| Simple and scalable predictive uncertainty estimation using deep ensembles B Lakshminarayanan, A Pritzel, C Blundell Advances in Neural Information Processing Systems, 6393-6395, 2017 | 3578 | 2017 |
| Clinically applicable deep learning for diagnosis and referral in retinal disease J De Fauw, JR Ledsam, B Romera-Paredes, S Nikolov, N Tomasev, ... Nature medicine 24 (9), 1342-1350, 2018 | 1728 | 2018 |
| 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, ... NeurIPS 2019, 2019 | 1039 | 2019 |
| Normalizing Flows for Probabilistic Modeling and Inference G Papamakarios, E Nalisnick, DJ Rezende, S Mohamed, ... Journal of Machine Learning Research 22 (57), 1-64, 2021 | 780 | 2021 |
| AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty D Hendrycks, N Mu, ED Cubuk, B Zoph, J Gilmer, B Lakshminarayanan ICLR 2020, 2020 | 671 | 2020 |
| Do Deep Generative Models Know What They Don't Know? E Nalisnick, A Matsukawa, YW Teh, D Gorur, B Lakshminarayanan ICLR 2019, 2019 | 526 | 2019 |
| Likelihood ratios for out-of-distribution detection J Ren, PJ Liu, E Fertig, J Snoek, R Poplin, M Depristo, J Dillon, ... NeurIPS 2019, 14707-14718, 2019 | 462 | 2019 |
| Learning in Implicit Generative Models S Mohamed, B Lakshminarayanan arXiv preprint arXiv:1610.03483, 2016 | 381 | 2016 |
| Deep Ensembles: A Loss Landscape Perspective S Fort, H Hu, B Lakshminarayanan arXiv preprint arXiv:1912.02757, 2019 | 342 | 2019 |
| The Cramer Distance as a Solution to Biased Wasserstein Gradients MG Bellemare, I Danihelka, W Dabney, S Mohamed, ... arXiv preprint arXiv:1705.10743, 2017 | 332 | 2017 |
| Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach F Briggs, B Lakshminarayanan, L Neal, XZ Fern, R Raich, SJK Hadley, ... The Journal of the Acoustical Society of America 131 (6), 4640-4650, 2012 | 318 | 2012 |
| Variational Approaches for Auto-Encoding Generative Adversarial Networks M Rosca, B Lakshminarayanan, D Warde-Farley, S Mohamed arXiv preprint arXiv:1706.04987, 2017 | 283 | 2017 |
| Mondrian forests: Efficient online random forests B Lakshminarayanan, DM Roy, YW Teh Advances in neural information processing systems 27, 3140-3148, 2014 | 241 | 2014 |
| Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step W Fedus, M Rosca, B Lakshminarayanan, AM Dai, S Mohamed, ... ICLR 2018, 2018 | 203 | 2018 |
| Simple and principled uncertainty estimation with deterministic deep learning via distance awareness J Liu, Z Lin, S Padhy, D Tran, T Bedrax Weiss, B Lakshminarayanan Advances in Neural Information Processing Systems 33, 2020 | 184 | 2020 |
| 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 | 137 | 2020 |
| Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality E Nalisnick, A Matsukawa, YW Teh, B Lakshminarayanan arXiv preprint arXiv:1906.02994, 2019 | 130 | 2019 |
| Training independent subnetworks for robust prediction M Havasi, R Jenatton, S Fort, JZ Liu, J Snoek, B Lakshminarayanan, ... ICLR 2021, 2021 | 105 | 2021 |
| Adapting auxiliary losses using gradient similarity Y Du, WM Czarnecki, SM Jayakumar, M Farajtabar, R Pascanu, ... arXiv preprint arXiv:1812.02224, 2018 | 102 | 2018 |
| Exploring the limits of out-of-distribution detection S Fort, J Ren, B Lakshminarayanan Advances in Neural Information Processing Systems 34, 2021 | 93 | 2021 |
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk
