Timothy Lillicrap

41 papers · 2015–2024 · 4 conferences · across top CS/AI conferences

Achievements

+13 more ↓

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🗺️ Taxonomy Completionist (10) 🌉 Interdisciplinary Bridge 🌍 Conference Polyglot (4)

🗺️ Taxonomy Completionist (10) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌟 Keyword Trendsetter Combo (4) 🤝 Dynamic Duo (11) 👑 Triple Crown 🏆 Keyword Champion (2) ⚡ Prolific Year (7) 🗃️ Keyword Collector (163) 📈 Trend Setter 💎 Century Club (41) 🚀 Conference Pioneer 🔥 Unstoppable (10)

Conferences

NIPS (19) ICML (12) ICLR (9) UAI (1)

Top co-authors

Adam Santoro (11) David Raposo (5) Gregory Wayne (5) Jack Rae (5) Yan Wu (4) Nicolas Heess (4) Sergey Bartunov (4) Theophane Weber (4) Daan Wierstra (3) Arthur Guez (3)

Keywords

neural network (7) continuous control (5) deep reinforcement learning (5) one-shot learning (4) model-based reinforcement learning (3) few-shot learning (3) reinforcement learning (3) experience retrieval (2) experience replay (2) feedback alignment (2) convolutional network (2) generative adversarial network (2) policy gradient (2) off-policy learning (2) relational reasoning (2) offline reinforcement learning (2) language modeling (2) variational inference (2) biologically plausible learning (2) memory-augmented neural network (2)

Papers

Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving NIPS 2024 AndroidInTheWild: A Large-Scale Dataset For Android Device Control NIPS 2023 On the Stability and Scalability of Node Perturbation Learning NIPS 2022 Intra-agent speech permits zero-shot task acquisition NIPS 2022 Large-Scale Retrieval for Reinforcement Learning NIPS 2022 A data-driven approach for learning to control computers ICML 2022 Retrieval-Augmented Reinforcement Learning ICML 2022 Towards Biologically Plausible Convolutional Networks NIPS 2021 The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning NIPS 2021 A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network NIPS 2020 Meta-Learning Deep Energy-Based Memory Models ICLR 2020 Dream to Control: Learning Behaviors by Latent Imagination ICLR 2020 Training Generative Adversarial Networks by Solving Ordinary Differential Equations NIPS 2020 Automated curriculum generation through setter-solver interactions ICLR 2020 Noise Contrastive Priors for Functional Uncertainty UAI 2019 Deep Learning without Weight Transport NIPS 2019 Experience Replay for Continual Learning NIPS 2019 Recall Traces: Backtracking Models for Efficient Reinforcement Learning ICLR 2019 Episodic Curiosity through Reachability ICLR 2019 Deep reinforcement learning with relational inductive biases ICLR 2019 Learning to Make Analogies by Contrasting Abstract Relational Structure ICLR 2019 An Investigation of Model-Free Planning ICML 2019 Learning Latent Dynamics for Planning from Pixels ICML 2019 Composing Entropic Policies using Divergence Correction ICML 2019 Meta-Learning Neural Bloom Filters ICML 2019 Deep Compressed Sensing ICML 2019 Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures NIPS 2018 Measuring abstract reasoning in neural networks ICML 2018 Relational recurrent neural networks NIPS 2018 Learning Attractor Dynamics for Generative Memory NIPS 2018 The Kanerva Machine: A Generative Distributed Memory ICLR 2018 Distributed Distributional Deterministic Policy Gradients ICLR 2018 Fast Parametric Learning with Activation Memorization ICML 2018 A simple neural network module for relational reasoning NIPS 2017 Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning NIPS 2017 Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes NIPS 2016 Continuous Deep Q-Learning with Model-based Acceleration ICML 2016 Matching Networks for One Shot Learning NIPS 2016 Asynchronous Methods for Deep Reinforcement Learning ICML 2016 Meta-Learning with Memory-Augmented Neural Networks ICML 2016 Learning Continuous Control Policies by Stochastic Value Gradients NIPS 2015