Jasper Snoek
Jasper Snoek
Google Brain
Verified email at google.com
Cited by
Cited by
Practical bayesian optimization of machine learning algorithms
J Snoek, H Larochelle, RP Adams
arXiv preprint arXiv:1206.2944, 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
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
Multi-task bayesian optimization
K Swersky, J Snoek, RP Adams
Curran Associates, Inc., 2013
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, ...
arXiv preprint arXiv:1906.02530, 2019
Bayesian optimization with unknown constraints
MA Gelbart, J Snoek, RP Adams
arXiv preprint arXiv:1403.5607, 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
Spectral representations for convolutional neural networks
O Rippel, J Snoek, RP Adams
arXiv preprint arXiv:1506.03767, 2015
Freeze-thaw bayesian optimization
K Swersky, J Snoek, RP Adams
arXiv preprint arXiv:1406.3896, 2014
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
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
Likelihood ratios for out-of-distribution detection
J Ren, PJ Liu, E Fertig, J Snoek, R Poplin, MA DePristo, JV Dillon, ...
arXiv preprint arXiv:1906.02845, 2019
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
Winner's curse? On pace, progress, and empirical rigor
D Sculley, J Snoek, A Wiltschko, A Rahimi
Learning latent permutations with gumbel-sinkhorn networks
G Mena, D Belanger, S Linderman, J Snoek
arXiv preprint arXiv:1802.08665, 2018
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
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
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
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
Advances in neural information processing systems
J Snoek, H Larochelle, RP Adams
Red Hook: Curran Associates, Inc, 2012
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