| Practical bayesian optimization of machine learning algorithms J Snoek, H Larochelle, RP Adams Advances in Neural Information Processing Systems, 2012 | 7813 | 2012 |
| 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 | 1088 | 2019 |
| 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 | 1023 | 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 | 827 | 2016 |
| Multi-task bayesian optimization K Swersky, J Snoek, RP Adams Advances in Neural Information Processing Systems, 2013 | 730 | 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 | 484 | 2019 |
| Bayesian optimization with unknown constraints MA Gelbart, J Snoek, RP Adams Uncertainty in Artificial Intelligence, 2014 | 461 | 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 | 389 | 2013 |
| Spectral representations for convolutional neural networks O Rippel, J Snoek, RP Adams Advances in Neural Information Processing Systems, 2015 | 323 | 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 | 315 | 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 | 289 | 2018 |
| Freeze-thaw Bayesian optimization K Swersky, J Snoek, RP Adams arXiv preprint arXiv:1406.3896, 2014 | 259 | 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 | 254 | 2020 |
| 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 | 241 | 2014 |
| Learning latent permutations with gumbel-sinkhorn networks G Mena, D Belanger, S Linderman, J Snoek International Conference on Learning Representations, 2018 | 180 | 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 | 170* | 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 | 144 | 2020 |
| Second opinion needed: communicating uncertainty in medical machine learning B Kompa, J Snoek, AL Beam NPJ Digital Medicine 4 (1), 4, 2021 | 132 | 2021 |
| Hyperparameter ensembles for robustness and uncertainty quantification F Wenzel, J Snoek, D Tran, R Jenatton Advances in Neural Information Processing Systems, 2020 | 129 | 2020 |
| Training independent subnetworks for robust prediction M Havasi, R Jenatton, S Fort, JZ Liu, J Snoek, B Lakshminarayanan, ... International Conference on Learning Representations, 2021 | 118 | 2021 |
Ryan P. AdamsPrinceton University在 princeton.edu 的电子邮件经过验证
