T. Lillicrap

发表

Grandmaster level in StarCraft II using multi-agent reinforcement learning

Wojciech M. Czarnecki, Oriol Vinyals, I. Babuschkin, 2019, Nature.

Learning Continuous Control Policies by Stochastic Value Gradients

Yuval Tassa, David Silver, Nicolas Heess, 2015, NIPS.

Data-efficient Deep Reinforcement Learning for Dexterous Manipulation pdf

Yuval Tassa, Martin A. Riedmiller, Thomas Lampe, 2017, ArXiv.

Composing Entropic Policies using Divergence Correction

Nicolas Heess, Jonathan J. Hunt, Timothy Lillicrap, 2018, ICML.

Continuous control with deep reinforcement learning pdf

Yuval Tassa, David Silver, Daan Wierstra, 2015, ICLR.

Asynchronous Methods for Deep Reinforcement Learning pdf

Alex Graves, Koray Kavukcuoglu, David Silver, 2016, ICML.

Continuous Deep Q-Learning with Model-based Acceleration pdf

Sergey Levine, Ilya Sutskever, Shixiang Gu, 2016, ICML.

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

Sergey Levine, Shixiang Gu, Ethan Holly, 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

Memory-based control with recurrent neural networks pdf

David Silver, Nicolas Heess, Jonathan J. Hunt, 2015, ArXiv.

Optimizing agent behavior over long time scales by transporting value

Yan Wu, Arun Ahuja, Greg Wayne, 2018, Nature Communications.

Recall Traces: Backtracking Models for Efficient Reinforcement Learning pdf

Sergey Levine, Yoshua Bengio, Hugo Larochelle, 2018, ICLR.

Unsupervised Predictive Memory in a Goal-Directed Agent pdf

Joel Z. Leibo, Demis Hassabis, Koray Kavukcuoglu, 2018, ArXiv.

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

Demis Hassabis, Karen Simonyan, David Silver, 2017, ArXiv.

Distributed Distributional Deterministic Policy Gradients pdf

Matthew W. Hoffman, David Budden, Nicolas Heess, 2018, ICLR.

Relational recurrent neural networks

Razvan Pascanu, Oriol Vinyals, Daan Wierstra, 2018, NeurIPS.

Backpropagation and the brain

Adam Santoro, Colin J. Akerman, Geoffrey Hinton, 2020, Nature Reviews Neuroscience.

Preference Distributions of Primary Motor Cortex Neurons Reflect Control Solutions Optimized for Limb Biomechanics

T. Lillicrap, S. Scott, 2013, Neuron.

Learning and Transfer of Modulated Locomotor Controllers pdf

Yuval Tassa, Martin A. Riedmiller, David Silver, 2016, ArXiv.

dm_control: Software and Tasks for Continuous Control

Yuval Tassa, Steven Bohez, Tom Erez, 2020, Softw. Impacts.

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

Dustin Tran, James Davidson, Danijar Hafner, 2018, ArXiv.

DeepMind Control Suite pdf

Yuval Tassa, Martin A. Riedmiller, Yazhe Li, 2018, ArXiv.

Matching Networks for One Shot Learning

Oriol Vinyals, Koray Kavukcuoglu, Daan Wierstra, 2016, NIPS.

A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

Demis Hassabis, Karen Simonyan, David Silver, 2018, Science.

Deep Reinforcement Learning for Robotic Manipulation pdf

Sergey Levine, Shixiang Gu, Ethan Holly, 2016, ArXiv.

Deep Learning without Weight Transport

Peter C. Humphreys, Timothy P. Lillicrap, Mohamed Akrout, 2019, NeurIPS.

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

Razvan Pascanu, Raia Hadsell, Fabio Viola, 2018, Nature.

Learning to Learn for Global Optimization of Black Box Functions pdf

Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, 2016, ArXiv.

Relational Deep Reinforcement Learning pdf

Razvan Pascanu, Murray Shanahan, Yujia Li, 2018, ArXiv.

Towards Principled Unsupervised Learning pdf

Oriol Vinyals, Karol Gregor, Ilya Sutskever, 2015, ArXiv.

Learning to Make Analogies by Contrasting Abstract Relational Structure pdf

Felix Hill, Timothy P. Lillicrap, Adam Santoro, 2019, ICLR.

Sensitivity Derivatives for Flexible Sensorimotor Learning

Timothy P. Lillicrap, Douglas B. Tweed, M. N. Abdelghani, 2008, Neural Computation.

Automated curricula through setter-solver interactions pdf

Andrew K. Lampinen, Timothy P. Lillicrap, Adam Santoro, 2019, ArXiv.

Learning to Learn without Gradient Descent by Gradient Descent

Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, 2016, ICML.

What does it mean to understand a neural network? pdf

Konrad P. Körding, Timothy P. Lillicrap, Konrad Paul Kording, 2019, ArXiv.

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

Geoffrey E. Hinton, Timothy P. Lillicrap, Adam Santoro, 2018, NeurIPS.

Learning Attractor Dynamics for Generative Memory

Yan Wu, Karol Gregor, Timothy P. Lillicrap, 2018, NeurIPS.

Measuring abstract reasoning in neural networks pdf

Felix Hill, Timothy P. Lillicrap, Adam Santoro, 2018, ICML.

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

Demis Hassabis, Koray Kavukcuoglu, David Silver, 2016, Nature.

Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes

Alex Graves, Jonathan J. Hunt, Ivo Danihelka, 2016, NIPS.

Fast Parametric Learning with Activation Memorization

Peter Dayan, Chris Dyer, Timothy P. Lillicrap, 2018, ICML.

Meta-Learning with Memory-Augmented Neural Networks

Daan Wierstra, Matthew Botvinick, Timothy P. Lillicrap, 2016, ICML.

Mastering the game of Go without human knowledge

Demis Hassabis, Karen Simonyan, Fan Hui, 2017, Nature.

One-shot Learning with Memory-Augmented Neural Networks pdf

Daan Wierstra, Matthew Botvinick, Timothy P. Lillicrap, 2016, ArXiv.

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

Demis Hassabis, Karen Simonyan, David Silver, 2019, Nature.

Random feedback weights support learning in deep neural networks pdf

Daniel Cownden, Timothy P. Lillicrap, Colin J. Akerman, 2014, ArXiv.

StarCraft II: A New Challenge for Reinforcement Learning pdf

Tom Schaul, Oriol Vinyals, Karen Simonyan, 2017, ArXiv.

Towards deep learning with segregated dendrites

Timothy P Lillicrap, Blake A. Richards, Blake A Richards, 2016, eLife.

Experience Replay for Continual Learning

David Rolnick, Jonathan Schwarz, Arun Ahuja, 2018, NeurIPS.

Discovering objects and their relations from entangled scene representations pdf

Razvan Pascanu, Timothy P. Lillicrap, Adam Santoro, 2017, ICLR.

Relevance Realization and the Emerging Framework in Cognitive Science

Timothy P. Lillicrap, Blake A. Richards, John Vervaeke, 2012, J. Log. Comput..

Compressive Transformers for Long-Range Sequence Modelling pdf

Timothy P. Lillicrap, Siddhant M. Jayakumar, Anna Potapenko, 2019, ICLR.

Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017

Tom Schaul, Nando de Freitas, Shane Legg, 2017, 1711.08378.

Dream to Control: Learning Behaviors by Latent Imagination pdf

Mohammad Norouzi, Jimmy Ba, Danijar Hafner, 2019, ICLR.

LOGAN: Latent Optimisation for Generative Adversarial Networks pdf

Yan Wu, Jeff Donahue, Karen Simonyan, 2019, ArXiv.

Random synaptic feedback weights support error backpropagation for deep learning

Colin J. Akerman, Douglas B. Tweed, Timothy P. Lillicrap, 2016, Nature Communications.

Imitating Interactive Intelligence pdf

Felix Hill, Nikolay Savinov, Arun Ahuja, 2020, ArXiv.

Episodic Curiosity through Reachability pdf

Marc Pollefeys, Nikolay Savinov, Timothy Lillicrap, 2018, ICLR.

Tournament Why Copy Others ? Insights from the Social Learning Strategies

T. Lillicrap, M. Feldman, K. Laland, 2022 .

Why Copy Others? Insights from the Social Learning Strategies Tournament

T. Lillicrap, M. Feldman, K. Laland, 2010, Science.

Rotational dynamics in motor cortex are consistent with a feedback controller

Egidio Falotico, Philip N. Sabes, Stephen H. Scott, 2020, bioRxiv.

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

Christopher C. Pack, Blake A. Richards, Patrick J. Mineault, 2021, bioRxiv.

Temporal evolution of "automatic gain-scaling".

J. A. Pruszynski, T. Lillicrap, S. Scott, 2009, Journal of neurophysiology.

Neurons of Primary Motor Cortex Torque-Related Activity in Upper Arm Muscles and Nonuniform Distribution of Reach-Related and

B. T. Marsh, Nasir H. Bhanpuri, T. Lillicrap, 2015 .

Clonal Relationships Impact Neuronal Tuning within a Phylogenetically Ancient Vertebrate Brain Structure

Blake A. Richards, T. Lillicrap, Alistair Muldal, 2014, Current Biology.

Primary motor cortex neurons classified in a postural task predict muscle activation patterns in a reaching task.

Stephen H Scott, J Andrew Pruszynski, Mohsen Omrani, 2016, Journal of neurophysiology.

Supplementary materials for : “ Adaptation to inversion of the visual field : a new twist on an old problem . ”

T. Lillicrap, N. Troje, D. Tweed, 2013 .

Temporal evolution of both premotor and motor cortical tuning properties reflect changes in limb biomechanics.

Nicholas G Hatsopoulos, Aaron J Suminski, Timothy P Lillicrap, 2015, Journal of neurophysiology.

Double-stranded RNA as a not-self alarm signal: to evade, most viruses purine-load their RNAs, but some (HTLV-1, Epstein-Barr) pyrimidine-load.

T. Lillicrap, J. Mortimer, D. Forsdyke, 2001, Journal of theoretical biology.

Dream to Control: Learning Behaviors by Latent Imagination

Jimmy Ba, T. Lillicrap, Mohammad Norouzi, 2019, ICLR.

INCLUSIVE FITNESS ANALYSIS ON MATHEMATICAL GROUPS

T. Lillicrap, P. Taylor, D. Cownden, 2011, Evolution; international journal of organic evolution.

Retrieval-Augmented Reinforcement Learning

Nan Rosemary Ke, Adrià Puigdomènech Badia, T. Lillicrap, 2022, ICML.

Evaluating Multimodal Interactive Agents

Tamara von Glehn, Blake A. Richards, Petko Georgiev, 2022, ArXiv.

Intra-agent speech permits zero-shot task acquisition

Petko Georgiev, T. Lillicrap, Greg Wayne, 2022, NeurIPS.

Outlook A B Data : Brain : Potential Principles : Architecture : Learning Rules : Machine Learning Anatomy : Plasticity Rules : Systems

Konrad Paul Kording, T. Lillicrap, 2019 .

Automated curricula through setter-solver interactions

Andrew Kyle Lampinen, David P. Reichert, T. Lillicrap, 2019, ArXiv.

and its contribution to generalizable EMG predictions Movement representation in the primary motor cortex

T. Lillicrap, M. Nicolelis, M. Lebedev, 2015 .

Upper-Limb Muscles Complex Spatiotemporal Tuning in Human

J. A. Pruszynski, T. Lillicrap, S. Scott, 2015 .

Evaluating Multimodal Interactive Agents

Tamara von Glehn, Blake A. Richards, Petko Georgiev, 2022, ArXiv.

Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution

Eero P. Simoncelli, Doris Y. Tsao, Adam H. Marblestone, 2022, ArXiv.

Large-Scale Retrieval for Reinforcement Learning

L. Sifre, T. Lillicrap, A. Guez, 2022, ArXiv.

Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning

Tamara von Glehn, DeepMind Interactive Agents Team Josh Abramson, Petko Georgiev, 2021, ArXiv.

Evaluating Long-Term Memory in 3D Mazes

T. Lillicrap, Danijar Hafner, J. Pašukonis, 2022, ICLR.

Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback

Tamara von Glehn, Petko Georgiev, T. Lillicrap, 2022, ArXiv.

BCI learning phenomena can be explained by gradient-based optimization

T. Lillicrap, M. Botvinick, Kayvon Daie, 2022, bioRxiv.

A data-driven approach for learning to control computers

Petko Georgiev, Tobias Pohlen, T. Lillicrap, 2022, ICML.

Nonhuman Primates Kinematics and Kinetics of Multijoint Reaching in

Stephen Scott, Nicholas G. Hatsopoulos, Natalia Dounskaia, 2015 .

Mastering Diverse Domains through World Models

Jimmy Ba, T. Lillicrap, Danijar Hafner, 2023, ArXiv.

Equilibrium Aggregation: Encoding Sets via Optimization

T. Lillicrap, Sergey Bartunov, F. Fuchs, 2022, UAI.

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

Everton J. Agnes, Friedemann Zenke, Tim P. Vogels, 2020, bioRxiv.

Dendritic solutions to the credit assignment problem

Timothy P Lillicrap, Blake A. Richards, Blake A Richards, 2019, Current Opinion in Neurobiology.

Automated curriculum generation through setter-solver interactions

Andrew K. Lampinen, Andrew Kyle Lampinen, Sébastien Racanière, 2020, ICLR.

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

Peter C Humphreys, Felix Hill, Arun Ahuja, 2021, ArXiv.

Continuous Latent Search for Combinatorial Optimization

T. Lillicrap, Vinod Nair, P. Battaglia, 2021 .

Meta-Learning Deep Energy-Based Memory Models

Jack W. Rae, T. Lillicrap, Simon Osindero, 2019, ICLR.

Backpropagation through time and the brain

Timothy P Lillicrap, Adam Santoro, T. Lillicrap, 2019, Current Opinion in Neurobiology.

The Kanerva Machine: A Generative Distributed Memory pdf

Alex Graves, Yan Wu, Greg Wayne, 2018, ICLR.

Learning Latent Dynamics for Planning from Pixels pdf

Ruben Villegas, Honglak Lee, James Davidson, 2018, ICML.

Mastering Atari with Discrete World Models pdf

Mohammad Norouzi, Jimmy Ba, Danijar Hafner, 2020, ICLR.

Signals for Action Planning Visual Ventral Premotor Cortex During Processing of Differential Involvement of Neurons in the Dorsal and

T. Lillicrap, J. Tanji, N. Hatsopoulos, 2015 .

Adapting to inversion of the visual field: a new twist on an old problem

Nikolaus F. Troje, Timothy P. Lillicrap, Juan Fernandez-Ruiz, 2013, Experimental Brain Research.

A simple neural network module for relational reasoning

Razvan Pascanu, Timothy P. Lillicrap, Adam Santoro, 2017, NIPS.

A deep learning framework for neuroscience

Surya Ganguli, Yoshua Bengio, Claudia Clopath, 2019, Nature Neuroscience.

Backpropagation and the brain

T. Lillicrap, Adam Santoro, Luke Marris, 2020, Nature Reviews Neuroscience.

Complex spatiotemporal tuning in human upper-limb muscles.

Stephen H Scott, Timothy P Lillicrap, J Andrew Pruszynski, 2010, Journal of neurophysiology.

Temporal Encoding of Movement in Motor Cortical Neurons

J. A. Pruszynski, T. Lillicrap, I. Kurtzer, 2007, Journal of Neuroscience.

Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights

Timothy P Lillicrap, Douglas B Tweed, Arash Samadi, 2017, Neural Computation.

Deep reinforcement learning with relational inductive biases

Razvan Pascanu, Murray Shanahan, Yujia Li, 2018, ICLR.

Meta-Learning Neural Bloom Filters

Sergey Bartunov, Jack W. Rae, Timothy Lillicrap, 2019, ICML.

Learning from unexpected events in the neocortical microcircuit

Yazan N. Billeh, Blake A. Richards, Jennifer A. Luviano, 2021, bioRxiv.

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

Blake A. Richards, T. Lillicrap, B. Richards, 2022, Frontiers in Computer Science.

Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution

Eero P. Simoncelli, Doris Y. Tsao, Adam H. Marblestone, 2022, ArXiv.

Symbolic Behaviour in Artificial Intelligence pdf

Andrew Kyle Lampinen, Timothy Lillicrap, Adam Santoro, 2021, ArXiv.

Deep Reinforcement Learning in Large Discrete Action Spaces

Richard Evans, Peter Sunehag, Timothy A. Mann, 2015, 1512.07679.

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

Sergey Levine, Richard E. Turner, Zoubin Ghahramani, 2016, ICLR.

Deep Compressed Sensing

Timothy Lillicrap, Yan Wu, Mihaela Rosca, 2019, ICML.

Catalyzing next-generation Artificial Intelligence through NeuroAI

Eero P. Simoncelli, Konrad Paul Kording, Blake A. Richards, 2023, Nature Communications.

An investigation of model-free planning pdf

Tom Eccles, Laurent Orseau, David Silver, 2019, ICML.

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

Richard E. Turner, Zoubin Ghahramani, Bernhard Scholkopf, 2017, NIPS.

Adapting to inversion of the visual field: a new twist on an old problem

T. Lillicrap, N. Troje, D. Tweed, 2013, Experimental Brain Research.

Generative Temporal Models with Memory pdf

David Amos, Greg Wayne, Chia-Chun Hung, 2017, ArXiv.

Responses of pyramidal cell somata and apical dendrites in mouse visual cortex over multiple days

Yazan N. Billeh, Jennifer A. Luviano, Thuyanh V. Nguyen, 2023, Scientific data.

Using Weight Mirrors to Improve Feedback Alignment

Timothy Lillicrap, Peter C. Humphreys, Douglas Tweed, 2019 .

Under review as a conference paper at ICLR 2017 D ISCOVERING OBJECTS AND THEIR RELATIONS FROM ENTANGLED SCENE REPRESENTATIONS

T. Lillicrap, P. Battaglia, David Raposo, 2016 .

Noise Contrastive Priors for Functional Uncertainty

T. Lillicrap, A. Irpan, Danijar Hafner, 2018, UAI.

Large-Scale Retrieval for Reinforcement Learning

L. Sifre, T. Lillicrap, A. Guez, 2022, NeurIPS.

Data-efficient Deep Reinforcement Learning for Dexterous Manipulation

Martin A. Riedmiller, T. Lillicrap, N. Heess, 2017, ArXiv.

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

Andrew Jaegle, Nicolas Heess, Lars Buesing, 2020, ArXiv.

Physically Embedded Planning Problems: New Challenges for Reinforcement Learning pdf

Andrew Jaegle, Jonathan J. Hunt, Nicolas Heess, 2020, ArXiv.

Meta-Learning Neural Bloom Filters

Jack W. Rae, T. Lillicrap, Sergey Bartunov, 2019, ICML.

Training Generative Adversarial Networks by Solving Ordinary Differential Equations pdf

Jeff Donahue, Jost Tobias Springenberg, Chongli Qin, 2020, NeurIPS.

On the Stability and Scalability of Node Perturbation Learning

T. Lillicrap, P. Latham, Naoki Hiratani, 2022, NeurIPS.

Towards Biologically Plausible Convolutional Networks pdf

Peter E. Latham, Timothy P. Lillicrap, Roman Pogodin, 2021, NeurIPS.

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

Chris J. Maddison, George van den Driessche, Aja Huang, 2016, Nature.

Gemini: A Family of Highly Capable Multimodal Models

Tara N. Sainath, Dalia El Badawy, Reinald Kim Amplayo, 2023, ArXiv.

dm_control: Software and Tasks for Continuous Control

Yuval Tassa, Steven Bohez, Nicolas Heess, 2020, Softw. Impacts.

Biologically feasible deep learning with segregated dendrites

Jordan Guergiuev, Blake A. Richards, Timothy Lillicrap, 2016 .

Deep learning with segregated dendrites

Timothy P. Lillicrap, Jordan Guergiuev, Blake A. Richards, 2016 .

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

Neil C. Rabinowitz, T. Lillicrap, K. Kavukcuoglu, 2018, Nature.

Equilibrium Aggregation: Encoding Sets via Optimization

T. Lillicrap, Sergey Bartunov, F. Fuchs, 2022, UAI.