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Jasper Snoek
Jasper Snoek
Google Brain
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Tytuł
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Cytowane przez
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Practical bayesian optimization of machine learning algorithms
J Snoek, H Larochelle, RP Adams
Advances in Neural Information Processing Systems, 2012
110822012
Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ...
arXiv preprint arXiv:2312.11805, 2023
25562023
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
19872019
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
13832015
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
11002016
Multi-task bayesian optimization
K Swersky, J Snoek, RP Adams
Advances in Neural Information Processing Systems, 2013
9672013
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
8122019
Bayesian optimization with unknown constraints
MA Gelbart, J Snoek, RP Adams
Uncertainty in Artificial Intelligence, 2014
7092014
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
5032018
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), 1-5, 2013
4782013
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
4752018
Spectral representations for convolutional neural networks
O Rippel, J Snoek, RP Adams
Advances in Neural Information Processing Systems, 2015
4322015
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
4182020
Second opinion needed: communicating uncertainty in medical machine learning
B Kompa, J Snoek, AL Beam
NPJ Digital Medicine 4 (1), 4, 2021
3522021
Freeze-thaw Bayesian optimization
K Swersky, J Snoek, RP Adams
arXiv preprint arXiv:1406.3896, 2014
3332014
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
3032014
Learning latent permutations with gumbel-sinkhorn networks
G Mena, D Belanger, S Linderman, J Snoek
International Conference on Learning Representations, 2018
2942018
Hyperparameter ensembles for robustness and uncertainty quantification
F Wenzel, J Snoek, D Tran, R Jenatton
Advances in Neural Information Processing Systems, 2020
2642020
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
2472020
Evaluating prediction-time batch normalization for robustness under covariate shift
Z Nado, S Padhy, D Sculley, A D'Amour, B Lakshminarayanan, J Snoek
arXiv preprint arXiv:2006.10963, 2020
2472020
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