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
430 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
205 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
160 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
101 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
75 2019 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
44 2020 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
36 2023 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-based inference for large linear models, with application to linearised Laplace J Antorán, S Padhy, R Barbano, E Nalisnick, D Janz, ...
arXiv preprint arXiv:2210.04994, 2022
15 2022 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
11 2023 Transport meets Variational Inference: Controlled Monte Carlo Diffusions F Vargas, S Padhy, B Denis, N Nüsken
arXiv preprint arXiv:2307.01050, 2023
10 * 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
8 2023 Stochastic Gradient Descent for Gaussian Processes Done Right JA Lin, S Padhy, J Antorán, A Tripp, A Terenin, C Szepesvári, ...
arXiv preprint arXiv:2310.20581, 2023
3 2023 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 DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised A Denker, F Vargas, S Padhy, K Didi, S Mathis, V Dutordoir, R Barbano, ...
arXiv preprint arXiv:2406.01781, 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
arXiv preprint arXiv:2405.18457, 2024
2024 Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes JA Lin, S Padhy, B Mlodozeniec, JM Hernández-Lobato
arXiv preprint arXiv:2405.18328, 2024
2024 A Generative Model of Symmetry Transformations JU Allingham, BK Mlodozeniec, S Padhy, J Antorán, D Krueger, ...
arXiv preprint arXiv:2403.01946, 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 
Balaji LakshminarayananSenior Staff Research Scientist at Google DeepMindDirección de correo verificada de google.com
