Timothy Lillicrap
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Computer Science
Why Is Timothy Lillicrap Influential?
(Suggest an Edit or Addition)According to Wikipedia, Timothy P. Lillicrap is a Canadian neuroscientist and AI researcher, adjunct professor at University College London, and staff research scientist at Google DeepMind, where he has been involved in the AlphaGo and AlphaZero projects mastering the games of Go, Chess and Shogi. His research focuses on machine learning and statistics for optimal control and decision making, as well as using these mathematical frameworks to understand how the brain learns. He has developed algorithms and approaches for exploiting deep neural networks in the context of reinforcement learning, and new recurrent memory architectures for one-shot learning.
Timothy Lillicrap's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Mastering the game of Go with deep neural networks and tree search (2016) (13400)
- Continuous control with deep reinforcement learning (2015) (8827)
- Mastering the game of Go without human knowledge (2017) (7165)
- Asynchronous Methods for Deep Reinforcement Learning (2016) (6519)
- Matching Networks for One Shot Learning (2016) (5050)
- A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play (2018) (2281)
- Grandmaster level in StarCraft II using multi-agent reinforcement learning (2019) (2227)
- A simple neural network module for relational reasoning (2017) (1374)
- Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm (2017) (1211)
- Meta-Learning with Memory-Augmented Neural Networks (2016) (1195)
- Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates (2016) (1148)
- Mastering Atari, Go, chess and shogi by planning with a learned model (2019) (1081)
- Learning Latent Dynamics for Planning from Pixels (2018) (866)
- Continuous Deep Q-Learning with Model-based Acceleration (2016) (862)
- StarCraft II: A New Challenge for Reinforcement Learning (2017) (651)
- Dream to Control: Learning Behaviors by Latent Imagination (2019) (649)
- Random synaptic feedback weights support error backpropagation for deep learning (2016) (599)
- A deep learning framework for neuroscience (2019) (509)
- One-shot Learning with Memory-Augmented Neural Networks (2016) (500)
- Learning Continuous Control Policies by Stochastic Value Gradients (2015) (478)
- Vector-based navigation using grid-like representations in artificial agents (2018) (455)
- Experience Replay for Continual Learning (2018) (432)
- Deep Reinforcement Learning in Large Discrete Action Spaces (2015) (413)
- Backpropagation and the brain (2020) (403)
- DeepMind Control Suite (2018) (367)
- Distributed Distributional Deterministic Policy Gradients (2018) (333)
- Mastering Atari with Discrete World Models (2020) (316)
- Why Copy Others? Insights from the Social Learning Strategies Tournament (2010) (311)
- Towards deep learning with segregated dendrites (2016) (305)
- Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic (2016) (302)
- Compressive Transformers for Long-Range Sequence Modelling (2019) (255)
- Measuring abstract reasoning in neural networks (2018) (253)
- Learning to Learn without Gradient Descent by Gradient Descent (2016) (243)
- Memory-based control with recurrent neural networks (2015) (236)
- Data-efficient Deep Reinforcement Learning for Dexterous Manipulation (2017) (216)
- Episodic Curiosity through Reachability (2018) (206)
- Learning and Transfer of Modulated Locomotor Controllers (2016) (191)
- Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures (2018) (185)
- Relational recurrent neural networks (2018) (182)
- Relational Deep Reinforcement Learning (2018) (174)
- dm_control: Software and Tasks for Continuous Control (2020) (159)
- Unsupervised Predictive Memory in a Goal-Directed Agent (2018) (159)
- Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes (2016) (144)
- Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning (2017) (142)
- Deep reinforcement learning with relational inductive biases (2018) (142)
- Random feedback weights support learning in deep neural networks (2014) (140)
- Temporal evolution of "automatic gain-scaling". (2009) (128)
- Deep Compressed Sensing (2019) (116)
- Discovering objects and their relations from entangled scene representations (2017) (107)
- Preference Distributions of Primary Motor Cortex Neurons Reflect Control Solutions Optimized for Limb Biomechanics (2013) (107)
- Dendritic solutions to the credit assignment problem (2019) (90)
- Optimizing agent behavior over long time scales by transporting value (2018) (85)
- Deep Reinforcement Learning for Robotic Manipulation (2016) (84)
- Deep Learning without Weight Transport (2019) (84)
- Backpropagation through time and the brain (2019) (81)
- An investigation of model-free planning (2019) (80)
- Noise Contrastive Priors for Functional Uncertainty (2018) (72)
- LOGAN: Latent Optimisation for Generative Adversarial Networks (2019) (69)
- Learning to Make Analogies by Contrasting Abstract Relational Structure (2019) (67)
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors (2018) (65)
- Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights (2017) (61)
- Generative Temporal Models with Memory (2017) (57)
- Recall Traces: Backtracking Models for Efficient Reinforcement Learning (2018) (56)
- Towards Principled Unsupervised Learning (2015) (46)
- Fast Parametric Learning with Activation Memorization (2018) (44)
- Sensitivity Derivatives for Flexible Sensorimotor Learning (2008) (43)
- Adapting to inversion of the visual field: a new twist on an old problem (2013) (41)
- What does it mean to understand a neural network? (2019) (37)
- Imitating Interactive Intelligence (2020) (37)
- 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. (2001) (36)
- Learning to Learn for Global Optimization of Black Box Functions (2016) (36)
- Automated curricula through setter-solver interactions (2019) (34)
- The Kanerva Machine: A Generative Distributed Memory (2018) (34)
- The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning (2021) (30)
- Building machines that learn and think for themselves (2017) (29)
- Meta-Learning Deep Energy-Based Memory Models (2019) (29)
- INCLUSIVE FITNESS ANALYSIS ON MATHEMATICAL GROUPS (2011) (27)
- Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning (2021) (26)
- Meta-Learning Neural Bloom Filters (2019) (26)
- Automated curriculum generation through setter-solver interactions (2020) (25)
- Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution (2022) (22)
- Rotational dynamics in motor cortex are consistent with a feedback controller (2020) (22)
- Composing Entropic Policies using Divergence Correction (2018) (22)
- Learning from unexpected events in the neocortical microcircuit (2021) (22)
- Symbolic Behaviour in Artificial Intelligence (2021) (21)
- Training Generative Adversarial Networks by Solving Ordinary Differential Equations (2020) (21)
- Relevance Realization and the Emerging Framework in Cognitive Science (2012) (20)
- Mastering Diverse Domains through World Models (2023) (19)
- Temporal evolution of both premotor and motor cortical tuning properties reflect changes in limb biomechanics. (2015) (19)
- A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network (2020) (17)
- Using Weight Mirrors to Improve Feedback Alignment (2019) (16)
- Learning Attractor Dynamics for Generative Memory (2018) (16)
- Primary motor cortex neurons classified in a postural task predict muscle activation patterns in a reaching task. (2016) (16)
- A data-driven approach for learning to control computers (2022) (15)
- Retrieval-Augmented Reinforcement Learning (2022) (15)
- Towards Biologically Plausible Convolutional Networks (2021) (13)
- Clonal Relationships Impact Neuronal Tuning within a Phylogenetically Ancient Vertebrate Brain Structure (2014) (10)
- Physically Embedded Planning Problems: New Challenges for Reinforcement Learning (2020) (9)
- Deep learning with segregated dendrites (2016) (9)
- Complex spatiotemporal tuning in human upper-limb muscles. (2010) (9)
- Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback (2022) (8)
- The Brain-Computer Metaphor Debate Is Useless: A Matter of Semantics (2022) (8)
- Biologically feasible deep learning with segregated dendrites (2016) (7)
- Temporal Encoding of Movement in Motor Cortical Neurons (2007) (6)
- Learning Strategies Tournament Why Copy Others ? Insights from the Social (6)
- Catalyzing next-generation Artificial Intelligence through NeuroAI (2023) (6)
- Can neocortical feedback alter the sign of plasticity? (2018) (6)
- Intra-agent speech permits zero-shot task acquisition (2022) (5)
- Author response: Towards deep learning with segregated dendrites (2017) (4)
- Large-Scale Retrieval for Reinforcement Learning (2022) (3)
- Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban (2020) (3)
- Entropic Policy Composition with Generalized Policy Improvement and Divergence Correction (2018) (2)
- Tournament Why Copy Others ? Insights from the Social Learning Strategies (2)
- Continuous Latent Search for Combinatorial Optimization (2021) (2)
- Is coding a relevant metaphor for building AI? (2019) (2)
- Evaluating Multimodal Interactive Agents (2022) (1)
- BCI learning phenomena can be explained by gradient-based optimization (2022) (1)
- Equilibrium Aggregation: Encoding Sets via Optimization (2022) (1)
- Multielectrode Arrays Single-Unit Stability Using Chronically Implanted (2015) (0)
- Supplementary material for : Temporal evolution of ‘ automatic gain scaling ’ . (2009) (0)
- and its contribution to generalizable EMG predictions Movement representation in the primary motor cortex (2015) (0)
- Supplementary: Training Generative Adversarial Networks by Solving Ordinary Differential Equations (2020) (0)
- Evolution 2010 Inclusive fitness analysis on mathematical groups Supplementary Materials (2010) (0)
- Cells in Dorsal Premotor Cortex and Parietal Area 5 Different Arm Orientations. II. Activity of Individual Reaching Movements With Similar Hand Paths but (2015) (0)
- Signals for Action Planning Visual Ventral Premotor Cortex During Processing of Differential Involvement of Neurons in the Dorsal and (2015) (0)
- Upper-Limb Muscles Complex Spatiotemporal Tuning in Human (2015) (0)
- Evaluating Long-Term Memory in 3D Mazes (2022) (0)
- Learning sensitivity derivative by implicit supervision (2007) (0)
- AreaVersus Limb Trajectory in Dorsal Premotor Preferential Representation of Instructed Target (2015) (0)
- Adapting to inversion of the visual field: a new twist on an old problem (2013) (0)
- Outlook A B Data : Brain : Potential Principles : Architecture : Learning Rules : Machine Learning Anatomy : Plasticity Rules : Systems (2019) (0)
- Neurons of Primary Motor Cortex Torque-Related Activity in Upper Arm Muscles and Nonuniform Distribution of Reach-Related and (2015) (0)
- Vector-based navigation using grid-like representations in artificial agents (2018) (0)
- On the Stability and Scalability of Node Perturbation Learning (2022) (0)
- An alternative to explicit divisive normalization models (2012) (0)
- Unsupervised learning is crucial to learning the names of objects (2007) (0)
- Nonhuman Primates Kinematics and Kinetics of Multijoint Reaching in (2015) (0)
- Supplementary materials for : “ Adaptation to inversion of the visual field : a new twist on an old problem . ” (2013) (0)
- Can neocortical feedback alter the sign of plasticity? (2018) (0)
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