Jasper Snoek

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Ryan P. AdamsPrinceton UniversityDirección de correo verificada de princeton.edu
Hugo LarochelleGoogle Brain & MilaDirección de correo verificada de google.com
Kevin SwerskyGoogle BrainDirección de correo verificada de cs.toronto.edu
Alex MihailidisProfessor, Department of Occupational Science & Occupational Therapy, University of TorontoDirección de correo verificada de utoronto.ca
Babak TaatiScientist & Assistant Professor, KITE | Toronto Rehab - UHN & University of TorontoDirección de correo verificada de uhn.ca
Oren RippelMIT, Harvard UniversityDirección de correo verificada de mit.edu
David R KelleyCalico LabsDirección de correo verificada de calicolabs.com
Richard ZemelProfessor of Computer Science, University of TorontoDirección de correo verificada de cs.toronto.edu
Frank HutterProfessor of Computer Science, University of Freiburg, GermanyDirección de correo verificada de cs.uni-freiburg.de
PrabhatLawrence Berkeley National Lab; UC BerkeleyDirección de correo verificada de lbl.gov
Michael (Mike) GelbartUniversity of British ColumbiaDirección de correo verificada de cs.ubc.ca
Kevin Leyton-BrownProfessor, Computer Science, University of British Columbia; Canada CIFAR AI ChairDirección de correo verificada de cs.ubc.ca
James BergstraDirección de correo verificada de uwaterloo.ca
Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British ColumbiaDirección de correo verificada de cs.rwth-aachen.de
David DuvenaudAssistant Professor, University of TorontoDirección de correo verificada de cs.toronto.edu
Jesse HoeyProfessor of Computer Science, University of WaterlooDirección de correo verificada de cs.uwaterloo.ca
Ahmad AklPhD Candidate, University of TorontoDirección de correo verificada de utoronto.ca
Michael A OsborneProfessor of Machine Learning, University of OxfordDirección de correo verificada de robots.ox.ac.uk
Shamim NematiUCSDDirección de correo verificada de ucsd.edu
Mohammad Mahdi GhassemiMichigan State UniversityDirección de correo verificada de msu.edu

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Jasper Snoek

Google Brain

Dirección de correo verificada de google.com

Machine Learning


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Practical bayesian optimization of machine learning algorithms J Snoek, H Larochelle, RP Adams Advances in Neural Information Processing Systems, 2012	6547	2012
Scalable bayesian optimization using deep neural networks J Snoek, O Rippel, K Swersky, R Kiros, N Satish, N Sundaram, M Patwary, ... International conference on machine learning, 2015	838	2015
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift Y Ovadia, E Fertig, J Ren, Z Nado, D Sculley, S Nowozin, JV Dillon, ... Advances in Neural Information Processing Systems, 2019	781	2019
Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks DR Kelley, J Snoek, JL Rinn Genome research 26 (7), 990-999, 2016	746	2016
Multi-task bayesian optimization K Swersky, J Snoek, RP Adams Advances in Neural Information Processing Systems, 2013	628	2013
Bayesian optimization with unknown constraints MA Gelbart, J Snoek, RP Adams Uncertainty in Artificial Intelligence, 2014	373	2014
Towards an empirical foundation for assessing bayesian optimization of hyperparameters K Eggensperger, M Feurer, F Hutter, J Bergstra, J Snoek, H Hoos, ... NIPS workshop on Bayesian Optimization in Theory and Practice 10 (3), 2013	342	2013
Likelihood ratios for out-of-distribution detection J Ren, PJ Liu, E Fertig, J Snoek, R Poplin, MA DePristo, JV Dillon, ... Advances in Neural Information Processing Systems, 2019	327	2019
Spectral representations for convolutional neural networks O Rippel, J Snoek, RP Adams Advances in Neural Information Processing Systems, 2015	280	2015
Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling C Riquelme, G Tucker, J Snoek International Conference on Learning Representations, 2018	254	2018
Freeze-thaw Bayesian optimization K Swersky, J Snoek, RP Adams arXiv preprint arXiv:1406.3896, 2014	227	2014
Sequential regulatory activity prediction across chromosomes with convolutional neural networks DR Kelley, YA Reshef, M Bileschi, D Belanger, CY McLean, J Snoek Genome research 28 (5), 739-750, 2018	216	2018
Input warping for Bayesian optimization of non-stationary functions J Snoek, K Swersky, R Zemel, R Adams International Conference on Machine Learning, 1674-1682, 2014	210	2014
How good is the bayes posterior in deep neural networks really? F Wenzel, K Roth, BS Veeling, J Świątkowski, L Tran, S Mandt, J Snoek, ... International Conference on Machine Learning, 2020	164	2020
Learning latent permutations with gumbel-sinkhorn networks G Mena, D Belanger, S Linderman, J Snoek International Conference on Learning Representations, 2018	148	2018
Winner's curse? On pace, progress, and empirical rigor D Sculley, J Snoek, A Wiltschko, A Rahimi International Conference on Learning Representations Workshops, 2018	145*	2018
Efficient and scalable bayesian neural nets with rank-1 factors M Dusenberry, G Jerfel, Y Wen, Y Ma, J Snoek, K Heller, ... International conference on machine learning, 2782-2792, 2020	106	2020
Machine learning approaches in cardiovascular imaging M Henglin, G Stein, PV Hushcha, J Snoek, AB Wiltschko, S Cheng Circulation: Cardiovascular Imaging 10 (10), e005614, 2017	91	2017
Hyperparameter ensembles for robustness and uncertainty quantification F Wenzel, J Snoek, D Tran, R Jenatton Advances in Neural Information Processing Systems, 2020	84	2020
Raiders of the lost architecture: Kernels for Bayesian optimization in conditional parameter spaces K Swersky, D Duvenaud, J Snoek, F Hutter, MA Osborne NeurIPS workshop on Bayesian Optimization in Theory and Practice, 2013	82	2013

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