Alexander Pritzel
Alexander Pritzel
Deepmind
Vahvistettu sähköpostiosoite verkkotunnuksessa google.com
Nimike
Viittaukset
Viittaukset
Vuosi
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
70842015
Simple and scalable predictive uncertainty estimation using deep ensembles
B Lakshminarayanan, A Pritzel, C Blundell
arXiv preprint arXiv:1612.01474, 2016
19382016
Deep exploration via bootstrapped DQN
I Osband, C Blundell, A Pritzel, B Van Roy
Advances in neural information processing systems 29, 4026-4034, 2016
8092016
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
4772017
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
3662018
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
3032021
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
2652017
Neural episodic control
A Pritzel, B Uria, S Srinivasan, AP Badia, O Vinyals, D Hassabis, ...
International Conference on Machine Learning, 2827-2836, 2017
2042017
Model-free episodic control
C Blundell, B Uria, A Pritzel, Y Li, A Ruderman, JZ Leibo, J Rae, ...
arXiv preprint arXiv:1606.04460, 2016
184*2016
Scrambling in the black hole portrait
G Dvali, D Flassig, C Gomez, A Pritzel, N Wintergerst
Physical Review D 88 (12), 124041, 2013
902013
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
742021
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
742020
Black holes and quantumness on macroscopic scales
D Flassig, A Pritzel, N Wintergerst
Physical Review D 87 (8), 084007, 2013
662013
Memory-based parameter adaptation
P Sprechmann, SM Jayakumar, JW Rae, A Pritzel, AP Badia, B Uria, ...
arXiv preprint arXiv:1802.10542, 2018
612018
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
602020
Computational predictions of protein structures associated with COVID-19
J Jumper, K Tunyasuvunakool, P Kohli, D Hassabis, A Team
DeepMind website, 2020
56*2020
Continuous control with deep reinforcement learning. arXiv
TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, Y Tassa, D Silver, ...
arXiv preprint arXiv:1509.02971, 2015
462015
On ghosts in theories of self-interacting massive spin-2 particles
S Folkerts, A Pritzel, N Wintergerst
arXiv preprint arXiv:1107.3157, 2011
462011
Topological model for domain walls in (super-) Yang-Mills theories
M Dierigl, A Pritzel
Physical Review D 90 (10), 105008, 2014
382014
Meta-learning by the baldwin effect
C Fernando, J Sygnowski, S Osindero, J Wang, T Schaul, D Teplyashin, ...
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2018
342018
Järjestelmä ei voi suorittaa toimenpidettä nyt. Yritä myöhemmin uudelleen.
Artikkelit 1–20