| 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, 2015 | 727 | 2015 |
| Regularizing neural networks by penalizing confident output distributions G Pereyra, G Tucker, J Chorowski, Ł Kaiser, G Hinton arXiv preprint arXiv:1701.06548, 2017 | 465 | 2017 |
| 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 | 333 | 2015 |
| 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 | 311 | 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 | 281 | 2014 |
| Model-based reinforcement learning for atari L Kaiser, M Babaeizadeh, P Milos, B Osinski, RH Campbell, ... arXiv preprint arXiv:1903.00374, 2019 | 196 | 2019 |
| Soft Co-Clustering of Data FW Elliott, R Rohwer, SC Jones, GJ Tucker, CJ Kain, CN Weidert US Patent App. 12/133,902, 2009 | 196 | 2009 |
| 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 | 192 | 2017 |
| On variational bounds of mutual information B Poole, S Ozair, A Oord, AA Alemi, G Tucker arXiv preprint arXiv:1905.06922, 2019 | 140* | 2019 |
| 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 | 120* | 2018 |
| 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 | 110 | 2018 |
| 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 | 110 | 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, ... Science signaling 4 (196), rs10-rs10, 2011 | 95 | 2011 |
| Learning to walk via deep reinforcement learning T Haarnoja, S Ha, A Zhou, J Tan, G Tucker, S Levine arXiv preprint arXiv:1812.11103, 2018 | 91 | 2018 |
| Methods and devices for ignoring similar audio being received by a system AD Rosen, MJ Rodehorst, GJ Tucker, ALM Challenner US Patent 9,728,188, 2017 | 90 | 2017 |
| Stabilizing off-policy q-learning via bootstrapping error reduction A Kumar, J Fu, M Soh, G Tucker, S Levine Advances in Neural Information Processing Systems, 11784-11794, 2019 | 76 | 2019 |
| 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 | 74 | 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 | 66 | 2016 |
| 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 | 64 | 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 | 58 | 2016 |
Vinayagam ArunachalamSenior Principal Scientist, PfizerVerifierad e-postadress på pfizer.com
Vinayagam ArunachalamSenior Principal Scientist, PfizerVerifierad e-postadress på pfizer.com