Spremljaj
George Tucker
George Tucker
Google DeepMind
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Leto
Soft actor-critic algorithms and applications
T Haarnoja, A Zhou, K Hartikainen, G Tucker, S Ha, J Tan, V Kumar, ...
arXiv preprint arXiv:1812.05905, 2018
32932018
Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ...
arXiv preprint arXiv:2312.11805, 2023
32702023
Offline reinforcement learning: Tutorial, review, and perspectives on open problems
S Levine, A Kumar, G Tucker, J Fu
arXiv preprint arXiv:2005.01643, 2020
2441*2020
Conservative q-learning for offline reinforcement learning
A Kumar, A Zhou, G Tucker, S Levine
NeurIPS 2020, 2020
21192020
Efficient Bayesian mixed-model analysis increases association power in large cohorts
PR Loh, G Tucker, BK Bulik-Sullivan, BJ Vilhjálmsson, HK Finucane, ...
Nature genetics 47 (3), 284-290, 2015
16502015
Regularizing neural networks by penalizing confident output distributions
G Pereyra, G Tucker, J Chorowski, Ł Kaiser, G Hinton
ICLR 2017 Workshop, 2017
13372017
D4rl: Datasets for deep data-driven reinforcement learning
J Fu, A Kumar, O Nachum, G Tucker, S Levine
arXiv preprint arXiv:2004.07219, 2020
13262020
Stabilizing off-policy q-learning via bootstrapping error reduction
A Kumar, J Fu, G Tucker, S Levine
NeurIPS 2019, 2019
12142019
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ...
arXiv preprint arXiv:2403.05530, 2024
12112024
Model-based reinforcement learning for atari
L Kaiser, M Babaeizadeh, P Milos, B Osinski, RH Campbell, ...
ICLR 2020 Spotlight, 2020
10782020
Gemma: Open models based on gemini research and technology
G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ...
arXiv preprint arXiv:2403.08295, 2024
10492024
On variational bounds of mutual information
B Poole, S Ozair, A Van Den Oord, A Alemi, G Tucker
International conference on machine learning, 5171-5180, 2019
10062019
Behavior regularized offline reinforcement learning
Y Wu, G Tucker, O Nachum
arXiv preprint arXiv:1911.11361, 2019
8232019
Widespread macromolecular interaction perturbations in human genetic disorders
N Sahni, S Yi, M Taipale, JIF Bass, J Coulombe-Huntington, F Yang, ...
Cell 161 (3), 647-660, 2015
6072015
Learning to walk via deep reinforcement learning
T Haarnoja, S Ha, A Zhou, J Tan, G Tucker, S Levine
RSS 2019, 2019
5862019
Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling
C Riquelme, G Tucker, J Snoek
ICLR 2018, 2018
476*2018
A quantitative chaperone interaction network reveals the architecture of cellular protein homeostasis pathways
M Taipale, G Tucker, J Peng, I Krykbaeva, ZY Lin, B Larsen, H Choi, ...
Cell 158 (2), 434-448, 2014
4692014
Sample-efficient reinforcement learning with stochastic ensemble value expansion
J Buckman, D Hafner, G Tucker, E Brevdo, H Lee
NeurIPS 2018 Oral, 2018
4242018
Don't blame the elbo! a linear vae perspective on posterior collapse
J Lucas, G Tucker, RB Grosse, M Norouzi
Advances in Neural Information Processing Systems 32, 2019
376*2019
Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models
G Tucker, A Mnih, CJ Maddison, D Lawson, J Sohl-Dickstein
NIPS 2017 Oral, 2017
3682017
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