Stebėti
Nando de Freitas
Nando de Freitas
CIFAR & DeepMind
Patvirtintas el. paštas google.com - Pagrindinis puslapis
Pavadinimas
Cituota
Cituota
Metai
Sequential Monte Carlo methods in practice
A Doucet, N De Freitas, N Gordon
Springer Verlag, 2001
11856*2001
Taking the human out of the loop: A review of Bayesian optimization
B Shahriari, K Swersky, Z Wang, RP Adams, N De Freitas
Proceedings of the IEEE 104 (1), 148-175, 2015
57962015
Dueling network architectures for deep reinforcement learning
Z Wang, T Schaul, M Hessel, H Hasselt, M Lanctot, N Freitas
International conference on machine learning, 1995-2003, 2016
53512016
An introduction to MCMC for machine learning
C Andrieu, N De Freitas, A Doucet, MI Jordan
Machine learning 50, 5-43, 2003
36592003
A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning
E Brochu, VM Cora, N De Freitas
arXiv preprint arXiv:1012.2599, 2010
34772010
The unscented particle filter
R Van Der Merwe, A Doucet, N De Freitas, E Wan
Advances in neural information processing systems 13, 2000
25592000
Learning to learn by gradient descent by gradient descent
M Andrychowicz, M Denil, S Gomez, MW Hoffman, D Pfau, T Schaul, ...
Advances in neural information processing systems 29, 2016
24432016
Object recognition as machine translation: learning a lexicon for a fixed image vocabulary Leture Noyes in Computer Science
P Duygulu, K Barnard, JFG de Freitas
Heidelberg: Springer 23 (53), 97-112, 2002
2334*2002
Learning to communicate with deep multi-agent reinforcement learning
J Foerster, IA Assael, N De Freitas, S Whiteson
Advances in neural information processing systems 29, 2016
21512016
Matching words and pictures
K Barnard, P Duygulu, D Forsyth, N De Freitas, DM Blei, MI Jordan
The Journal of Machine Learning Research 3, 1107-1135, 2003
21152003
Rao-Blackwellised particle filtering for dynamic Bayesian networks
K Murphy, S Russell
Sequential Monte Carlo methods in practice, 499-515, 2001
20922001
Predicting parameters in deep learning
M Denil, B Shakibi, L Dinh, MA Ranzato, N De Freitas
Advances in neural information processing systems 26, 2013
16692013
A boosted particle filter: Multitarget detection and tracking
K Okuma, A Taleghani, N De Freitas, JJ Little, DG Lowe
Computer Vision-ECCV 2004: 8th European Conference on Computer Vision …, 2004
15722004
Sample efficient actor-critic with experience replay
Z Wang, V Bapst, N Heess, V Mnih, R Munos, K Kavukcuoglu, ...
arXiv preprint arXiv:1611.01224, 2016
10432016
Competition-level code generation with alphacode
Y Li, D Choi, J Chung, N Kushman, J Schrittwieser, R Leblond, T Eccles, ...
Science 378 (6624), 1092-1097, 2022
10032022
A generalist agent
S Reed, K Zolna, E Parisotto, SG Colmenarejo, A Novikov, G Barth-Maron, ...
arXiv preprint arXiv:2205.06175, 2022
9302022
Bayesian optimization in a billion dimensions via random embeddings
Z Wang, F Hutter, M Zoghi, D Matheson, N De Feitas
Journal of Artificial Intelligence Research 55, 361-387, 2016
8712016
Social influence as intrinsic motivation for multi-agent deep reinforcement learning
N Jaques, A Lazaridou, E Hughes, C Gulcehre, P Ortega, DJ Strouse, ...
International conference on machine learning, 3040-3049, 2019
5432019
Neural programmer-interpreters
S Reed, N De Freitas
arXiv preprint arXiv:1511.06279, 2015
5412015
Lipnet: End-to-end sentence-level lipreading
YM Assael, B Shillingford, S Whiteson, N De Freitas
arXiv preprint arXiv:1611.01599, 2016
4832016
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Straipsniai 1–20