Jasper Snoek - Google Scholar Citations

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Co-authors

Ryan P. AdamsPrinceton UniversityVerified email at princeton.edu
Hugo LarochelleGoogle BrainVerified email at usherbrooke.ca
Kevin SwerskyGoogle BrainVerified email at cs.toronto.edu
Alex MihailidisProfessor, Department of Occupational Science & Occupational Therapy, University of TorontoVerified email at utoronto.ca
Babak TaatiScientist & Assistant Professor, Toronto Rehabilitation Institute - UHN & University of TorontoVerified email at uhn.ca
Oren RippelMIT, Harvard UniversityVerified email at mit.edu
Richard ZemelProfessor of Computer Science, University of TorontoVerified email at cs.toronto.edu
David R KelleyCalico LabsVerified email at calicolabs.com
Frank HutterProfessor of Computer Science, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
PrabhatLawrence Berkeley National Lab; UC BerkeleyVerified email at lbl.gov
Jesse HoeyAssociate Professor of Computer Science, University of WaterlooVerified email at cs.uwaterloo.ca
Kevin Leyton-BrownProfessor of Computer Science, University of British Columbia; Canada CIFAR AI Chair, AmiiVerified email at cs.ubc.ca
James BergstraVerified email at uwaterloo.ca
Holger HoosLeiden University, The Netherlands & University of British Columbia, CanadaVerified email at liacs.nl
Michael (Mike) GelbartUniversity of British ColumbiaVerified email at cs.ubc.ca
David DuvenaudAssistant Professor, University of TorontoVerified email at cs.toronto.edu
Shamim NematiUCSDVerified email at ucsd.edu
Mohammad GhassemiScientist, The Massachusetts Institute of TechnologyVerified email at mit.edu
Michael A OsborneAssociate Professor, University of OxfordVerified email at robots.ox.ac.uk
Ahmad AklPhD Candidate, University of TorontoVerified email at utoronto.ca

Jasper Snoek

Google Brain

Verified email at google.com - Homepage

Machine Learning


Title	Cited by	Year
Practical bayesian optimization of machine learning algorithms J Snoek, H Larochelle, RP Adams Advances in neural information processing systems, 2951-2959, 2012	2775	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, 2171-2180, 2015	347	2015
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	329	2016
Multi-task bayesian optimization K Swersky, J Snoek, RP Adams Advances in neural information processing systems, 2004-2012, 2013	295	2013
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	163	2013
Bayesian optimization with unknown constraints MA Gelbart, J Snoek, RP Adams arXiv preprint arXiv:1403.5607, 2014	147	2014
Spectral representations for convolutional neural networks O Rippel, J Snoek, RP Adams Advances in neural information processing systems, 2449-2457, 2015	139	2015
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	126	2014
Freeze-thaw Bayesian optimization K Swersky, J Snoek, RP Adams arXiv preprint arXiv:1406.3896, 2014	114	2014
Automated detection of unusual events on stairs J Snoek, J Hoey, L Stewart, RS Zemel, A Mihailidis Image and Vision Computing 27 (1-2), 153-166, 2009	71	2009
Winner's curse? On pace, progress, and empirical rigor D Sculley, J Snoek, A Wiltschko, A Rahimi	55	2018
Towards a single sensor passive solution for automated fall detection M Belshaw, B Taati, J Snoek, A Mihailidis 2011 Annual International Conference of the IEEE Engineering in Medicine and …, 2011	52	2011
Nonparametric guidance of autoencoder representations using label information J Snoek, RP Adams, H Larochelle Journal of Machine Learning Research 13 (Sep), 2567-2588, 2012	50	2012
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	42	2018
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	40	2018
Learning latent permutations with gumbel-sinkhorn networks G Mena, D Belanger, S Linderman, J Snoek arXiv preprint arXiv:1802.08665, 2018	38	2018
Raiders of the lost architecture: Kernels for Bayesian optimization in conditional parameter spaces K Swersky, D Duvenaud, J Snoek, F Hutter, MA Osborne arXiv preprint arXiv:1409.4011, 2014	37	2014
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	32	2017
Bayesian optimization and semiparametric models with applications to assistive technology JR Snoek University of Toronto, 2013	31	2013
A determinantal point process latent variable model for inhibition in neural spiking data J Snoek, R Zemel, RP Adams Advances in Neural Information Processing Systems, 1932-1940, 2013	28	2013

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