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ORCIDTimothy P. Lillicrap
Tim Lillicrap
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- affiliation: Google DeepMind, London, UK
- affiliation: Queen's University, Kingston, Centre for Neuroscience Studies
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Journal Articles
- 2022
[j10]Blake A. Richards, Timothy P. Lillicrap:
The Brain-Computer Metaphor Debate Is Useless: A Matter of Semantics. Frontiers Comput. Sci. 4: 810358 (2022)- 2020
- [j9]Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy P. Lillicrap, David Silver
:
Mastering Atari, Go, chess and shogi by planning with a learned model. Nat. 588(7839): 604-609 (2020) - [j8]Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy P. Lillicrap, Nicolas Heess, Yuval Tassa:
dm_control: Software and tasks for continuous control. Softw. Impacts 6: 100022 (2020) - 2019
- [j7]Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, Junhyuk Oh, Dan Horgan, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander Sasha Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom Le Paine, Çaglar Gülçehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy P. Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, David Silver:
Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nat. 575(7782): 350-354 (2019) - 2018
- [j6]Andrea Banino, Caswell Barry, Benigno Uria, Charles Blundell, Timothy P. Lillicrap, Piotr Mirowski, Alexander Pritzel, Martin J. Chadwick, Thomas Degris, Joseph Modayil, Greg Wayne, Hubert Soyer, Fabio Viola, Brian Zhang, Ross Goroshin, Neil C. Rabinowitz, Razvan Pascanu, Charlie Beattie, Stig Petersen, Amir Sadik, Stephen Gaffney, Helen King, Koray Kavukcuoglu, Demis Hassabis, Raia Hadsell, Dharshan Kumaran:
Vector-based navigation using grid-like representations in artificial agents. Nat. 557(7705): 429-433 (2018) - 2017
- [j5]David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy P. Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, Demis Hassabis:
Mastering the game of Go without human knowledge. Nat. 550(7676): 354-359 (2017) - [j4]Arash Samadi, Timothy P. Lillicrap, Douglas B. Tweed:
Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights. Neural Comput. 29(3): 578-602 (2017) - 2016
- [j3]David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis:
Mastering the game of Go with deep neural networks and tree search. Nat. 529(7587): 484-489 (2016) - 2012
- [j2]John Vervaeke, Timothy P. Lillicrap, Blake A. Richards:
Relevance Realization and the Emerging Framework in Cognitive Science. J. Log. Comput. 22(1): 79-99 (2012) - 2008
- [j1]M. N. Abdelghani, Timothy P. Lillicrap, Douglas B. Tweed:
Sensitivity Derivatives for Flexible Sensorimotor Learning. Neural Comput. 20(8): 2085-2111 (2008)
Conference and Workshop Papers
- 2023
- [c49]Jurgis Pasukonis, Timothy P. Lillicrap, Danijar Hafner:
Evaluating Long-Term Memory in 3D Mazes. ICLR 2023 - [c48]Christopher Rawles, Alice Li, Daniel Rodriguez, Oriana Riva, Timothy P. Lillicrap:
AndroidInTheWild: A Large-Scale Dataset For Android Device Control. NeurIPS 2023 - 2022
- [c47]Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Peter C. Humphreys, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell:
Retrieval-Augmented Reinforcement Learning. ICML 2022: 7740-7765 - [c46]Peter C. Humphreys, David Raposo, Tobias Pohlen, Gregory Thornton, Rachita Chhaparia, Alistair Muldal, Josh Abramson, Petko Georgiev, Adam Santoro, Timothy P. Lillicrap:
A data-driven approach for learning to control computers. ICML 2022: 9466-9482 - [c45]Naoki Hiratani, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham:
On the Stability and Scalability of Node Perturbation Learning. NeurIPS 2022 - [c44]Peter C. Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Theophane Weber, Timothy P. Lillicrap:
Large-Scale Retrieval for Reinforcement Learning. NeurIPS 2022 - [c43]Chen Yan, Federico Carnevale, Petko Georgiev, Adam Santoro, Aurelia Guy, Alistair Muldal, Chia-Chun Hung, Josh Abramson, Timothy P. Lillicrap, Gregory Wayne:
Intra-agent speech permits zero-shot task acquisition. NeurIPS 2022 - [c42]Sergey Bartunov, Fabian B. Fuchs, Timothy P. Lillicrap:
Equilibrium aggregation: encoding sets via optimization. UAI 2022: 139-149 - 2021
- [c41]Danijar Hafner, Timothy P. Lillicrap, Mohammad Norouzi, Jimmy Ba:
Mastering Atari with Discrete World Models. ICLR 2021 - [c40]Roman Pogodin, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham:
Towards Biologically Plausible Convolutional Networks. NeurIPS 2021: 13924-13936 - [c39]Shahab Bakhtiari, Patrick J. Mineault, Timothy P. Lillicrap, Christopher C. Pack, Blake A. Richards:
The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning. NeurIPS 2021: 25164-25178 - 2020
- [c38]Sergey Bartunov, Jack W. Rae, Simon Osindero, Timothy P. Lillicrap:
Meta-Learning Deep Energy-Based Memory Models. ICLR 2020 - [c37]Danijar Hafner, Timothy P. Lillicrap, Jimmy Ba, Mohammad Norouzi:
Dream to Control: Learning Behaviors by Latent Imagination. ICLR 2020 - [c36]Sébastien Racanière, Andrew K. Lampinen, Adam Santoro, David P. Reichert, Vlad Firoiu, Timothy P. Lillicrap:
Automated curriculum generation through setter-solver interactions. ICLR 2020 - [c35]Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Chloe Hillier, Timothy P. Lillicrap:
Compressive Transformers for Long-Range Sequence Modelling. ICLR 2020 - [c34]Basile Confavreux, Friedemann Zenke, Everton J. Agnes, Timothy P. Lillicrap, Tim P. Vogels:
A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. NeurIPS 2020 - [c33]Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andy Brock, Jeff Donahue, Timothy P. Lillicrap, Pushmeet Kohli:
Training Generative Adversarial Networks by Solving Ordinary Differential Equations. NeurIPS 2020 - 2019
- [c32]Anirudh Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy P. Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio:
Recall Traces: Backtracking Models for Efficient Reinforcement Learning. ICLR (Poster) 2019 - [c31]Felix Hill, Adam Santoro, David G. T. Barrett, Ari S. Morcos, Timothy P. Lillicrap:
Learning to Make Analogies by Contrasting Abstract Relational Structure. ICLR (Poster) 2019 - [c30]Nikolay Savinov, Anton Raichuk, Damien Vincent, Raphaël Marinier, Marc Pollefeys, Timothy P. Lillicrap, Sylvain Gelly:
Episodic Curiosity through Reachability. ICLR (Poster) 2019 - [c29]Vinícius Flores Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David P. Reichert, Timothy P. Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew M. Botvinick, Oriol Vinyals, Peter W. Battaglia:
Deep reinforcement learning with relational inductive biases. ICLR (Poster) 2019 - [c28]Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Theophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy P. Lillicrap:
An Investigation of Model-Free Planning. ICML 2019: 2464-2473 - [c27]Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson:
Learning Latent Dynamics for Planning from Pixels. ICML 2019: 2555-2565 - [c26]Jonathan J. Hunt, André Barreto, Timothy P. Lillicrap, Nicolas Heess:
Composing Entropic Policies using Divergence Correction. ICML 2019: 2911-2920 - [c25]Jack W. Rae, Sergey Bartunov, Timothy P. Lillicrap:
Meta-Learning Neural Bloom Filters. ICML 2019: 5271-5280 - [c24]Yan Wu, Mihaela Rosca, Timothy P. Lillicrap:
Deep Compressed Sensing. ICML 2019: 6850-6860 - [c23]David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Gregory Wayne:
Experience Replay for Continual Learning. NeurIPS 2019: 348-358 - [c22]Mohamed Akrout, Collin Wilson, Peter C. Humphreys, Timothy P. Lillicrap, Douglas B. Tweed:
Deep Learning without Weight Transport. NeurIPS 2019: 974-982 - [c21]Danijar Hafner, Dustin Tran, Timothy P. Lillicrap, Alex Irpan, James Davidson:
Noise Contrastive Priors for Functional Uncertainty. UAI 2019: 905-914 - 2018
- [c20]Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy P. Lillicrap:
Distributed Distributional Deterministic Policy Gradients. ICLR (Poster) 2018 - [c19]Yan Wu, Greg Wayne, Alex Graves, Timothy P. Lillicrap:
The Kanerva Machine: A Generative Distributed Memory. ICLR (Poster) 2018 - [c18]Jack W. Rae, Chris Dyer, Peter Dayan, Timothy P. Lillicrap:
Fast Parametric Learning with Activation Memorization. ICML 2018: 4225-4234 - [c17]Adam Santoro, Felix Hill, David G. T. Barrett, Ari S. Morcos, Timothy P. Lillicrap:
Measuring abstract reasoning in neural networks. ICML 2018: 4477-4486 - [c16]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 - [c15]Sergey Bartunov, Adam Santoro, Blake A. Richards, Luke Marris, Geoffrey E. Hinton, Timothy P. Lillicrap:
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures. NeurIPS 2018: 9390-9400 - [c14]Yan Wu, Gregory Wayne, Karol Gregor, Timothy P. Lillicrap:
Learning Attractor Dynamics for Generative Memory. NeurIPS 2018: 9401-9410 - 2017
- [c13]Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine:
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. ICLR 2017 - [c12]David Raposo, Adam Santoro, David G. T. Barrett, Razvan Pascanu, Tim Lillicrap, Peter W. Battaglia:
Discovering objects and their relations from entangled scene representations. ICLR (Workshop) 2017 - [c11]Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matthew M. Botvinick, Nando de Freitas:
Learning to Learn without Gradient Descent by Gradient Descent. ICML 2017: 748-756 - [c10]Shixiang Gu, Ethan Holly, Timothy P. Lillicrap, Sergey Levine:
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. ICRA 2017: 3389-3396 - [c9]Shixiang Gu, Tim Lillicrap, Richard E. Turner, Zoubin Ghahramani, Bernhard Schölkopf, Sergey Levine:
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. NIPS 2017: 3846-3855 - [c8]Adam Santoro, David Raposo, David G. T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter W. Battaglia, Tim Lillicrap:
A simple neural network module for relational reasoning. NIPS 2017: 4967-4976 - 2016
- [c7]Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, Timothy P. Lillicrap:
Meta-Learning with Memory-Augmented Neural Networks. ICML 2016: 1842-1850 - [c6]Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu:
Asynchronous Methods for Deep Reinforcement Learning. ICML 2016: 1928-1937 - [c5]Shixiang Gu, Timothy P. Lillicrap, Ilya Sutskever, Sergey Levine:
Continuous Deep Q-Learning with Model-based Acceleration. ICML 2016: 2829-2838 - [c4]Jack W. Rae, Jonathan J. Hunt, Ivo Danihelka, Timothy Harley, Andrew W. Senior, Gregory Wayne, Alex Graves, Tim Lillicrap:
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes. NIPS 2016: 3621-3629 - [c3]Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, Daan Wierstra:
Matching Networks for One Shot Learning. NIPS 2016: 3630-3638 - [c2]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 - 2015
- [c1]Nicolas Heess, Gregory Wayne, David Silver, Timothy P. Lillicrap, Tom Erez, Yuval Tassa:
Learning Continuous Control Policies by Stochastic Value Gradients. NIPS 2015: 2944-2952
Informal and Other Publications
- 2024
- [i73]Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy P. Lillicrap, Jean-Baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, Ioannis Antonoglou, Rohan Anil, Sebastian Borgeaud, Andrew M. Dai, Katie Millican, Ethan Dyer, Mia Glaese, Thibault Sottiaux, Benjamin Lee, Fabio Viola, Malcolm Reynolds, Yuanzhong Xu, James Molloy, Jilin Chen, Michael Isard, Paul Barham, Tom Hennigan, Ross McIlroy, Melvin Johnson, Johan Schalkwyk, Eli Collins, Eliza Rutherford, Erica Moreira, Kareem Ayoub, Megha Goel, Clemens Meyer, Gregory Thornton, Zhen Yang, Henryk Michalewski, Zaheer Abbas, Nathan Schucher, Ankesh Anand, Richard Ives, James Keeling, Karel Lenc, Salem Haykal, Siamak Shakeri, Pranav Shyam, Aakanksha Chowdhery, Roman Ring, Stephen Spencer, Eren Sezener, et al.:
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. CoRR abs/2403.05530 (2024) - 2023
- [i72]Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy P. Lillicrap:
Mastering Diverse Domains through World Models. CoRR abs/2301.04104 (2023) - [i71]Christopher Rawles, Alice Li, Daniel Rodriguez, Oriana Riva, Timothy P. Lillicrap:
Android in the Wild: A Large-Scale Dataset for Android Device Control. CoRR abs/2307.10088 (2023) - [i70]Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M. Dai, Anja Hauth, Katie Millican, David Silver, Slav Petrov, Melvin Johnson, Ioannis Antonoglou, Julian Schrittwieser, Amelia Glaese, Jilin Chen, Emily Pitler, Timothy P. Lillicrap, Angeliki Lazaridou, Orhan Firat, James Molloy, Michael Isard, Paul Ronald Barham, Tom Hennigan, Benjamin Lee, Fabio Viola, Malcolm Reynolds, Yuanzhong Xu, Ryan Doherty, Eli Collins, Clemens Meyer, Eliza Rutherford, Erica Moreira, Kareem Ayoub, Megha Goel, George Tucker, Enrique Piqueras, Maxim Krikun, Iain Barr, Nikolay Savinov, Ivo Danihelka, Becca Roelofs, Anaïs White, Anders Andreassen, Tamara von Glehn, Lakshman Yagati, Mehran Kazemi, Lucas Gonzalez, Misha Khalman, Jakub Sygnowski, et al.:
Gemini: A Family of Highly Capable Multimodal Models. CoRR abs/2312.11805 (2023) - 2022
- [i69]Peter C. Humphreys, David Raposo, Toby Pohlen, Gregory Thornton, Rachita Chhaparia, Alistair Muldal, Josh Abramson, Petko Georgiev, Alex Goldin, Adam Santoro, Timothy P. Lillicrap:
A data-driven approach for learning to control computers. CoRR abs/2202.08137 (2022) - [i68]Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell:
Retrieval-Augmented Reinforcement Learning. CoRR abs/2202.08417 (2022) - [i67]Sergey Bartunov, Fabian B. Fuchs, Timothy P. Lillicrap:
Equilibrium Aggregation: Encoding Sets via Optimization. CoRR abs/2202.12795 (2022) - [i66]Josh Abramson, Arun Ahuja, Federico Carnevale, Petko Georgiev, Alex Goldin, Alden Hung, Jessica Landon, Timothy P. Lillicrap, Alistair Muldal, Blake A. Richards, Adam Santoro, Tamara von Glehn, Greg Wayne, Nathaniel Wong, Chen Yan:
Evaluating Multimodal Interactive Agents. CoRR abs/2205.13274 (2022) - [i65]Chen Yan, Federico Carnevale, Petko Georgiev, Adam Santoro, Aurelia Guy, Alistair Muldal, Chia-Chun Hung, Josh Abramson, Timothy P. Lillicrap, Gregory Wayne:
Intra-agent speech permits zero-shot task acquisition. CoRR abs/2206.03139 (2022) - [i64]Peter C. Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Théophane Weber, Timothy P. Lillicrap:
Large-Scale Retrieval for Reinforcement Learning. CoRR abs/2206.05314 (2022) - [i63]Anthony Zador, Blake A. Richards, Bence Ölveczky, Sean Escola, Yoshua Bengio, Kwabena Boahen, Matthew M. Botvinick, Dmitri B. Chklovskii, Anne Churchland, Claudia Clopath, James DiCarlo, Surya Ganguli, Jeff Hawkins, Konrad P. Körding, Alexei A. Koulakov, Yann LeCun, Timothy P. Lillicrap, Adam H. Marblestone, Bruno A. Olshausen, Alexandre Pouget, Cristina Savin, Terrence J. Sejnowski, Eero P. Simoncelli, Sara A. Solla, David Sussillo, Andreas S. Tolias, Doris Tsao:
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution. CoRR abs/2210.08340 (2022) - [i62]Jurgis Pasukonis, Timothy P. Lillicrap, Danijar Hafner:
Evaluating Long-Term Memory in 3D Mazes. CoRR abs/2210.13383 (2022) - [i61]Josh Abramson, Arun Ahuja, Federico Carnevale, Petko Georgiev, Alex Goldin, Alden Hung, Jessica Landon, Jirka Lhotka, Timothy P. Lillicrap, Alistair Muldal, George Powell, Adam Santoro, Guy Scully, Sanjana Srivastava, Tamara von Glehn, Greg Wayne, Nathaniel Wong, Chen Yan, Rui Zhu:
Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback. CoRR abs/2211.11602 (2022) - 2021
- [i60]Adam Santoro, Andrew K. Lampinen, Kory W. Mathewson, Timothy P. Lillicrap, David Raposo:
Symbolic Behaviour in Artificial Intelligence. CoRR abs/2102.03406 (2021) - [i59]Roman Pogodin, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham:
Towards Biologically Plausible Convolutional Networks. CoRR abs/2106.13031 (2021) - [i58]Josh Abramson, Arun Ahuja, Arthur Brussee, Federico Carnevale, Mary Cassin, Felix Fischer, Petko Georgiev, Alex Goldin, Tim Harley, Felix Hill, Peter C. Humphreys, Alden Hung, Jessica Landon, Timothy P. Lillicrap, Hamza Merzic, Alistair Muldal, Adam Santoro, Guy Scully, Tamara von Glehn, Greg Wayne, Nathaniel Wong, Chen Yan, Rui Zhu:
Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning. CoRR abs/2112.03763 (2021) - 2020
- [i57]Yuval Tassa, Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy P. Lillicrap, Nicolas Heess:
dm_control: Software and Tasks for Continuous Control. CoRR abs/2006.12983 (2020) - [i56]Mehdi Mirza, Andrew Jaegle, Jonathan J. Hunt, Arthur Guez, Saran Tunyasuvunakool, Alistair Muldal, Théophane Weber, Péter Karkus, Sébastien Racanière, Lars Buesing, Timothy P. Lillicrap, Nicolas Heess:
Physically Embedded Planning Problems: New Challenges for Reinforcement Learning. CoRR abs/2009.05524 (2020) - [i55]Péter Karkus, Mehdi Mirza, Arthur Guez, Andrew Jaegle, Timothy P. Lillicrap, Lars Buesing, Nicolas Heess, Theophane Weber:
Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban. CoRR abs/2010.01298 (2020) - [i54]Danijar Hafner, Timothy P. Lillicrap, Mohammad Norouzi, Jimmy Ba:
Mastering Atari with Discrete World Models. CoRR abs/2010.02193 (2020) - [i53]Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andrew Brock, Jeff Donahue, Timothy P. Lillicrap, Pushmeet Kohli:
Training Generative Adversarial Networks by Solving Ordinary Differential Equations. CoRR abs/2010.15040 (2020) - [i52]Josh Abramson, Arun Ahuja, Arthur Brussee, Federico Carnevale, Mary Cassin, Stephen Clark, Andrew Dudzik, Petko Georgiev, Aurelia Guy, Tim Harley, Felix Hill, Alden Hung, Zachary Kenton, Jessica Landon, Timothy P. Lillicrap, Kory W. Mathewson, Alistair Muldal, Adam Santoro, Nikolay Savinov, Vikrant Varma, Greg Wayne, Nathaniel Wong, Chen Yan, Rui Zhu:
Imitating Interactive Intelligence. CoRR abs/2012.05672 (2020) - 2019
- [i51]Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Théophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy P. Lillicrap:
An investigation of model-free planning. CoRR abs/1901.03559 (2019) - [i50]Felix Hill, Adam Santoro, David G. T. Barrett, Ari S. Morcos, Timothy P. Lillicrap:
Learning to Make Analogies by Contrasting Abstract Relational Structure. CoRR abs/1902.00120 (2019) - [i49]Mohamed Akrout, Collin Wilson, Peter C. Humphreys, Timothy P. Lillicrap, Douglas B. Tweed:
Deep Learning without Weight Transport. CoRR abs/1904.05391 (2019) - [i48]Adam Santoro, Felix Hill, David G. T. Barrett, David Raposo, Matthew M. Botvinick, Timothy P. Lillicrap:
Is coding a relevant metaphor for building AI? A commentary on "Is coding a relevant metaphor for the brain?", by Romain Brette. CoRR abs/1904.10396 (2019) - [i47]Yan Wu, Mihaela Rosca, Timothy P. Lillicrap:
Deep Compressed Sensing. CoRR abs/1905.06723 (2019) - [i46]Jack W. Rae, Sergey Bartunov, Timothy P. Lillicrap:
Meta-Learning Neural Bloom Filters. CoRR abs/1906.04304 (2019) - [i45]Timothy P. Lillicrap, Konrad P. Körding:
What does it mean to understand a neural network? CoRR abs/1907.06374 (2019) - [i44]Sébastien Racanière, Andrew K. Lampinen, Adam Santoro, David P. Reichert, Vlad Firoiu, Timothy P. Lillicrap:
Automated curricula through setter-solver interactions. CoRR abs/1909.12892 (2019) - [i43]Sergey Bartunov, Jack W. Rae, Simon Osindero, Timothy P. Lillicrap:
Meta-Learning Deep Energy-Based Memory Models. CoRR abs/1910.02720 (2019) - [i42]Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Timothy P. Lillicrap:
Compressive Transformers for Long-Range Sequence Modelling. CoRR abs/1911.05507 (2019) - [i41]Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy P. Lillicrap, David Silver:
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. CoRR abs/1911.08265 (2019) - [i40]Yan Wu, Jeff Donahue, David Balduzzi, Karen Simonyan, Timothy P. Lillicrap:
LOGAN: Latent Optimisation for Generative Adversarial Networks. CoRR abs/1912.00953 (2019) - [i39]Danijar Hafner, Timothy P. Lillicrap, Jimmy Ba, Mohammad Norouzi:
Dream to Control: Learning Behaviors by Latent Imagination. CoRR abs/1912.01603 (2019) - 2018
- [i38]Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, Timothy P. Lillicrap, Martin A. Riedmiller:
DeepMind Control Suite. CoRR abs/1801.00690 (2018) - [i37]Jack W. Rae, Chris Dyer, Peter Dayan, Timothy P. Lillicrap:
Fast Parametric Learning with Activation Memorization. CoRR abs/1803.10049 (2018) - [i36]Greg Wayne, Chia-Chun Hung, David Amos, Mehdi Mirza, Arun Ahuja, Agnieszka Grabska-Barwinska, Jack W. Rae, Piotr Mirowski, Joel Z. Leibo, Adam Santoro, Mevlana Gemici, Malcolm Reynolds, Tim Harley, Josh Abramson, Shakir Mohamed, Danilo Jimenez Rezende, David Saxton, Adam Cain, Chloe Hillier, David Silver, Koray Kavukcuoglu, Matthew M. Botvinick, Demis Hassabis, Timothy P. Lillicrap:
Unsupervised Predictive Memory in a Goal-Directed Agent. CoRR abs/1803.10760 (2018) - [i35]Anirudh Goyal, Philemon Brakel, William Fedus, Timothy P. Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio:
Recall Traces: Backtracking Models for Efficient Reinforcement Learning. CoRR abs/1804.00379 (2018) - [i34]Yan Wu, Greg Wayne, Alex Graves, Timothy P. Lillicrap:
The Kanerva Machine: A Generative Distributed Memory. CoRR abs/1804.01756 (2018) - [i33]Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy P. Lillicrap:
Distributed Distributional Deterministic Policy Gradients. CoRR abs/1804.08617 (2018) - [i32]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) - [i31]Vinícius Flores Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David P. Reichert, Timothy P. Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew M. Botvinick, Oriol Vinyals, Peter W. Battaglia:
Relational Deep Reinforcement Learning. CoRR abs/1806.01830 (2018) - [i30]David G. T. Barrett, Felix Hill, Adam Santoro, Ari S. Morcos, Timothy P. Lillicrap:
Measuring abstract reasoning in neural networks. CoRR abs/1807.04225 (2018) - [i29]Sergey Bartunov, Adam Santoro, Blake A. Richards, Geoffrey E. Hinton, Timothy P. Lillicrap:
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures. CoRR abs/1807.04587 (2018) - [i28]Danijar Hafner, Dustin Tran, Alex Irpan, Timothy P. Lillicrap, James Davidson:
Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors. CoRR abs/1807.09289 (2018) - [i27]Nikolay Savinov, Anton Raichuk, Raphaël Marinier, Damien Vincent, Marc Pollefeys, Timothy P. Lillicrap, Sylvain Gelly:
Episodic Curiosity through Reachability. CoRR abs/1810.02274 (2018) - [i26]Chia-Chun Hung, Timothy P. Lillicrap, Josh Abramson, Yan Wu, Mehdi Mirza, Federico Carnevale, Arun Ahuja, Greg Wayne:
Optimizing Agent Behavior over Long Time Scales by Transporting Value. CoRR abs/1810.06721 (2018) - [i25]Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson:
Learning Latent Dynamics for Planning from Pixels. CoRR abs/1811.04551 (2018) - [i24]Yan Wu, Greg Wayne, Karol Gregor, Timothy P. Lillicrap:
Learning Attractor Dynamics for Generative Memory. CoRR abs/1811.09556 (2018) - [i23]David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Greg Wayne:
Experience Replay for Continual Learning. CoRR abs/1811.11682 (2018) - [i22]Jonathan J. Hunt, André Barreto, Timothy P. Lillicrap, Nicolas Heess:
Entropic Policy Composition with Generalized Policy Improvement and Divergence Correction. CoRR abs/1812.02216 (2018) - 2017
- [i21]Mevlana Gemici, Chia-Chun Hung, Adam Santoro, Greg Wayne, Shakir Mohamed, Danilo Jimenez Rezende, David Amos, Timothy P. Lillicrap:
Generative Temporal Models with Memory. CoRR abs/1702.04649 (2017) - [i20]David Raposo, Adam Santoro, David G. T. Barrett, Razvan Pascanu, Timothy P. Lillicrap, Peter W. Battaglia:
Discovering objects and their relations from entangled scene representations. CoRR abs/1702.05068 (2017) - [i19]Ivaylo Popov, Nicolas Heess, Timothy P. Lillicrap, Roland Hafner, Gabriel Barth-Maron, Matej Vecerík, Thomas Lampe, Yuval Tassa, Tom Erez, Martin A. Riedmiller:
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation. CoRR abs/1704.03073 (2017) - [i18]Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Bernhard Schölkopf, Sergey Levine:
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning. CoRR abs/1706.00387 (2017) - [i17]Adam Santoro, David Raposo, David G. T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter W. Battaglia, Timothy P. Lillicrap:
A simple neural network module for relational reasoning. CoRR abs/1706.01427 (2017) - [i16]Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John P. Agapiou, Julian Schrittwieser, John Quan, Stephen Gaffney, Stig Petersen, Karen Simonyan, Tom Schaul, Hado van Hasselt, David Silver, Timothy P. Lillicrap, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, Anders Ekermo, Jacob Repp, Rodney Tsing:
StarCraft II: A New Challenge for Reinforcement Learning. CoRR abs/1708.04782 (2017) - [i15]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) - [i14]David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy P. Lillicrap, Karen Simonyan, Demis Hassabis:
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. CoRR abs/1712.01815 (2017) - 2016
- [i13]Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu:
Asynchronous Methods for Deep Reinforcement Learning. CoRR abs/1602.01783 (2016) - [i12]Shixiang Gu, Timothy P. Lillicrap, Ilya Sutskever, Sergey Levine:
Continuous Deep Q-Learning with Model-based Acceleration. CoRR abs/1603.00748 (2016) - [i11]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) - [i10]Oriol Vinyals, Charles Blundell, Timothy P. Lillicrap, Koray Kavukcuoglu, Daan Wierstra:
Matching Networks for One Shot Learning. CoRR abs/1606.04080 (2016) - [i9]Shixiang Gu, Ethan Holly, Timothy P. Lillicrap, Sergey Levine:
Deep Reinforcement Learning for Robotic Manipulation. CoRR abs/1610.00633 (2016) - [i8]Nicolas Heess, Gregory Wayne, Yuval Tassa, Timothy P. Lillicrap, Martin A. Riedmiller, David Silver:
Learning and Transfer of Modulated Locomotor Controllers. CoRR abs/1610.05182 (2016) - [i7]Jack W. Rae, Jonathan J. Hunt, Tim Harley, Ivo Danihelka, Andrew W. Senior, Greg Wayne, Alex Graves, Timothy P. Lillicrap:
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes. CoRR abs/1610.09027 (2016) - [i6]Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine:
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. CoRR abs/1611.02247 (2016) - [i5]Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Nando de Freitas:
Learning to Learn for Global Optimization of Black Box Functions. CoRR abs/1611.03824 (2016) - 2015
- [i4]Nicolas Heess, Greg Wayne, David Silver, Timothy P. Lillicrap, Yuval Tassa, Tom Erez:
Learning Continuous Control Policies by Stochastic Value Gradients. CoRR abs/1510.09142 (2015) - [i3]Ilya Sutskever, Rafal Józefowicz, Karol Gregor, Danilo Jimenez Rezende, Timothy P. Lillicrap, Oriol Vinyals:
Towards Principled Unsupervised Learning. CoRR abs/1511.06440 (2015) - [i2]Nicolas Heess, Jonathan J. Hunt, Timothy P. Lillicrap, David Silver:
Memory-based control with recurrent neural networks. CoRR abs/1512.04455 (2015) - 2014
- [i1]Timothy P. Lillicrap, Daniel Cownden, Douglas B. Tweed, Colin J. Akerman:
Random feedback weights support learning in deep neural networks. CoRR abs/1411.0247 (2014)
Coauthor Index
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