| Continuous control with deep reinforcement learning TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, Y Tassa, D Silver, ... arXiv preprint arXiv:1509.02971, 2015 | 18956 | 2015 |
| Deep reinforcement learning in large discrete action spaces G Dulac-Arnold, R Evans, H van Hasselt, P Sunehag, T Lillicrap, J Hunt, ... arXiv preprint arXiv:1512.07679, 2015 | 800 | 2015 |
| Successor features for transfer in reinforcement learning A Barreto, W Dabney, R Munos, JJ Hunt, T Schaul, HP van Hasselt, ... Advances in neural information processing systems 30, 2017 | 708 | 2017 |
| Continuous control with deep reinforcement learning. arXiv 2015 TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, Y Tassa, D Silver, ... arXiv preprint arXiv:1509.02971, 2015 | 648 | 2015 |
| Memory-based control with recurrent neural networks N Heess, JJ Hunt, TP Lillicrap, D Silver arXiv preprint arXiv:1512.04455, 2015 | 414 | 2015 |
| Diffusion policies as an expressive policy class for offline reinforcement learning Z Wang, JJ Hunt, M Zhou arXiv preprint arXiv:2208.06193, 2022 | 359 | 2022 |
| Scaling memory-augmented neural networks with sparse reads and writes J Rae, JJ Hunt, I Danihelka, T Harley, AW Senior, G Wayne, A Graves, ... Advances in Neural Information Processing Systems 29, 2016 | 191 | 2016 |
| The Option Keyboard: Combining Skills in Reinforcement Learning A Barreto, D Borsa, S Hou, G Comanici, E Aygün, P Hamel, D Toyama, ... Advances in Neural Information Processing Systems, 13031-13041, 2019 | 117 | 2019 |
| & Wierstra, D.(2015). Continuous control with deep reinforcement learning TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, Y Tassa arXiv preprint arXiv:1509.02971, 0 | 112 | |
| Spiking neuron classifier apparatus and methods using conditionally independent subsets JJ Hunt, O Sinyavskiy US Patent 9,195,934, 2015 | 54 | 2015 |
| Continuous control with deep reinforcement learning. CoRR abs/1509.02971 (2015) TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, Y Tassa, D Silver, ... arXiv preprint arXiv:1509.02971, 2015 | 46 | 2015 |
| Sparse coding can predict primary visual cortex receptive field changes induced by abnormal visual input JJ Hunt, P Dayan, GJ Goodhill PLoS computational biology 9 (5), e1003005, 2013 | 44 | 2013 |
| Composing entropic policies using divergence correction J Hunt, A Barreto, T Lillicrap, N Heess International Conference on Machine Learning, 2911-2920, 2019 | 38 | 2019 |
| Statistical structure of lateral connections in the primary visual cortex JJ Hunt, WH Bosking, GJ Goodhill Neural systems & circuits 1, 1-12, 2011 | 30 | 2011 |
| Hyperbolic deep reinforcement learning E Cetin, B Chamberlain, M Bronstein, JJ Hunt arXiv preprint arXiv:2210.01542, 2022 | 26 | 2022 |
| RecSys 2021 Challenge Workshop: Fairness-aware engagement prediction at scale on Twitter’s Home Timeline VW Anelli, S Kalloori, B Ferwerda, L Belli, A Tejani, F Portman, ... Proceedings of the 15th ACM Conference on Recommender Systems, 819-824, 2021 | 20 | 2021 |
| Sparse coding on the spot: Spontaneous retinal waves suffice for orientation selectivity JJ Hunt, M Ibbotson, GJ Goodhill Neural computation 24 (9), 2422-2433, 2012 | 19 | 2012 |
| Apparatus and methods for developing parallel networks using a general purpose programming language JJ Hunt, O Sinyavskiy US Patent 9,330,356, 2016 | 16 | 2016 |
| On the simulation of enzymatic digest patterns: The fragmentation of oligomeric and polymeric galacturonides by endo-polygalacturonase II JJ Hunt, R Cameron, MAK Williams Biochimica et Biophysica Acta (BBA)-General Subjects 1760 (11), 1696-1703, 2006 | 16 | 2006 |
| Augmenting neural networks with sparsely-accessed external memory I Danihelka, GD Wayne, FM Wang, ET Grefenstette, JW Rae, AB Graves, ... US Patent 11,151,443, 2021 | 15 | 2021 |
Timothy P. LillicrapDirector of Research, Google DeepMindVerified email at google.com
