrap Peter Battaglia
+	Q-PrOP: Sample-efficient policy gradient with an off-policy critic	2017	Shixiang Gu Timothy Lillicrap Zoubin Ghahramani Richard E. Turner Sergey Levine
+	Generative Temporal Models with Memory	2017	Mevlana Gemici Chia-Chun Hung Adam Santoro Greg Wayne Shakir Mohamed Danilo Jimenez Rezende Amos David Timothy Lillicrap
+	StarCraft II: A New Challenge for Reinforcement Learning	2017	Oriol Vinyals Timo Ewalds Sergey Bartunov Petko Georgiev Alexander Sasha Vezhnevets Michelle Yeo Alireza Makhzani Heinrich Küttler John Agapiou Julian Schrittwieser
+	Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm	2017	David Silver Thomas Hubert Julian Schrittwieser Ioannis Antonoglou Matthew Lai Arthur Guez Marc Lanctot Laurent Sifre Dharshan Kumaran Thore Graepel
+	Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning	2017	Shixiang Gu Timothy Lillicrap Zoubin Ghahramani Richard E. Turner Bernhard Schölkopf Sergey Levine
+	A simple neural network module for relational reasoning	2017	Adam Santoro David Raposo David G. T. Barrett Mateusz Malinowski Razvan Pascanu Peter Battaglia Timothy Lillicrap
+	Data-efficient Deep Reinforcement Learning for Dexterous Manipulation	2017	Ivaylo Popov Nicolas Heess Timothy Lillicrap Roland Hafner Gabriel Barth-Maron Matej Vecerík Thomas Lampe Yuval Tassa Tom Erez Martin Riedmiller
+	Discovering objects and their relations from entangled scene representations	2017	David Raposo Adam Santoro David M. Barrett Razvan Pascanu Timothy Lillicrap Peter Battaglia
+	Matching networks for one shot learning	2016	Oriol Vinyals Charles Blundell Timothy Lillicrap Koray Kavukcuoglu Daan Wierstra
+	Learning to Learn for Global Optimization of Black Box Functions.	2016	Yutian Chen Matthew W. Hoffman Sergio Gómez Colmenarejo Misha Denil Timothy Lillicrap Nando de Freitas
+	Learning to Learn without Gradient Descent by Gradient Descent	2016	Yutian Chen Matthew W. Hoffman Sergio Gómez Colmenarejo Misha Denil Timothy Lillicrap Matt Botvinick Nando de Freitas
+	Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes	2016	Jack W. Rae Jonathan J. Hunt Tim Harley Ivo Danihelka Andrew Senior Greg Wayne Alex Graves Timothy Lillicrap
+	Learning and Transfer of Modulated Locomotor Controllers	2016	Nicolas Heess Gregory Wayne Yuval Tassa Timothy Lillicrap Martin Riedmiller David Silver
+	Biologically feasible deep learning with segregated dendrites	2016	Jordan Guergiuev Timothy Lillicrap Blake A. Richards
+	Towards deep learning with segregated dendrites	2016	Jordan Guergiuev Timothy Lillicrap Blake A. Richards
+	Deep learning with segregated dendrites	2016	Jordan Guergiuev Timothy Lillicrap Blake A. Richards
+	Continuous control with deep reinforcement learning	2016	Timothy Lillicrap Jonathan J. Hunt Alexander Pritzel Nicolas Heess Tom Erez Yuval Tassa David Silver Daan Wierstra
+	Matching Networks for One Shot Learning	2016	Oriol Vinyals Charles Blundell Timothy Lillicrap Koray Kavukcuoglu Daan Wierstra
+	One-shot Learning with Memory-Augmented Neural Networks	2016	Adam Santoro Sergey Bartunov Matthew Botvinick Daan Wierstra Timothy Lillicrap
+	Asynchronous Methods for Deep Reinforcement Learning	2016	Volodymyr Mnih Adrià Puigdomènech Badia Mehdi Mirza Alex Graves Timothy Lillicrap Tim Harley David Silver Koray Kavukcuoglu
+	Learning to Learn without Gradient Descent by Gradient Descent	2016	Yutian Chen Matthew W. Hoffman Sergio Gómez Colmenarejo Misha Denil Timothy Lillicrap Matt Botvinick Nando de Freitas
+	Continuous Deep Q-Learning with Model-based Acceleration	2016	Shixiang Gu Timothy Lillicrap Ilya Sutskever Sergey Levine
+	Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic	2016	Shixiang Gu Timothy Lillicrap Zoubin Ghahramani Richard E. Turner Sergey Levine
+	Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates	2016	Shixiang Gu Ethan Holly Timothy Lillicrap Sergey Levine
+	Asynchronous Methods for Deep Reinforcement Learning	2016	Volodymyr Mnih Adrià Puigdomènech Badia Mehdi Mirza Alex Graves Tim Harley Timothy Lillicrap David Silver Koray Kavukcuoglu
+	One-shot Learning with Memory-Augmented Neural Networks	2016	Adam Santoro Sergey Bartunov Matthew Botvinick Daan Wierstra Timothy Lillicrap
+	Learning and Transfer of Modulated Locomotor Controllers	2016	Nicolas Heess Greg Wayne Yuval Tassa Timothy Lillicrap Martin Riedmiller David Silver
+	Matching Networks for One Shot Learning	2016	Oriol Vinyals Charles Blundell Timothy Lillicrap Koray Kavukcuoglu Daan Wierstra
+	Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes	2016	Jack W. Rae Jonathan J. Hunt Tim Harley Ivo Danihelka Andrew Senior Greg Wayne Alex Graves Timothy Lillicrap
+	Towards deep learning with segregated dendrites	2016	Jordan Guergiuev Timothy Lillicrap Blake A. Richards
+	Learning continuous control policies by stochastic value gradients	2015	Nicolas Heess Greg Wayne David Silver Timothy Lillicrap Yuval Tassa Tom Erez
+	Learning Continuous Control Policies by Stochastic Value Gradients	2015	Nicolas Heess Greg Wayne David Silver Timothy Lillicrap Yuval Tassa Tom Erez
+	Continuous control with deep reinforcement learning	2015	Timothy Lillicrap Jonathan J. Hunt Alexander Pritzel Nicolas Heess Tom Erez Yuval Tassa David Silver Daan Wierstra
+	Towards Principled Unsupervised Learning	2015	Ilya Sutskever Rafał Józefowicz Karol Gregor Danilo Jimenez Rezende Timothy Lillicrap Oriol Vinyals
+	Memory-based control with recurrent neural networks	2015	Nicolas Heess Jonathan J. Hunt Timothy Lillicrap David Silver
+	Deep Reinforcement Learning in Large Discrete Action Spaces	2015	Gabriel Dulac-Arnold Richard Evans Hado van Hasselt Peter Sunehag Timothy Lillicrap Jonathan J. Hunt Timothy Mann Théophane Weber Thomas Degris Ben Coppin
+	Random feedback weights support learning in deep neural networks	2014	Timothy Lillicrap Daniel Cownden Douglas Tweed Colin J. Akerman
+	Random feedback weights support learning in deep neural networks	2014	Timothy Lillicrap Daniel Cownden Douglas Tweed Colin J. Akerman

Common Coauthors

Coauthor	Papers Together
Adam Santoro	29
Greg Wayne	22
Nicolas Heess	19
David Silver	16
Arun Ahuja	14
Jack W. Rae	14
David Raposo	14
Alistair Muldal	13
Josh Abramson	12
Petko Georgiev	11
Arthur Guez	11
Federico Carnevale	10
Yuval Tassa	10
Danijar Hafner	10
Théophane Weber	10
Oriol Vinyals	10
Jonathan J. Hunt	10
Tim Harley	9
Daan Wierstra	9
Peter C. Humphreys	9
Blake A. Richards	9
Sergey Levine	8
Mehdi Mirza	8
Tom Erez	8
Jessica Landon	8
Koray Kavukcuoglu	8
Sergey Bartunov	8
Razvan Pascanu	7
Alex Goldin	7
Felix Hill	7
David G. T. Barrett	6
Nikolay Savinov	6
Alex Graves	6
Tamara von Glehn	6
Nathaniel Wong	6
Shixiang Gu	6
Alden Hung	6
Anirudh Goyal	6
Matthew Botvinick	6
Douglas Tweed	5
Laurent Sifre	5
Yan Wu	5
Martin Riedmiller	5
Matthew W. Hoffman	5
Peter Battaglia	5
Demis Hassabis	5
Julian Schrittwieser	5
Adrià Puigdomènech Badia	5
Jimmy Ba	5
Karol Gregor	5

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Asynchronous Methods for Deep Reinforcement Learning	2016	Volodymyr Mnih Adrià Puigdomènech Badia Mehdi Mirza Alex Graves Tim Harley Timothy Lillicrap David Silver Koray Kavukcuoglu	16
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	13
+	Continuous control with deep reinforcement learning	2015	Timothy Lillicrap Jonathan J. Hunt Alexander Pritzel Nicolas Heess Tom Erez Yuval Tassa David Silver Daan Wierstra	12
+	Adam: A Method for Stochastic Optimization	2014	Diederik P. Kingma Jimmy Ba	12
+	Trust Region Policy Optimization	2015	John Schulman Sergey Levine Philipp Moritz Michael I. Jordan Pieter Abbeel	11
+	Imagination-Augmented Agents for Deep Reinforcement Learning	2017	Sébastien Racanière Théophane Weber David Reichert Lars Buesing Arthur Guez Danilo Jimenez Rezende Adrià Puigdomènech Badia Oriol Vinyals Nicolas Heess Yujia Li	10
+	Neural Machine Translation by Jointly Learning to Align and Translate	2015	Dzmitry Bahdanau Kyunghyun Cho Yoshua Bengio	9
+	Stochastic Backpropagation and Approximate Inference in Deep Generative Models	2014	Danilo Jimenez Rezende Shakir Mohamed Daan Wierstra	9
+	Neural Turing Machines	2014	Alex Graves Greg Wayne Ivo Danihelka	9
+	Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift	2015	Sergey Ioffe Christian Szegedy	9
+ PDF Chat	The Arcade Learning Environment: An Evaluation Platform for General Agents	2013	Marc G. Bellemare Yavar Naddaf Joel Veness Michael Bowling	9
+	IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures	2018	Lasse Espeholt Hubert Soyer Rémi Munos Karen Simonyan Volodymir Mnih Tom Ward Yotam Doron Vlad Firoiu Tim Harley Iain Dunning	8
+	High-Dimensional Continuous Control Using Generalized Advantage Estimation	2015	John Schulman Philipp Moritz Sergey Levine Michael I. Jordan Pieter Abbeel	8
+	Proximal Policy Optimization Algorithms	2017	John Schulman Filip Wolski Prafulla Dhariwal Alec Radford Oleg Klimov	7
+	Learning and Querying Fast Generative Models for Reinforcement Learning	2018	Lars Buesing Théophane Weber Sébastien Racanière S. M. Ali Eslami Danilo Jimenez Rezende David Reichert Fabio Viola Frederic Besse Karol Gregor Demis Hassabis	7
+	Value Prediction Network	2017	Junhyuk Oh Satinder Singh Honglak Lee	7
+	End-to-End Training of Deep Visuomotor Policies	2015	Sergey Levine Chelsea Finn Trevor Darrell Pieter Abbeel	7
+	Learning continuous control policies by stochastic value gradients	2015	Nicolas Heess Greg Wayne David Silver Timothy Lillicrap Yuval Tassa Tom Erez	6
+	Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)	2015	Djork-Arné Clevert Thomas Unterthiner Sepp Hochreiter	6
+	Unsupervised Predictive Memory in a Goal-Directed Agent	2018	Greg Wayne Chia-Chun Hung Amos David Mehdi Mirza Arun Ahuja Agnieszka Grabska‐Barwińska Jack W. Rae Piotr Mirowski Joel Z. Leibo Adam Santoro	6
+	Prioritized Experience Replay	2015	Tom Schaul John Quan Ioannis Antonoglou David Silver	6
+	Universal Planning Networks	2018	Aravind Srinivas Allan Jabri Pieter Abbeel Sergey Levine Chelsea Finn	5
+	Model-Based Planning with Discrete and Continuous Actions	2017	Mikael Henaff WILLIAM F. WHITNEY Yann LeCun	5
+	Model-Ensemble Trust-Region Policy Optimization	2018	Thanard Kurutach Ignasi Clavera Yan Duan Aviv Tamar Pieter Abbeel	5
+	Distributed Prioritized Experience Replay	2018	Dan Horgan John Quan David Budden Gabriel Barth-Maron Matteo Hessel Hado van Hasselt David Silver	5
+	DeepMind Control Suite	2018	Yuval Tassa Yotam Doron Alistair Muldal Tom Erez Yazhe Li Diego de Las Casas David Budden Abbas Abdolmaleki Josh Merel Andrew Lefrancq	5
+	Self-Supervised Visual Planning with Temporal Skip Connections	2017	Frederik Ebert Chelsea Finn Alex X. Lee Sergey Levine	5
+	Continuous control with deep reinforcement learning	2016	Timothy Lillicrap Jonathan J. Hunt Alexander Pritzel Nicolas Heess Tom Erez Yuval Tassa David Silver Daan Wierstra	5
+	Reinforcement Learning with Unsupervised Auxiliary Tasks	2016	Max Jaderberg Volodymyr Mnih Wojciech Marian Czarnecki Tom Schaul Joel Z. Leibo David Silver Koray Kavukcuoglu	5
+	Generative Temporal Models with Memory	2017	Mevlana Gemici Chia-Chun Hung Adam Santoro Greg Wayne Shakir Mohamed Danilo Jimenez Rezende Amos David Timothy Lillicrap	5
+ PDF Chat	Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification	2015	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	5
+	Data-efficient Deep Reinforcement Learning for Dexterous Manipulation	2017	Ivaylo Popov Nicolas Heess Timothy Lillicrap Roland Hafner Gabriel Barth-Maron Matej Vecerík Thomas Lampe Yuval Tassa Tom Erez Martin Riedmiller	5
+	Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models	2018	Kurtland Chua Roberto Calandra Rowan McAllister Sergey Levine	5
+	Prioritized Experience Replay	2015	Tom Schaul John Quan Ioannis Antonoglou David Silver	4
+	Compatible Value Gradients for Reinforcement Learning of Continuous Deep Policies	2015	David Balduzzi Muhammad Ghifary	4
+	Rainbow: Combining Improvements in Deep Reinforcement Learning	2017	Matteo Hessel Joseph Modayil Hado van Hasselt Tom Schaul Georg Ostrovski Will Dabney Daniel Horgan Bilal Piot Mohammad Gheshlaghi Azar David Silver	4
+ PDF Chat	Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents	2018	Marlos C. Machado Marc G. Bellemare Erik Talvitie Joel Veness Matthew Hausknecht Michael Bowling	4
+ PDF Chat	Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning	2018	Anusha Nagabandi Gregory Kahn Ronald S. Fearing Sergey Levine	4
+	Learning to learn by gradient descent by gradient descent	2016	Marcin Andrychowicz Misha Denil Sergio Luis Suárez Gómez Matthew W. Hoffman David Pfau Tom Schaul Brendan Shillingford Nando de Freitas	4
+	End-To-End Memory Networks	2015	Sainbayar Sukhbaatar Arthur Szlam Jason Weston Rob Fergus	4
+	Learning Continuous Control Policies by Stochastic Value Gradients	2015	Nicolas Heess Greg Wayne David Silver Timothy Lillicrap Yuval Tassa Tom Erez	4
+	Emergence of Locomotion Behaviours in Rich Environments	2017	Nicolas Heess Dhruva Tb Sriram Srinivasan Jay Lemmon Josh Merel Greg Wayne Yuval Tassa Tom Erez Ziyu Wang S. M. Ali Eslami	4
+	Deep Variational Reinforcement Learning for POMDPs	2018	Maximilian Igl Luisa Zintgraf Tuan Anh Le Frank Wood Shimon Whiteson	4
+	Recurrent Environment Simulators	2017	Silvia Chiappa Sébastien Racanière Daan Wierstra Shakir Mohamed	4
+ PDF Chat	ImageNet Large Scale Visual Recognition Challenge	2015	Olga Russakovsky Jia Deng Hao Su Jonathan Krause Sanjeev Satheesh Sean Ma Zhiheng Huang Andrej Karpathy Aditya Khosla Michael S. Bernstein	4
+	Sequence to Sequence Learning with Neural Networks	2014	Ilya Sutskever Oriol Vinyals Quoc V. Le	4
+	Model-Based Reinforcement Learning for Atari	2019	Łukasz Kaiser Mohammad Babaeizadeh Piotr Miłoś Błażej Osiński Roy H. Campbell Konrad Czechowski Dumitru Erhan Chelsea Finn Piotr Kozakowski Sergey Levine	4
+	Semi-Supervised Classification with Graph Convolutional Networks	2016	Thomas Kipf Max Welling	4
+	Representation Learning with Contrastive Predictive Coding	2018	Aäron van den Oord Yazhe Li Oriol Vinyals	4
+	SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning	2018	Marvin Zhang Sharad Vikram Laura Smith Pieter Abbeel Matthew Johnson Sergey Levine	4