Timothy P. Lillicrap

According to our database, Timothy P. Lillicrap authored at least 103 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of three.
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2024

The refinement paradox and cumulative cultural evolution: Complex products of collective improvement favor conformist outcomes, blind copying, and hyper-credulity.

Elena Miu, Luke Rendell, Sam Bowles, Rob Boyd, Daniel Cownden, Magnus Enquist, Kimmo Eriksson, Marcus W. Feldman, Timothy P. Lillicrap, Richard McElreath, Stuart Murray, James Ounsley, Kevin N. Lala

PLoS Comput. Biol., 2024

AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents.

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

CoRR, 2024

Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving.

Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Siyuan Guo, Michal Valko, Timothy P. Lillicrap, Danilo J. Rezende, Yoshua Bengio, Michael Mozer, Sanjeev Arora

CoRR, 2024

Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning.

Yuxi Xie, Anirudh Goyal, Wenyue Zheng, Min-Yen Kan, Timothy P. Lillicrap, Kenji Kawaguchi, Michael Shieh

CoRR, 2024

Mixture-of-Depths: Dynamically allocating compute in transformer-based language models.

David Raposo, Samuel Ritter, Blake A. Richards, Timothy P. Lillicrap, Peter Conway Humphreys, Adam Santoro

CoRR, 2024

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

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.

CoRR, 2024

2023

Gemini: A Family of Highly Capable Multimodal Models.

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.

CoRR, 2023

Android in the Wild: A Large-Scale Dataset for Android Device Control.

Christopher Rawles, Alice Li, Daniel Rodriguez, Oriana Riva, Timothy P. Lillicrap

CoRR, 2023

Mastering Diverse Domains through World Models.

Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy P. Lillicrap

CoRR, 2023

AndroidInTheWild: A Large-Scale Dataset For Android Device Control.

Christopher Rawles, Alice Li, Daniel Rodriguez, Oriana Riva, Timothy P. Lillicrap

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Evaluating Long-Term Memory in 3D Mazes.

Jurgis Pasukonis, Timothy P. Lillicrap, Danijar Hafner

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

The Brain-Computer Metaphor Debate Is Useless: A Matter of Semantics.

Blake A. Richards, Timothy P. Lillicrap

Frontiers Comput. Sci., 2022

Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback.

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

CoRR, 2022

Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution.

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

CoRR, 2022

Evaluating Multimodal Interactive Agents.

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

CoRR, 2022

Retrieval-Augmented Reinforcement Learning.

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

CoRR, 2022

A data-driven approach for learning to control computers.

Peter Conway Humphreys, David Raposo, Toby Pohlen, Gregory Thornton, Rachita Chhaparia, Alistair Muldal, Josh Abramson, Petko Georgiev, Alex Goldin, Adam Santoro, Timothy P. Lillicrap

CoRR, 2022

Equilibrium aggregation: encoding sets via optimization.

Sergey Bartunov, Fabian B. Fuchs, Timothy P. Lillicrap

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Intra-agent speech permits zero-shot task acquisition.

Chen Yan, Federico Carnevale, Petko Georgiev, Adam Santoro, Aurelia Guy, Alistair Muldal, Chia-Chun Hung, Josh Abramson, Timothy P. Lillicrap, Gregory Wayne

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Large-Scale Retrieval for Reinforcement Learning.

Peter Conway Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Theophane Weber, Timothy P. Lillicrap

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On the Stability and Scalability of Node Perturbation Learning.

Naoki Hiratani, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A data-driven approach for learning to control computers.

Peter Conway Humphreys, David Raposo, Tobias Pohlen, Gregory Thornton, Rachita Chhaparia, Alistair Muldal, Josh Abramson, Petko Georgiev, Adam Santoro, Timothy P. Lillicrap

Proceedings of the International Conference on Machine Learning, 2022

Retrieval-Augmented Reinforcement Learning.

Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Peter Conway Humphreys, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy P. Lillicrap, Nicolas Heess, Charles Blundell

Proceedings of the International Conference on Machine Learning, 2022

2021

Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning.

Josh Abramson, Arun Ahuja, Arthur Brussee, Federico Carnevale, Mary Cassin, Felix Fischer, Petko Georgiev, Alex Goldin, Tim Harley, Felix Hill, Peter Conway 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

CoRR, 2021

Symbolic Behaviour in Artificial Intelligence.

Adam Santoro, Andrew K. Lampinen, Kory W. Mathewson, Timothy P. Lillicrap, David Raposo

CoRR, 2021

Towards Biologically Plausible Convolutional Networks.

Roman Pogodin, Yash Mehta, Timothy P. Lillicrap, Peter E. Latham

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning.

Shahab Bakhtiari, Patrick J. Mineault, Timothy P. Lillicrap, Christopher C. Pack, Blake A. Richards

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Mastering Atari with Discrete World Models.

Danijar Hafner, Timothy P. Lillicrap, Mohammad Norouzi, Jimmy Ba

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

dm_control: Software and tasks for continuous control.

Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy P. Lillicrap, Nicolas Heess, Yuval Tassa

Softw. Impacts, 2020

Mastering Atari, Go, chess and shogi by planning with a learned model.

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

Nat., 2020

Imitating Interactive Intelligence.

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

CoRR, 2020

Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban.

Péter Karkus, Mehdi Mirza, Arthur Guez, Andrew Jaegle, Timothy P. Lillicrap, Lars Buesing, Nicolas Heess, Theophane Weber

CoRR, 2020

Physically Embedded Planning Problems: New Challenges for Reinforcement Learning.

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

CoRR, 2020

Training Generative Adversarial Networks by Solving Ordinary Differential Equations.

Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andy Brock, Jeff Donahue, Timothy P. Lillicrap, Pushmeet Kohli

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network.

Basile Confavreux, Friedemann Zenke, Everton J. Agnes, Timothy P. Lillicrap, Tim P. Vogels

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Compressive Transformers for Long-Range Sequence Modelling.

Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Chloe Hillier, Timothy P. Lillicrap

Proceedings of the 8th International Conference on Learning Representations, 2020

Automated curriculum generation through setter-solver interactions.

Sébastien Racanière, Andrew K. Lampinen, Adam Santoro, David P. Reichert, Vlad Firoiu, Timothy P. Lillicrap

Proceedings of the 8th International Conference on Learning Representations, 2020

Dream to Control: Learning Behaviors by Latent Imagination.

Danijar Hafner, Timothy P. Lillicrap, Jimmy Ba, Mohammad Norouzi

Proceedings of the 8th International Conference on Learning Representations, 2020

Meta-Learning Deep Energy-Based Memory Models.

Sergey Bartunov, Jack W. Rae, Simon Osindero, Timothy P. Lillicrap

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Grandmaster level in StarCraft II using multi-agent reinforcement learning.

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

Nat., 2019

LOGAN: Latent Optimisation for Generative Adversarial Networks.

Yan Wu, Jeff Donahue, David Balduzzi, Karen Simonyan, Timothy P. Lillicrap

CoRR, 2019

Compressive Transformers for Long-Range Sequence Modelling.

Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Timothy P. Lillicrap

CoRR, 2019

Automated curricula through setter-solver interactions.

Sébastien Racanière, Andrew K. Lampinen, Adam Santoro, David P. Reichert, Vlad Firoiu, Timothy P. Lillicrap

CoRR, 2019

What does it mean to understand a neural network?

Timothy P. Lillicrap, Konrad P. Körding

CoRR, 2019

Is coding a relevant metaphor for building AI? A commentary on "Is coding a relevant metaphor for the brain?", by Romain Brette.

Adam Santoro, Felix Hill, David G. T. Barrett, David Raposo, Matthew M. Botvinick, Timothy P. Lillicrap

CoRR, 2019

Noise Contrastive Priors for Functional Uncertainty.

Danijar Hafner, Dustin Tran, Timothy P. Lillicrap, Alex Irpan, James Davidson

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Experience Replay for Continual Learning.

David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Gregory Wayne

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Deep Learning without Weight Transport.

Mohamed Akrout, Collin Wilson, Peter Conway Humphreys, Timothy P. Lillicrap, Douglas B. Tweed

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Deep Compressed Sensing.

Yan Wu, Mihaela Rosca, Timothy P. Lillicrap

Proceedings of the 36th International Conference on Machine Learning, 2019

Meta-Learning Neural Bloom Filters.

Jack W. Rae, Sergey Bartunov, Timothy P. Lillicrap

Proceedings of the 36th International Conference on Machine Learning, 2019

Composing Entropic Policies using Divergence Correction.

Jonathan J. Hunt, André Barreto, Timothy P. Lillicrap, Nicolas Heess

Proceedings of the 36th International Conference on Machine Learning, 2019

Learning Latent Dynamics for Planning from Pixels.

Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson

Proceedings of the 36th International Conference on Machine Learning, 2019

An Investigation of Model-Free Planning.

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

Proceedings of the 36th International Conference on Machine Learning, 2019

Deep reinforcement learning with relational inductive biases.

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

Proceedings of the 7th International Conference on Learning Representations, 2019

Episodic Curiosity through Reachability.

Nikolay Savinov, Anton Raichuk, Damien Vincent, Raphaël Marinier, Marc Pollefeys, Timothy P. Lillicrap, Sylvain Gelly

Proceedings of the 7th International Conference on Learning Representations, 2019

Learning to Make Analogies by Contrasting Abstract Relational Structure.

Felix Hill, Adam Santoro, David G. T. Barrett, Ari S. Morcos, Timothy P. Lillicrap

Proceedings of the 7th International Conference on Learning Representations, 2019

Recall Traces: Backtracking Models for Efficient Reinforcement Learning.

Anirudh Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy P. Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Vector-based navigation using grid-like representations in artificial agents.

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

Nat., 2018

Entropic Policy Composition with Generalized Policy Improvement and Divergence Correction.

Jonathan J. Hunt, André Barreto, Timothy P. Lillicrap, Nicolas Heess

CoRR, 2018

Optimizing Agent Behavior over Long Time Scales by Transporting Value.

Chia-Chun Hung, Timothy P. Lillicrap, Josh Abramson, Yan Wu, Mehdi Mirza, Federico Carnevale, Arun Ahuja, Greg Wayne

CoRR, 2018

Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors.

Danijar Hafner, Dustin Tran, Alex Irpan, Timothy P. Lillicrap, James Davidson

CoRR, 2018

Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures.

Sergey Bartunov, Adam Santoro, Blake A. Richards, Geoffrey E. Hinton, Timothy P. Lillicrap

CoRR, 2018

Relational Deep Reinforcement Learning.

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

CoRR, 2018

Recall Traces: Backtracking Models for Efficient Reinforcement Learning.

Anirudh Goyal, Philemon Brakel, William Fedus, Timothy P. Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio

CoRR, 2018

Unsupervised Predictive Memory in a Goal-Directed Agent.

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

CoRR, 2018

DeepMind Control Suite.

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

CoRR, 2018

Learning Attractor Dynamics for Generative Memory.

Yan Wu, Gregory Wayne, Karol Gregor, Timothy P. Lillicrap

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Relational recurrent neural networks.

Adam Santoro, Ryan Faulkner, David Raposo, Jack W. Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy P. Lillicrap

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures.

Sergey Bartunov, Adam Santoro, Blake A. Richards, Luke Marris, Geoffrey E. Hinton, Timothy P. Lillicrap

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Measuring abstract reasoning in neural networks.

Adam Santoro, Felix Hill, David G. T. Barrett, Ari S. Morcos, Timothy P. Lillicrap

Proceedings of the 35th International Conference on Machine Learning, 2018

Fast Parametric Learning with Activation Memorization.

Jack W. Rae, Chris Dyer, Peter Dayan, Timothy P. Lillicrap

Proceedings of the 35th International Conference on Machine Learning, 2018

The Kanerva Machine: A Generative Distributed Memory.

Yan Wu, Greg Wayne, Alex Graves, Timothy P. Lillicrap

Proceedings of the 6th International Conference on Learning Representations, 2018

Distributed Distributional Deterministic Policy Gradients.

Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy P. Lillicrap

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights.

Arash Samadi, Timothy P. Lillicrap, Douglas B. Tweed

Neural Comput., 2017

Mastering the game of Go without human knowledge.

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

Nat., 2017

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm.

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

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, Tom Schaul, Christopher Summerfield, Greg Wayne, Theophane Weber, Daan Wierstra, Shane Legg, Demis Hassabis

CoRR, 2017

StarCraft II: A New Challenge for Reinforcement Learning.

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

CoRR, 2017

Data-efficient Deep Reinforcement Learning for Dexterous Manipulation.

Ivaylo Popov, Nicolas Heess, Timothy P. Lillicrap, Roland Hafner, Gabriel Barth-Maron, Matej Vecerík, Thomas Lampe, Yuval Tassa, Tom Erez, Martin A. Riedmiller

CoRR, 2017

Generative Temporal Models with Memory.

Mevlana Gemici, Chia-Chun Hung, Adam Santoro, Greg Wayne, Shakir Mohamed, Danilo Jimenez Rezende, David Amos, Timothy P. Lillicrap

CoRR, 2017

A simple neural network module for relational reasoning.

Adam Santoro, David Raposo, David G. T. Barrett, Mateusz Malinowski, Razvan Pascanu, Peter W. Battaglia, Tim Lillicrap

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning.

Shixiang Gu, Tim Lillicrap, Richard E. Turner, Zoubin Ghahramani, Bernhard Schölkopf, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates.

Shixiang Gu, Ethan Holly, Timothy P. Lillicrap, Sergey Levine

Proceedings of the 2017 IEEE International Conference on Robotics and Automation, 2017

Learning to Learn without Gradient Descent by Gradient Descent.

Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matthew M. Botvinick, Nando de Freitas

Proceedings of the 34th International Conference on Machine Learning, 2017

Discovering objects and their relations from entangled scene representations.

David Raposo, Adam Santoro, David G. T. Barrett, Razvan Pascanu, Tim Lillicrap, Peter W. Battaglia

Proceedings of the 5th International Conference on Learning Representations, 2017

Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic.

Shixiang Gu, Timothy P. Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Mastering the game of Go with deep neural networks and tree search.

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

Nat., 2016

One-shot Learning with Memory-Augmented Neural Networks.

Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, Timothy P. Lillicrap

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

Proceedings of the 4th International Conference on Learning Representations, 2016

Learning and Transfer of Modulated Locomotor Controllers.

Nicolas Heess, Gregory Wayne, Yuval Tassa, Timothy P. Lillicrap, Martin A. Riedmiller, David Silver

CoRR, 2016

Deep Reinforcement Learning for Robotic Manipulation.

Shixiang Gu, Ethan Holly, Timothy P. Lillicrap, Sergey Levine

CoRR, 2016

Learning to Learn for Global Optimization of Black Box Functions.

Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Nando de Freitas

CoRR, 2016

Matching Networks for One Shot Learning.

Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, Daan Wierstra

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes.

Jack W. Rae, Jonathan J. Hunt, Ivo Danihelka, Timothy Harley, Andrew W. Senior, Gregory Wayne, Alex Graves, Tim Lillicrap

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Meta-Learning with Memory-Augmented Neural Networks.

Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, Timothy P. Lillicrap

Proceedings of the 33nd International Conference on Machine Learning, 2016

Asynchronous Methods for Deep Reinforcement Learning.

Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu

Proceedings of the 33nd International Conference on Machine Learning, 2016

Continuous Deep Q-Learning with Model-based Acceleration.

Shixiang Gu, Timothy P. Lillicrap, Ilya Sutskever, Sergey Levine

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Towards Principled Unsupervised Learning.

Ilya Sutskever, Rafal Józefowicz, Karol Gregor, Danilo Jimenez Rezende, Timothy P. Lillicrap, Oriol Vinyals

CoRR, 2015

Memory-based control with recurrent neural networks.

Nicolas Heess, Jonathan J. Hunt, Timothy P. Lillicrap, David Silver

CoRR, 2015

Learning Continuous Control Policies by Stochastic Value Gradients.

Nicolas Heess, Gregory Wayne, David Silver, Timothy P. Lillicrap, Tom Erez, Yuval Tassa

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Random feedback weights support learning in deep neural networks.

Timothy P. Lillicrap, Daniel Cownden, Douglas B. Tweed, Colin J. Akerman

CoRR, 2014

2012

Relevance Realization and the Emerging Framework in Cognitive Science.

John Vervaeke, Timothy P. Lillicrap, Blake A. Richards

J. Log. Comput., 2012

2008

Sensitivity Derivatives for Flexible Sensorimotor Learning.

M. N. Abdelghani, Timothy P. Lillicrap, Douglas B. Tweed

Neural Comput., 2008