Lillicrap, Timothy

40 publications

NeurIPS 2024 Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, Siyuan Guo, Michal Valko, Timothy Lillicrap, Danilo Rezende, Yoshua Bengio, Michael Mozer, Sanjeev Arora

NeurIPS 2023 AndroidInTheWild: A Large-Scale Dataset for Android Device Control Christopher Rawles, Alice Li, Daniel Rodriguez, Oriana Riva, Timothy Lillicrap

ICML 2022 A Data-Driven Approach for Learning to Control Computers Peter C Humphreys, David Raposo, Tobias Pohlen, Gregory Thornton, Rachita Chhaparia, Alistair Muldal, Josh Abramson, Petko Georgiev, Adam Santoro, Timothy Lillicrap

NeurIPS 2022 Intra-Agent Speech Permits Zero-Shot Task Acquisition Chen Yan, Federico Carnevale, Petko I Georgiev, Adam Santoro, Aurelia Guy, Alistair Muldal, Chia-Chun Hung, Joshua Abramson, Timothy Lillicrap, Gregory Wayne

NeurIPS 2022 Large-Scale Retrieval for Reinforcement Learning Peter Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Theophane Weber, Timothy Lillicrap

NeurIPS 2022 On the Stability and Scalability of Node Perturbation Learning Naoki Hiratani, Yash Mehta, Timothy Lillicrap, Peter E Latham

ICML 2022 Retrieval-Augmented Reinforcement Learning Anirudh Goyal, Abram Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adrià Puigdomènech Badia, Arthur Guez, Mehdi Mirza, Peter C Humphreys, Ksenia Konyushova, Michal Valko, Simon Osindero, Timothy Lillicrap, Nicolas Heess, Charles Blundell

NeurIPS 2021 The Functional Specialization of Visual Cortex Emerges from Training Parallel Pathways with Self-Supervised Predictive Learning Shahab Bakhtiari, Patrick Mineault, Timothy Lillicrap, Christopher Pack, Blake Richards

NeurIPS 2021 Towards Biologically Plausible Convolutional Networks Roman Pogodin, Yash Mehta, Timothy Lillicrap, Peter E Latham

NeurIPS 2020 A Meta-Learning Approach to (re)discover Plasticity Rules That Carve a Desired Function into a Neural Network Basile Confavreux, Friedemann Zenke, Everton Agnes, Timothy Lillicrap, Tim Vogels

ICLR 2020 Automated Curriculum Generation Through Setter-Solver Interactions Sebastien Racaniere, Andrew Lampinen, Adam Santoro, David Reichert, Vlad Firoiu, Timothy Lillicrap

ICLR 2020 Dream to Control: Learning Behaviors by Latent Imagination Danijar Hafner, Timothy Lillicrap, Jimmy Ba, Mohammad Norouzi

NeurIPS 2020 Training Generative Adversarial Networks by Solving Ordinary Differential Equations Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andy Brock, Jeff Donahue, Timothy Lillicrap, Pushmeet Kohli

ICML 2019 An Investigation of Model-Free Planning Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sebastien Racaniere, Theophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy Lillicrap

ICML 2019 Composing Entropic Policies Using Divergence Correction Jonathan Hunt, Andre Barreto, Timothy Lillicrap, Nicolas Heess

ICML 2019 Deep Compressed Sensing Yan Wu, Mihaela Rosca, Timothy Lillicrap

NeurIPS 2019 Deep Learning Without Weight Transport Mohamed Akrout, Collin Wilson, Peter Humphreys, Timothy Lillicrap, Douglas B Tweed

ICLR 2019 Deep Reinforcement Learning with Relational Inductive Biases Vinicius Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David Reichert, Timothy Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew Botvinick, Oriol Vinyals, Peter Battaglia

ICLR 2019 Episodic Curiosity Through Reachability Nikolay Savinov, Anton Raichuk, Damien Vincent, Raphael Marinier, Marc Pollefeys, Timothy Lillicrap, Sylvain Gelly

NeurIPS 2019 Experience Replay for Continual Learning David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, Gregory Wayne

ICML 2019 Learning Latent Dynamics for Planning from Pixels Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson

ICLR 2019 Learning to Make Analogies by Contrasting Abstract Relational Structure Felix Hill, Adam Santoro, David Barrett, Ari Morcos, Timothy Lillicrap

ICML 2019 Meta-Learning Neural Bloom Filters Jack Rae, Sergey Bartunov, Timothy Lillicrap

UAI 2019 Noise Contrastive Priors for Functional Uncertainty Danijar Hafner, Dustin Tran, Timothy Lillicrap, Alex Irpan, James Davidson

ICLR 2019 Recall Traces: Backtracking Models for Efficient Reinforcement Learning Anirudh Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio

NeurIPS 2018 Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures Sergey Bartunov, Adam Santoro, Blake Richards, Luke Marris, Geoffrey E. Hinton, Timothy Lillicrap

ICLR 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 Lillicrap

ICML 2018 Fast Parametric Learning with Activation Memorization Jack Rae, Chris Dyer, Peter Dayan, Timothy Lillicrap

NeurIPS 2018 Learning Attractor Dynamics for Generative Memory Yan Wu, Gregory Wayne, Karol Gregor, Timothy Lillicrap

ICML 2018 Measuring Abstract Reasoning in Neural Networks David Barrett, Felix Hill, Adam Santoro, Ari Morcos, Timothy Lillicrap

NeurIPS 2018 Relational Recurrent Neural Networks Adam Santoro, Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy Lillicrap

ICLR 2018 The Kanerva Machine: A Generative Distributed Memory Yan Wu, Greg Wayne, Alex Graves, Timothy Lillicrap

NeurIPS 2017 A Simple Neural Network Module for Relational Reasoning Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, Timothy Lillicrap

NeurIPS 2017 Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning Shixiang Gu, Timothy Lillicrap, Richard E Turner, Zoubin Ghahramani, Bernhard Schölkopf, Sergey Levine

ICML 2016 Asynchronous Methods for Deep Reinforcement Learning Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu

ICML 2016 Continuous Deep Q-Learning with Model-Based Acceleration Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, Sergey Levine

NeurIPS 2016 Matching Networks for One Shot Learning Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra

ICML 2016 Meta-Learning with Memory-Augmented Neural Networks Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap

NeurIPS 2016 Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes Jack Rae, Jonathan J Hunt, Ivo Danihelka, Timothy Harley, Andrew W. Senior, Gregory Wayne, Alex Graves, Timothy Lillicrap

NeurIPS 2015 Learning Continuous Control Policies by Stochastic Value Gradients Nicolas Heess, Gregory Wayne, David Silver, Timothy Lillicrap, Tom Erez, Yuval Tassa