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
53002015
Simple and scalable predictive uncertainty estimation using deep ensembles
B Lakshminarayanan, A Pritzel, C Blundell
arXiv preprint arXiv:1612.01474, 2016
12742016
Deep exploration via bootstrapped DQN
I Osband, C Blundell, A Pritzel, B Van Roy
arXiv preprint arXiv:1602.04621, 2016
6552016
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
3702017
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
3062018
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
2092017
Neural episodic control
A Pritzel, B Uria, S Srinivasan, AP Badia, O Vinyals, D Hassabis, ...
International Conference on Machine Learning, 2827-2836, 2017
1642017
Model-free episodic control
C Blundell, B Uria, A Pritzel, Y Li, A Ruderman, JZ Leibo, J Rae, ...
arXiv preprint arXiv:1606.04460, 2016
160*2016
Scrambling in the black hole portrait
G Dvali, D Flassig, C Gomez, A Pritzel, N Wintergerst
Physical Review D 88 (12), 124041, 2013
802013
Black holes and quantumness on macroscopic scales
D Flassig, A Pritzel, N Wintergerst
Physical Review D 87 (8), 084007, 2013
622013
Memory-based parameter adaptation
P Sprechmann, SM Jayakumar, JW Rae, A Pritzel, AP Badia, B Uria, ...
arXiv preprint arXiv:1802.10542, 2018
512018
On ghosts in theories of self-interacting massive spin-2 particles
S Folkerts, A Pritzel, N Wintergerst
arXiv preprint arXiv:1107.3157, 2011
462011
Computational predictions of protein structures associated with COVID-19
J Jumper, K Tunyasuvunakool, P Kohli, D Hassabis, A Team
DeepMind website, 2020
37*2020
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
29*2020
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
282018
Topological model for domain walls in (super-) Yang-Mills theories
M Dierigl, A Pritzel
Physical Review D 90 (10), 105008, 2014
272014
Fast deep reinforcement learning using online adjustments from the past
S Hansen, P Sprechmann, A Pritzel, A Barreto, C Blundell
arXiv preprint arXiv:1810.08163, 2018
192018
Generative temporal models with spatial memory for partially observed environments
M Fraccaro, D Rezende, Y Zwols, A Pritzel, SMA Eslami, F Viola
International Conference on Machine Learning, 1549-1558, 2018
162018
Large-N ground state of the Lieb-Liniger model and Yang-Mills theory on a two-sphere
D Flassig, A Franca, A Pritzel
Physical Review A 93 (1), 013627, 2016
162016
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
142020
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Artikkelit 1–20