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, 7498-7512, 2020 | 490 | 2020 |
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 | 238 | 2020 |
A simple fix to mahalanobis distance for improving near-ood detection J Ren, S Fort, J Liu, AG Roy, S Padhy, B Lakshminarayanan arXiv preprint arXiv:2106.09022, 2021 | 206 | 2021 |
Uncertainty baselines: Benchmarks for uncertainty & robustness in deep learning Z Nado, N Band, M Collier, J Djolonga, MW Dusenberry, S Farquhar, ... arXiv preprint arXiv:2106.04015, 2021 | 111 | 2021 |
Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer's disease and mild cognitive impairment CF Liu, S Padhy, S Ramachandran, VX Wang, A Efimov, A Bernal, L Shi, ... Magnetic resonance imaging 64, 190-199, 2019 | 82 | 2019 |
A simple approach to improve single-model deep uncertainty via distance-awareness JZ Liu, S Padhy, J Ren, Z Lin, Y Wen, G Jerfel, Z Nado, J Snoek, D Tran, ... Journal of Machine Learning Research 24 (42), 1-63, 2023 | 50 | 2023 |
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks S Padhy, Z Nado, J Ren, J Liu, J Snoek, B Lakshminarayanan arXiv preprint arXiv:2007.05134, 2020 | 48 | 2020 |
Sampling-based inference for large linear models, with application to linearised Laplace J Antorán, S Padhy, R Barbano, E Nalisnick, D Janz, ... The Twelfth International Conference on Learning Representations, 2023, 2022 | 23 | 2022 |
Stochastic solutions to rough surface scattering using the finite element method UK Khankhoje, S Padhy IEEE Transactions on Antennas and Propagation 65 (8), 4170-4180, 2017 | 22 | 2017 |
Sampling from Gaussian process posteriors using stochastic gradient descent JA Lin, J Antorán, S Padhy, D Janz, JM Hernández-Lobato, A Terenin Advances in Neural Information Processing Systems 36, 36886-36912, 2023 | 20 | 2023 |
Transport meets Variational Inference: Controlled Monte Carlo Diffusions F Vargas, S Padhy, B Denis, N Nüsken The Twelfth International Conference on Learning Representations, 2024, 2023 | 18* | 2023 |
Kernel regression with infinite-width neural networks on millions of examples B Adlam, J Lee, S Padhy, Z Nado, J Snoek arXiv preprint arXiv:2303.05420, 2023 | 10 | 2023 |
Stochastic Gradient Descent for Gaussian Processes Done Right JA Lin, S Padhy, J Antorán, A Tripp, A Terenin, C Szepesvári, ... The Twelfth International Conference on Learning Representations, 2024, 2023 | 6 | 2023 |
DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised -transform A Denker, F Vargas, S Padhy, K Didi, S Mathis, V Dutordoir, R Barbano, ... NeurIPS, 2024 | 5 | 2024 |
Learning generative models with invariance to symmetries JU Allingham, J Antoran, S Padhy, E Nalisnick, JM Hernández-Lobato NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations, 2022 | 2 | 2022 |
A Generative Model of Symmetry Transformations JU Allingham, BK Mlodozeniec, S Padhy, J Antorán, D Krueger, ... NeurIPS, 2024 | 1 | 2024 |
Analyzing shape and residual pose of subcortical structures in brains of subjects with schizophrenia S Padhy Johns Hopkins University, 2019 | 1 | 2019 |
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes JA Lin, S Padhy, B Mlodozeniec, J Antorán, JM Hernández-Lobato NeurIPS, 2024 | | 2024 |
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes JA Lin, S Padhy, B Mlodozeniec, JM Hernández-Lobato Advances in Approximate Bayesian Inference, 2024 | | 2024 |
Very High Energy Ground Based Gamma Ray Telescopy Using TACTIC M Reddy, A Gupta, S Padhy, PR Hebbar arXiv preprint arXiv:1812.03429, 2018 | | 2018 |