Timothy P. Lillicrap

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 122

Top Venues

CoRR

ICML

NeurIPS

ICLR

Publications

Yuxi Xie, Anirudh Goyal, Wenyue Zheng, Min-Yen Kan, Timothy P. Lillicrap, Kenji Kawaguchi, Michael Shieh
Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning. CoRR (2024)
David Raposo, Samuel Ritter, Blake A. Richards, Timothy P. Lillicrap, Peter Conway Humphreys, Adam Santoro
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models. CoRR (2024)
Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Siyuan Guo, Michal Valko, Timothy P. Lillicrap, Danilo J. Rezende, Yoshua Bengio, Michael Mozer, Sanjeev Arora
Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving. CoRR (2024)
Christopher Rawles, Sarah Clinckemaillie, Yifan Chang, Jonathan Waltz, Gabrielle Lau, Marybeth Fair, Alice Li, William E. Bishop, Wei Li, Folawiyo Campbell-Ajala, Daniel Toyama, Robert Berry, Divya Tyamagundlu, Timothy P. Lillicrap, Oriana Riva
AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents. CoRR (2024)
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 (2024)
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 (2023)
Christopher Rawles, Alice Li, Daniel Rodriguez, Oriana Riva, Timothy P. Lillicrap
AndroidInTheWild: A Large-Scale Dataset For Android Device Control. NeurIPS (2023)
Jurgis Pasukonis, Timothy P. Lillicrap, Danijar Hafner
Evaluating Long-Term Memory in 3D Mazes. ICLR (2023)
Christopher Rawles, Alice Li, Daniel Rodriguez, Oriana Riva, Timothy P. Lillicrap
Android in the Wild: A Large-Scale Dataset for Android Device Control. CoRR (2023)
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy P. Lillicrap
Mastering Diverse Domains through World Models. CoRR (2023)
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)
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)
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)
Naoki Hiratani, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham
On the Stability and Scalability of Node Perturbation Learning. NeurIPS (2022)
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 (2022)
Sergey Bartunov, Fabian B. Fuchs, Timothy P. Lillicrap
Equilibrium Aggregation: Encoding Sets via Optimization. CoRR (2022)
Sergey Bartunov, Fabian B. Fuchs, Timothy P. Lillicrap
Equilibrium aggregation: encoding sets via optimization. UAI (2022)
Peter C. Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Théophane Weber, Timothy P. Lillicrap
Large-Scale Retrieval for Reinforcement Learning. CoRR (2022)
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 (2022)
Peter C. Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Theophane Weber, Timothy P. Lillicrap
Large-Scale Retrieval for Reinforcement Learning. NeurIPS (2022)
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 (2022)
Blake A. Richards, Timothy P. Lillicrap
The Brain-Computer Metaphor Debate Is Useless: A Matter of Semantics. Frontiers Comput. Sci. 4 (2022)
Jurgis Pasukonis, Timothy P. Lillicrap, Danijar Hafner
Evaluating Long-Term Memory in 3D Mazes. CoRR (2022)
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 (2022)
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 (2022)
Roman Pogodin, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham
Towards Biologically Plausible Convolutional Networks. CoRR (2021)
Shahab Bakhtiari, Patrick J. Mineault, Timothy P. Lillicrap, Christopher C. Pack, Blake Richards
The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning. NeurIPS (2021)
Roman Pogodin, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham
Towards Biologically Plausible Convolutional Networks. NeurIPS (2021)
Danijar Hafner, Timothy P. Lillicrap, Mohammad Norouzi, Jimmy Ba
Mastering Atari with Discrete World Models. ICLR (2021)
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 (2021)
Adam Santoro, Andrew K. Lampinen, Kory W. Mathewson, Timothy P. Lillicrap, David Raposo
Symbolic Behaviour in Artificial Intelligence. CoRR (2021)
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 (2020)
Danijar Hafner, Timothy P. Lillicrap, Jimmy Ba, Mohammad Norouzi
Dream to Control: Learning Behaviors by Latent Imagination. ICLR (2020)
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)
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 (2020)
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)
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 (2020)
Danijar Hafner, Timothy P. Lillicrap, Mohammad Norouzi, Jimmy Ba
Mastering Atari with Discrete World Models. CoRR (2020)
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 (2020)
Sergey Bartunov, Jack W. Rae, Simon Osindero, Timothy P. Lillicrap
Meta-Learning Deep Energy-Based Memory Models. ICLR (2020)
Sébastien Racanière, Andrew K. Lampinen, Adam Santoro, David P. Reichert, Vlad Firoiu, Timothy P. Lillicrap
Automated curriculum generation through setter-solver interactions. ICLR (2020)
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 (2020)
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) (2020)
Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Chloe Hillier, Timothy P. Lillicrap
Compressive Transformers for Long-Range Sequence Modelling. ICLR (2020)
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 (2020)
Mohamed Akrout, Collin Wilson, Peter C. Humphreys, Timothy P. Lillicrap, Douglas B. Tweed
Deep Learning without Weight Transport. NeurIPS (2019)
Jonathan J. Hunt, André Barreto, Timothy P. Lillicrap, Nicolas Heess
Composing Entropic Policies using Divergence Correction. ICML (2019)
Yan Wu, Mihaela Rosca, Timothy P. Lillicrap
Deep Compressed Sensing. CoRR (2019)
Jack W. Rae, Sergey Bartunov, Timothy P. Lillicrap
Meta-Learning Neural Bloom Filters. CoRR (2019)
Felix Hill, Adam Santoro, David G. T. Barrett, Ari S. Morcos, Timothy P. Lillicrap
Learning to Make Analogies by Contrasting Abstract Relational Structure. CoRR (2019)
Mohamed Akrout, Collin Wilson, Peter C. Humphreys, Timothy P. Lillicrap, Douglas B. Tweed
Deep Learning without Weight Transport. CoRR (2019)
Sébastien Racanière, Andrew K. Lampinen, Adam Santoro, David P. Reichert, Vlad Firoiu, Timothy P. Lillicrap
Automated curricula through setter-solver interactions. CoRR (2019)
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 (2019)
Nikolay Savinov, Anton Raichuk, Damien Vincent, Raphaël Marinier, Marc Pollefeys, Timothy P. Lillicrap, Sylvain Gelly
Episodic Curiosity through Reachability. ICLR (Poster) (2019)
Adam Santoro, Felix Hill, David G. T. Barrett, David Raposo, Matthew 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 (2019)
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)
Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Timothy P. Lillicrap
Compressive Transformers for Long-Range Sequence Modelling. CoRR (2019)
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 (2019)
Danijar Hafner, Timothy P. Lillicrap, Jimmy Ba, Mohammad Norouzi
Dream to Control: Learning Behaviors by Latent Imagination. CoRR (2019)
Timothy P. Lillicrap, Konrad P. Körding
What does it mean to understand a neural network? CoRR (2019)
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)
Sergey Bartunov, Jack W. Rae, Simon Osindero, Timothy P. Lillicrap
Meta-Learning Deep Energy-Based Memory Models. CoRR (2019)
Jack W. Rae, Sergey Bartunov, Timothy P. Lillicrap
Meta-Learning Neural Bloom Filters. ICML (2019)
Yan Wu, Jeff Donahue, David Balduzzi, Karen Simonyan, Timothy P. Lillicrap
LOGAN: Latent Optimisation for Generative Adversarial Networks. CoRR (2019)
Danijar Hafner, Dustin Tran, Timothy P. Lillicrap, Alex Irpan, James Davidson
Noise Contrastive Priors for Functional Uncertainty. UAI (2019)
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Gregory Wayne
Experience Replay for Continual Learning. NeurIPS (2019)
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)
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 Botvinick, Oriol Vinyals, Peter W. Battaglia
Deep reinforcement learning with relational inductive biases. ICLR (Poster) (2019)
Yan Wu, Mihaela Rosca, Timothy P. Lillicrap
Deep Compressed Sensing. ICML (2019)
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) (2019)
Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson
Learning Latent Dynamics for Planning from Pixels. ICML (2019)
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 (2018)
Yan Wu, Greg Wayne, Alex Graves, Timothy P. Lillicrap
The Kanerva Machine: A Generative Distributed Memory. ICLR (Poster) (2018)
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 (2018)
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) (2018)
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 Botvinick, Oriol Vinyals, Peter W. Battaglia
Relational Deep Reinforcement Learning. CoRR (2018)
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 (2018)
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 (2018)
Jack W. Rae, Chris Dyer, Peter Dayan, Timothy P. Lillicrap
Fast Parametric Learning with Activation Memorization. ICML (2018)
Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson
Learning Latent Dynamics for Planning from Pixels. CoRR (2018)
Adam Santoro, Felix Hill, David G. T. Barrett, Ari S. Morcos, Timothy P. Lillicrap
Measuring abstract reasoning in neural networks. ICML (2018)
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Greg Wayne
Experience Replay for Continual Learning. CoRR (2018)
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)
Jack W. Rae, Chris Dyer, Peter Dayan, Timothy P. Lillicrap
Fast Parametric Learning with Activation Memorization. CoRR (2018)
Nikolay Savinov, Anton Raichuk, Raphaël Marinier, Damien Vincent, Marc Pollefeys, Timothy P. Lillicrap, Sylvain Gelly
Episodic Curiosity through Reachability. CoRR (2018)
David G. T. Barrett, Felix Hill, Adam Santoro, Ari S. Morcos, Timothy P. Lillicrap
Measuring abstract reasoning in neural networks. CoRR (2018)
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 (2018)
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)
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 Botvinick, Demis Hassabis, Timothy P. Lillicrap
Unsupervised Predictive Memory in a Goal-Directed Agent. CoRR (2018)
Anirudh Goyal, Philemon Brakel, William Fedus, Timothy P. Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio
Recall Traces: Backtracking Models for Efficient Reinforcement Learning. CoRR (2018)
Jonathan J. Hunt, André Barreto, Timothy P. Lillicrap, Nicolas Heess
Entropic Policy Composition with Generalized Policy Improvement and Divergence Correction. CoRR (2018)
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)
Danijar Hafner, Dustin Tran, Alex Irpan, Timothy P. Lillicrap, James Davidson
Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors. CoRR (2018)
Yan Wu, Greg Wayne, Karol Gregor, Timothy P. Lillicrap
Learning Attractor Dynamics for Generative Memory. CoRR (2018)
Yan Wu, Greg Wayne, Alex Graves, Timothy P. Lillicrap
The Kanerva Machine: A Generative Distributed Memory. CoRR (2018)
Yan Wu, Gregory Wayne, Karol Gregor, Timothy P. Lillicrap
Learning Attractor Dynamics for Generative Memory. NeurIPS (2018)
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 (2017)
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 (2017)
Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matthew Botvinick, Nando de Freitas
Learning to Learn without Gradient Descent by Gradient Descent. ICML (2017)
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 (2017)

Timothy P. Lillicrap.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

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Timothy P. Lillicrap