Sensitivity Derivatives for Flexible Sensorimotor Learning. - M. N. Abdelghani; Timothy P. Lillicrap; Douglas B. Tweed (Neural Comput., 2008)

Relevance Realization and the Emerging Framework in Cognitive Science. - John Vervaeke; Timothy P. Lillicrap; Blake A. Richards (J. Log. Comput., 2012)

Random feedback weights support learning in deep neural networks. - Timothy P. Lillicrap; Daniel Cownden; Douglas B. Tweed; Colin J. Akerman (CoRR, 2014)

Learning Continuous Control Policies by Stochastic Value Gradients. - Nicolas Heess; Gregory Wayne; David Silver; Timothy P. Lillicrap; Tom Erez; Yuval Tassa (NIPS, 2015)

Memory-based control with recurrent neural networks. - Nicolas Heess; Jonathan J. Hunt; Timothy P. Lillicrap; David Silver (CoRR, 2015)

Towards Principled Unsupervised Learning. - Ilya Sutskever; Rafal Józefowicz; Karol Gregor; Danilo Jimenez Rezende; Timothy P. Lillicrap; Oriol Vinyals (CoRR, 2015)

Continuous Deep Q-Learning with Model-based Acceleration. - Shixiang Gu; Timothy P. Lillicrap; Ilya Sutskever; Sergey Levine (ICML, 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 (ICML, 2016)

Meta-Learning with Memory-Augmented Neural Networks. - Adam Santoro; Sergey Bartunov; Matthew M. Botvinick; Daan Wierstra; Timothy P. Lillicrap (ICML, 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 (NIPS, 2016)

Matching Networks for One Shot Learning. - Oriol Vinyals; Charles Blundell; Tim Lillicrap; Koray Kavukcuoglu; Daan Wierstra (NIPS, 2016)

Learning to Learn for Global Optimization of Black Box Functions. - Yutian Chen 001; Matthew W. Hoffman; Sergio Gomez Colmenarejo; Misha Denil; Timothy P. Lillicrap; Nando de Freitas (CoRR, 2016)

Deep Reinforcement Learning for Robotic Manipulation. - Shixiang Gu; Ethan Holly; Timothy P. Lillicrap; Sergey Levine (CoRR, 2016)

Learning and Transfer of Modulated Locomotor Controllers. - Nicolas Heess; Gregory Wayne; Yuval Tassa; Timothy P. Lillicrap; Martin A. Riedmiller; David Silver (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 (ICLR (Poster), 2016)

One-shot Learning with Memory-Augmented Neural Networks. - Adam Santoro; Sergey Bartunov; Matthew M. Botvinick; Daan Wierstra; Timothy P. Lillicrap (CoRR, 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 002; Julian Schrittwieser; Ioannis Antonoglou; Vedavyas Panneershelvam; Marc Lanctot; Sander Dieleman; Dominik Grewe; et al. (Nat., 2016)

Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. - Shixiang Gu; Timothy P. Lillicrap; Zoubin Ghahramani; Richard E. Turner; Sergey Levine (ICLR, 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 (ICLR (Workshop), 2017)

Learning to Learn without Gradient Descent by Gradient Descent. - Yutian Chen 001; Matthew W. Hoffman; Sergio Gomez Colmenarejo; Misha Denil; Timothy P. Lillicrap; Matthew M. Botvinick; Nando de Freitas (ICML, 2017)

Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. - Shixiang Gu; Ethan Holly; Timothy P. Lillicrap; Sergey Levine (ICRA, 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 (NIPS, 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 (NIPS, 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)

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)

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; et al. (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; et al. (CoRR, 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; et al. (CoRR, 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 001; Timothy P. Lillicrap; et al. (Nat., 2017)

Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights. - Arash Samadi; Timothy P. Lillicrap; Douglas B. Tweed (Neural Comput., 2017)

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 (ICLR (Poster), 2018)

The Kanerva Machine: A Generative Distributed Memory. - Yan Wu 010; Greg Wayne; Alex Graves; Timothy P. Lillicrap (ICLR (Poster), 2018)

Fast Parametric Learning with Activation Memorization. - Jack W. Rae; Chris Dyer; Peter Dayan; Timothy P. Lillicrap (ICML, 2018)

Measuring abstract reasoning in neural networks. - Adam Santoro; Felix Hill; David G. T. Barrett; Ari S. Morcos; Timothy P. Lillicrap (ICML, 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 (NeurIPS, 2018)

Relational recurrent neural networks. - Adam Santoro; Ryan Faulkner 001; David Raposo; Jack W. Rae; Mike Chrzanowski; Theophane Weber; Daan Wierstra; Oriol Vinyals; Razvan Pascanu; Timothy P. Lillicrap (NeurIPS, 2018)

Learning Attractor Dynamics for Generative Memory. - Yan Wu 010; Gregory Wayne; Karol Gregor; Timothy P. Lillicrap (NeurIPS, 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)

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; et al. (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)

Relational Deep Reinforcement Learning. - Vinícius Flores Zambaldi; David Raposo; Adam Santoro; Victor Bapst; Yujia Li 001; Igor Babuschkin; Karl Tuyls; David P. Reichert; Timothy P. Lillicrap; Edward Lockhart; Murray Shanahan; Victoria Langston; et al. (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)

Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors. - Danijar Hafner; Dustin Tran; Alex Irpan; Timothy P. Lillicrap; James Davidson (CoRR, 2018)

Optimizing Agent Behavior over Long Time Scales by Transporting Value. - Chia-Chun Hung; Timothy P. Lillicrap; Josh Abramson; Yan Wu 010; Mehdi Mirza; Federico Carnevale; Arun Ahuja; Greg Wayne (CoRR, 2018)

Entropic Policy Composition with Generalized Policy Improvement and Divergence Correction. - Jonathan J. Hunt; André Barreto 001; Timothy P. Lillicrap; Nicolas Heess (CoRR, 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; et al. (Nat., 2018)

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 (ICLR (Poster), 2019)

Learning to Make Analogies by Contrasting Abstract Relational Structure. - Felix Hill; Adam Santoro; David G. T. Barrett; Ari S. Morcos; Timothy P. Lillicrap (ICLR (Poster), 2019)

Episodic Curiosity through Reachability. - Nikolay Savinov; Anton Raichuk; Damien Vincent; Raphaël Marinier; Marc Pollefeys; Timothy P. Lillicrap; Sylvain Gelly (ICLR (Poster), 2019)

Deep reinforcement learning with relational inductive biases. - Vinícius Flores Zambaldi; David Raposo; Adam Santoro; Victor Bapst; Yujia Li 001; Igor Babuschkin; Karl Tuyls; David P. Reichert; Timothy P. Lillicrap; Edward Lockhart; Murray Shanahan; Victoria Langston; et al. (ICLR (Poster), 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; et al. (ICML, 2019)

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

Composing Entropic Policies using Divergence Correction. - Jonathan J. Hunt; André Barreto 001; Timothy P. Lillicrap; Nicolas Heess (ICML, 2019)

Meta-Learning Neural Bloom Filters. - Jack W. Rae; Sergey Bartunov; Timothy P. Lillicrap (ICML, 2019)

Deep Compressed Sensing. - Yan Wu 010; Mihaela Rosca; Timothy P. Lillicrap (ICML, 2019)

Deep Learning without Weight Transport. - Mohamed Akrout; Collin Wilson; Peter Conway Humphreys; Timothy P. Lillicrap; Douglas B. Tweed (NeurIPS, 2019)

Experience Replay for Continual Learning. - David Rolnick; Arun Ahuja; Jonathan Schwarz; Timothy P. Lillicrap; Gregory Wayne (NeurIPS, 2019)

Noise Contrastive Priors for Functional Uncertainty. - Danijar Hafner; Dustin Tran; Timothy P. Lillicrap; Alex Irpan; James Davidson (UAI, 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)

What does it mean to understand a neural network? - Timothy P. Lillicrap; Konrad P. Körding (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)

Compressive Transformers for Long-Range Sequence Modelling. - Jack W. Rae; Anna Potapenko; Siddhant M. Jayakumar; Timothy P. Lillicrap (CoRR, 2019)

LOGAN: Latent Optimisation for Generative Adversarial Networks. - Yan Wu 010; Jeff Donahue; David Balduzzi; Karen Simonyan; Timothy P. Lillicrap (CoRR, 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; et al. (Nat., 2019)

Meta-Learning Deep Energy-Based Memory Models. - Sergey Bartunov; Jack W. Rae; Simon Osindero; Timothy P. Lillicrap (ICLR, 2020)

Dream to Control: Learning Behaviors by Latent Imagination. - Danijar Hafner; Timothy P. Lillicrap; Jimmy Ba; Mohammad Norouzi 002 (ICLR, 2020)

Automated curriculum generation through setter-solver interactions. - Sébastien Racanière; Andrew K. Lampinen; Adam Santoro; David P. Reichert; Vlad Firoiu; Timothy P. Lillicrap (ICLR, 2020)

Compressive Transformers for Long-Range Sequence Modelling. - Jack W. Rae; Anna Potapenko; Siddhant M. Jayakumar; Chloe Hillier; Timothy P. Lillicrap (ICLR, 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 (NeurIPS, 2020)

Training Generative Adversarial Networks by Solving Ordinary Differential Equations. - Chongli Qin; Yan Wu 010; Jost Tobias Springenberg; Andy Brock; Jeff Donahue; Timothy P. Lillicrap; Pushmeet Kohli (NeurIPS, 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)

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)

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; et al. (CoRR, 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)

dm_control: Software and tasks for continuous control. - Saran Tunyasuvunakool; Alistair Muldal; Yotam Doron; Siqi Liu 002; Steven Bohez; Josh Merel; Tom Erez; Timothy P. Lillicrap; Nicolas Heess; Yuval Tassa (Softw. Impacts, 2020)

Mastering Atari with Discrete World Models. - Danijar Hafner; Timothy P. Lillicrap; Mohammad Norouzi 002; Jimmy Ba (ICLR, 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 (NeurIPS, 2021)

Towards Biologically Plausible Convolutional Networks. - Roman Pogodin; Yash Mehta; Timothy P. Lillicrap; Peter E. Latham (NeurIPS, 2021)

Symbolic Behaviour in Artificial Intelligence. - Adam Santoro; Andrew K. Lampinen; Kory W. Mathewson; Timothy P. Lillicrap; David Raposo (CoRR, 2021)

Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning. - Josh Abramson; Arun Ahuja; Arthur Brussee; Federico Carnevale; Mary Cassin; Felix Fischer 004; Petko Georgiev; Alex Goldin; Tim Harley; Felix Hill; Peter Conway Humphreys; Alden Hung; et al. (CoRR, 2021)

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; et al. (ICML, 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 (ICML, 2022)

On the Stability and Scalability of Node Perturbation Learning. - Naoki Hiratani; Yash Mehta; Timothy P. Lillicrap; Peter E. Latham (NeurIPS, 2022)

Large-Scale Retrieval for Reinforcement Learning. - Peter Conway Humphreys; Arthur Guez; Olivier Tieleman; Laurent Sifre; Theophane Weber; Timothy P. Lillicrap (NeurIPS, 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 (NeurIPS, 2022)

Equilibrium aggregation: encoding sets via optimization. - Sergey Bartunov; Fabian B. Fuchs; Timothy P. Lillicrap (UAI, 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)

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; et al. (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; et al. (CoRR, 2022)

Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution. - Anthony Zador; Blake A. Richards; Bence Ölveczky; Sean Escola; Yoshua Bengio; Kwabena Boahen 001; Matthew M. Botvinick; Dmitri B. Chklovskii; Anne Churchland; Claudia Clopath; James DiCarlo; Surya Ganguli; et al. (CoRR, 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; et al. (CoRR, 2022)

The Brain-Computer Metaphor Debate Is Useless: A Matter of Semantics. - Blake A. Richards; Timothy P. Lillicrap (Frontiers Comput. Sci., 2022)

Evaluating Long-Term Memory in 3D Mazes. - Jurgis Pasukonis; Timothy P. Lillicrap; Danijar Hafner (ICLR, 2023)

AndroidInTheWild: A Large-Scale Dataset For Android Device Control. - Christopher Rawles; Alice Li; Daniel Rodriguez; Oriana Riva; Timothy P. Lillicrap (NeurIPS, 2023)

Mastering Diverse Domains through World Models. - Danijar Hafner; Jurgis Pasukonis; Jimmy Ba; Timothy P. Lillicrap (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)

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; et al. (CoRR, 2023)

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 Jimenez Rezende; Yoshua Bengio; Michael C. Mozer; Sanjeev Arora (NeurIPS, 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; et al. (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)

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)

Can foundation models actively gather information in interactive environments to test hypotheses? - Nan Rosemary Ke; Danny P. Sawyer; Hubert Soyer; Martin Engelcke; David P. Reichert; Drew A. Hudson; John Reid; Alexander Lerchner; Danilo Jimenez Rezende; Timothy P. Lillicrap; Michael Mozer; Jane X. Wang (CoRR, 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; et al. (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 Kenji Toyama; Robert James Berry; et al. (ICLR, 2025)

Societal Alignment Frameworks Can Improve LLM Alignment. - Karolina Stanczak; Nicholas Meade; Mehar Bhatia; Hattie Zhou; Konstantin Böttinger; Jeremy Barnes 002; Jason Stanley; Jessica Montgomery; Richard S. Zemel; Nicolas Papernot; Nicolas Chapados; Denis Therien; et al. (CoRR, 2025)

Mastering diverse control tasks through world models. - Danijar Hafner; Jurgis Pasukonis; Jimmy Ba; Timothy P. Lillicrap (Nat., 2025)