George Tucker
George Tucker
Google Brain
Verified email at - Homepage
TitleCited byYear
Efficient Bayesian mixed-model analysis increases association power in large cohorts
PR Loh, G Tucker, BK Bulik-Sullivan, BJ Vilhjalmsson, HK Finucane, ...
Nature genetics 47 (3), 284, 2015
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
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
Regularizing neural networks by penalizing confident output distributions
G Pereyra, G Tucker, J Chorowski, Ł Kaiser, G Hinton
arXiv preprint arXiv:1701.06548, 2017
Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models
G Tucker, A Mnih, CJ Maddison, J Lawson, J Sohl-Dickstein
Advances in Neural Information Processing Systems, 2627-2636, 2017
Proteomic and functional genomic landscape of receptor tyrosine kinase and ras to extracellular signal–regulated kinase signaling
AA Friedman, G Tucker, R Singh, D Yan, A Vinayagam, Y Hu, R Binari, ...
Sci. Signal. 4 (196), rs10-rs10, 2011
Filtering variational objectives
CJ Maddison, J Lawson, G Tucker, N Heess, M Norouzi, A Mnih, A Doucet, ...
Advances in Neural Information Processing Systems, 6573-6583, 2017
Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach
P Meyer, T Cokelaer, D Chandran, KH Kim, PR Loh, G Tucker, M Lipson, ...
BMC systems biology 8 (1), 13, 2014
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
Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling
C Riquelme, G Tucker, J Snoek
arXiv preprint arXiv:1802.09127, 2018
Model Compression Applied to Small-Footprint Keyword Spotting.
G Tucker, M Wu, M Sun, S Panchapagesan, G Fu, S Vitaladevuni
INTERSPEECH, 1878-1882, 2016
Model-based reinforcement learning for atari
L Kaiser, M Babaeizadeh, P Milos, B Osinski, RH Campbell, ...
arXiv preprint arXiv:1903.00374, 2019
The mirage of action-dependent baselines in reinforcement learning
G Tucker, S Bhupatiraju, S Gu, RE Turner, Z Ghahramani, S Levine
arXiv preprint arXiv:1802.10031, 2018
Improving the power of GWAS and avoiding confounding from population stratification with PC-Select
G Tucker, AL Price, B Berger
Genetics 197 (3), 1045-1049, 2014
Max-pooling loss training of long short-term memory networks for small-footprint keyword spotting
M Sun, A Raju, G Tucker, S Panchapagesan, G Fu, A Mandal, ...
2016 IEEE Spoken Language Technology Workshop (SLT), 474-480, 2016
Sample-efficient reinforcement learning with stochastic ensemble value expansion
J Buckman, D Hafner, G Tucker, E Brevdo, H Lee
Advances in Neural Information Processing Systems, 8224-8234, 2018
On variational bounds of mutual information
B Poole, S Ozair, A Oord, AA Alemi, G Tucker
arXiv preprint arXiv:1905.06922, 2019
Learning to walk via deep reinforcement learning
T Haarnoja, A Zhou, S Ha, J Tan, G Tucker, S Levine
arXiv preprint arXiv:1812.11103, 2018
Two-variance-component model improves genetic prediction in family datasets
G Tucker, PR Loh, IM MacLeod, BJ Hayes, ME Goddard, B Berger, ...
The American Journal of Human Genetics 97 (5), 677-690, 2015
Doubly reparameterized gradient estimators for Monte Carlo objectives
G Tucker, D Lawson, S Gu, CJ Maddison
arXiv preprint arXiv:1810.04152, 2018
The system can't perform the operation now. Try again later.
Articles 1–20