עקוב אחר
Jasper Snoek
Jasper Snoek
Google Brain
כתובת אימייל מאומתת בדומיין google.com
כותרת
צוטט על ידי
צוטט על ידי
שנה
Practical bayesian optimization of machine learning algorithms‏
J Snoek, H Larochelle, RP Adams‏
Advances in Neural Information Processing Systems, 2012‏
107502012
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
22492023
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
19132019
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
13472015
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
10802016
Multi-task bayesian optimization‏
K Swersky, J Snoek, RP Adams‏
Advances in Neural Information Processing Systems, 2013‏
9482013
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
7872019
Bayesian optimization with unknown constraints‏
MA Gelbart, J Snoek, RP Adams‏
Uncertainty in Artificial Intelligence, 2014‏
6842014
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
4782018
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
4732013
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
4602018
Spectral representations for convolutional neural networks‏
O Rippel, J Snoek, RP Adams‏
Advances in Neural Information Processing Systems, 2015‏
4192015
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
4022020
Second opinion needed: communicating uncertainty in medical machine learning‏
B Kompa, J Snoek, AL Beam‏
NPJ Digital Medicine 4 (1), 4, 2021‏
3272021
Freeze-thaw Bayesian optimization‏
K Swersky, J Snoek, RP Adams‏
arXiv preprint arXiv:1406.3896, 2014‏
3252014
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
2942014
Learning latent permutations with gumbel-sinkhorn networks‏
G Mena, D Belanger, S Linderman, J Snoek‏
International Conference on Learning Representations, 2018‏
2852018
Hyperparameter ensembles for robustness and uncertainty quantification‏
F Wenzel, J Snoek, D Tran, R Jenatton‏
Advances in Neural Information Processing Systems, 2020‏
2492020
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
2422020
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
2382020
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מאמרים 1–20