| Learning Latent Dynamics for Planning from Pixels D Hafner, T Lillicrap, I Fischer, R Villegas, D Ha, H Lee, J Davidson Proceedings of the International Conference on Machine Learning (ICML), 2019 | 920 | 2019 |
| Dream to Control: Learning Behaviors by Latent Imagination D Hafner, T Lillicrap, J Ba, M Norouzi International Conference on Learning Representations (ICLR), 2019 | 631 | 2019 |
| Sim-to-Real: Learning Agile Locomotion for Quadruped Robots J Tan, T Zhang, E Coumans, A Iscen, Y Bai, D Hafner, S Bohez, ... Proceedings of Robotics: Science and Systems (RSS), 2018 | 600 | 2018 |
| A Deep Learning Framework for Neuroscience BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ... Nature Neuroscience 22 (11), 2019 | 564 | 2019 |
| Mastering Atari with Discrete World Models D Hafner, T Lillicrap, M Norouzi, J Ba International Conference on Learning Representations (ICLR), 2020 | 277 | 2020 |
| Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion J Buckman, D Hafner, G Tucker, E Brevdo, H Lee Advances in Neural Information Processing Systems (NeurIPS), 2018 | 260 | 2018 |
| Planning to Explore via Self-Supervised World Models R Sekar, O Rybkin, K Daniilidis, P Abbeel, D Hafner, D Pathak Proceedings of the International Conference on Machine Learning (ICML), 2020 | 189 | 2020 |
| Bayesian Layers: A Module for Neural Network Uncertainty D Tran, M Dusenberry, M van der Wilk, D Hafner Advances in Neural Information Processing Systems (NeurIPS), 2019 | 95 | 2019 |
| Sophisticated Inference K Friston, L Da Costa, D Hafner, C Hesp, T Parr Neural Computation 33 (3), 713-763, 2021 | 72 | 2021 |
| Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors D Hafner, D Tran, T Lillicrap, A Irpan, J Davidson Uncertainty in Artificial Intelligence (UAI), 2018 | 64 | 2018 |
| Noise Contrastive Priors for Functional Uncertainty D Hafner, D Tran, T Lillicrap, A Irpan, J Davidson Uncertainty in Artificial Intelligence (UAI), 2019 | 58 | 2019 |
| TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow D Hafner, J Davidson, V Vanhoucke Technical report, 2017 | 53 | 2017 |
| Action and Perception as Divergence Minimization D Hafner, PA Ortega, J Ba, T Parr, K Friston, N Heess arXiv preprint arXiv:2009.01791, 2020 | 37 | 2020 |
| Discovering and Achieving Goals via World Models R Mendonca, O Rybkin, K Daniilidis, D Hafner, D Pathak Proceedings of the International Conference on Machine Learning (ICML), 2021 | 32 | 2021 |
| TensorFlow for Machine Intelligence S Abrahams, D Hafner, E Erwitt, A Scarpinelli Bleeding Edge Press, 2016 | 31 | 2016 |
| Clockwork Variational Autoencoders V Saxena, J Ba, D Hafner Advances in Neural Information Processing Systems (NeurIPS), 2021 | 24 | 2021 |
| Benchmarking the Spectrum of Agent Capabilities D Hafner International Conference on Learning Representations (ICLR), 2021 | 23 | 2021 |
| Latent Skill Planning for Exploration and Transfer K Xie, H Bharadhwaj, D Hafner, A Garg, F Shkurti International Conference on Learning Representations (ICLR), 2021 | 17* | 2021 |
| Modulated Policy Hierarchies A Pashevich, D Hafner, J Davidson, R Sukthankar, C Schmid Deep Reinforcement Learning Workshop (DRLW), 2018 | 13 | 2018 |
| Learning Hierarchical Information Flow with Recurrent Neural Modules D Hafner, A Irpan, J Davidson, N Heess Advances in Neural Information Processing Systems (NIPS), 2017 | 12 | 2017 |
Timothy P. LillicrapSenior Research Scientist, Google DeepMindVerified email at google.com
