| 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 | 12008 | 2015 |
| Highly accurate protein structure prediction with AlphaFold J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... Nature 596 (7873), 583-589, 2021 | 10504 | 2021 |
| Simple and scalable predictive uncertainty estimation using deep ensembles B Lakshminarayanan, A Pritzel, C Blundell Advances in neural information processing systems 30, 2017 | 3887 | 2017 |
| Highly accurate protein structure prediction for the human proteome K Tunyasuvunakool, J Adler, Z Wu, T Green, M Zielinski, A Žídek, ... Nature 596 (7873), 590-596, 2021 | 1218 | 2021 |
| Deep exploration via bootstrapped DQN I Osband, C Blundell, A Pritzel, B Van Roy Advances in neural information processing systems 29, 2016 | 1165 | 2016 |
| Pathnet: Evolution channels gradient descent in super neural networks C Fernando, D Banarse, C Blundell, Y Zwols, D Ha, AA Rusu, A Pritzel, ... arXiv preprint arXiv:1701.08734, 2017 | 722 | 2017 |
| Protein complex prediction with AlphaFold-Multimer R Evans, M O’Neill, A Pritzel, N Antropova, A Senior, T Green, A Žídek, ... BioRxiv, 2021.10. 04.463034, 2021 | 680 | 2021 |
| Vector-based navigation using grid-like representations in artificial agents A Banino, C Barry, B Uria, C Blundell, T Lillicrap, P Mirowski, A Pritzel, ... Nature 557 (7705), 429-433, 2018 | 569 | 2018 |
| Darla: Improving zero-shot transfer in reinforcement learning I Higgins, A Pal, A Rusu, L Matthey, C Burgess, A Pritzel, M Botvinick, ... International Conference on Machine Learning, 1480-1490, 2017 | 405 | 2017 |
| Neural episodic control A Pritzel, B Uria, S Srinivasan, AP Badia, O Vinyals, D Hassabis, ... International conference on machine learning, 2827-2836, 2017 | 330 | 2017 |
| Model-free episodic control C Blundell, B Uria, A Pritzel, Y Li, A Ruderman, JZ Leibo, J Rae, ... arXiv preprint arXiv:1606.04460, 2016 | 254* | 2016 |
| Never give up: Learning directed exploration strategies AP Badia, P Sprechmann, A Vitvitskyi, D Guo, B Piot, S Kapturowski, ... arXiv preprint arXiv:2002.06038, 2020 | 202 | 2020 |
| Applying and improving AlphaFold at CASP14 J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... Proteins: Structure, Function, and Bioinformatics 89 (12), 1711-1721, 2021 | 146 | 2021 |
| High accuracy protein structure prediction using deep learning J Jumper, R Evans, A Pritzel, T Green, M Figurnov, K Tunyasuvunakool, ... Fourteenth Critical Assessment of Techniques for Protein Structure …, 2020 | 131 | 2020 |
| Scrambling in the black hole portrait G Dvali, D Flassig, C Gomez, A Pritzel, N Wintergerst Physical Review D 88 (12), 124041, 2013 | 103 | 2013 |
| Memory-based parameter adaptation P Sprechmann, SM Jayakumar, JW Rae, A Pritzel, AP Badia, B Uria, ... arXiv preprint arXiv:1802.10542, 2018 | 87 | 2018 |
| Computational predictions of protein structures associated with COVID-19 J Jumper, K Tunyasuvunakool, P Kohli, D Hassabis, A Team DeepMind website, 2020 | 73* | 2020 |
| Black holes and quantumness on macroscopic scales D Flassig, A Pritzel, N Wintergerst Physical Review D 87 (8), 084007, 2013 | 68 | 2013 |
| Targeted free energy estimation via learned mappings P Wirnsberger, AJ Ballard, G Papamakarios, S Abercrombie, S Racanière, ... The Journal of Chemical Physics 153 (14), 144112, 2020 | 52 | 2020 |
| 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 | 50 | 2015 |
Charles BlundellResearch Scientist at DeepMind在 google.com 的电子邮件经过验证
