Data augmentation in Bayesian neural networks and the cold posterior effect

10 June 2021

Papers citing "Data augmentation in Bayesian neural networks and the cold posterior effect"

32 / 32 papers shown

Title
Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo Hyunsu Kim G. Nam Chulhee Yun Hongseok Yang Juho Lee BDL UQCV 52 0 0 02 Mar 2025
Noether's razor: Learning Conserved Quantities Tycho F. A. van der Ouderaa Mark van der Wilk Pim de Haan 14 0 0 10 Oct 2024
Temperature Optimization for Bayesian Deep Learning Kenyon Ng Chris van der Heide Liam Hodgkinson Susan Wei BDL 33 0 0 08 Oct 2024
Robust Classification by Coupling Data Mollification with Label Smoothing Markus Heinonen Ba-Hien Tran Michael Kampffmeyer Maurizio Filippone 65 0 0 03 Jun 2024
Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models Emre Onal Klemens Flöge Emma Caldwell A. Sheverdin Vincent Fortuin UQCV BDL 37 9 0 06 May 2024
On Uncertainty Quantification for Near-Bayes Optimal Algorithms Ziyu Wang Chris Holmes UQCV 34 2 0 28 Mar 2024
Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation Yidong Zhao João Tourais Iain Pierce Christian Nitsche T. Treibel Sebastian Weingartner Artur M. Schweidtmann Qian Tao BDL UQCV 25 5 0 04 Mar 2024
Can a Confident Prior Replace a Cold Posterior? Martin Marek Brooks Paige Pavel Izmailov UQCV BDL 24 4 0 02 Mar 2024
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel ... David Rügamer Yee Whye Teh Max Welling Andrew Gordon Wilson Ruqi Zhang UQCV BDL 40 27 0 01 Feb 2024
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks Alexander M¨ollers Alexander Immer Elvin Isufi Vincent Fortuin SSL BDL UQCV 21 1 0 30 Nov 2023
If there is no underfitting, there is no Cold Posterior Effect Yijie Zhang Yi-Shan Wu Luis A. Ortega A. Masegosa UQCV 21 1 0 02 Oct 2023
A Primer on Bayesian Neural Networks: Review and Debates Federico Danieli Konstantinos Pitas M. Vladimirova Vincent Fortuin BDL AAML 56 18 0 28 Sep 2023
The fine print on tempered posteriors Konstantinos Pitas Julyan Arbel 25 1 0 11 Sep 2023
Improving Neural Additive Models with Bayesian Principles Kouroche Bouchiat Alexander Immer Hugo Yèche Gunnar Rätsch Vincent Fortuin BDL MedIm 26 6 0 26 May 2023
Hyperparameter Optimization through Neural Network Partitioning Bruno Mlodozeniec M. Reisser Christos Louizos 29 6 0 28 Apr 2023
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization Agustinus Kristiadi Alexander Immer Runa Eschenhagen Vincent Fortuin BDL UQCV 15 8 0 17 Apr 2023
Incorporating Unlabelled Data into Bayesian Neural Networks Mrinank Sharma Tom Rainforth Yee Whye Teh Vincent Fortuin SSL UQCV BDL 34 9 0 04 Apr 2023
Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study Tom Huix Szymon Majewski Alain Durmus Eric Moulines Anna Korba BDL 8 6 0 08 Jul 2022
Cold Posteriors through PAC-Bayes Konstantinos Pitas Julyan Arbel 21 5 0 22 Jun 2022
Learning Continuous Rotation Canonicalization with Radial Beam Sampling J. Schmidt Sebastian Stober 17 1 0 21 Jun 2022
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning Javier Antorán David Janz J. Allingham Erik A. Daxberger Riccardo Barbano Eric T. Nalisnick José Miguel Hernández-Lobato UQCV BDL 25 28 0 17 Jun 2022
How Tempering Fixes Data Augmentation in Bayesian Neural Networks Gregor Bachmann Lorenzo Noci Thomas Hofmann BDL AAML 72 8 0 27 May 2022
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification Sanyam Kapoor Wesley J. Maddox Pavel Izmailov A. Wilson BDL UD 21 48 0 30 Mar 2022
Regularising for invariance to data augmentation improves supervised learning Aleksander Botev Matthias Bauer Soham De 30 14 0 07 Mar 2022
UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography Francisca Vasconcelos Bobby He Nalini Singh Yee Whye Teh BDL OOD UQCV 24 12 0 22 Feb 2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations Alexander Immer Tycho F. A. van der Ouderaa Gunnar Rätsch Vincent Fortuin Mark van der Wilk BDL 24 44 0 22 Feb 2022
Weight Pruning and Uncertainty in Radio Galaxy Classification Devina Mohan A. Scaife UQCV 10 0 0 23 Nov 2021
Bayesian neural network unit priors and generalized Weibull-tail property M. Vladimirova Julyan Arbel Stéphane Girard BDL 54 9 0 06 Oct 2021
Last Layer Marginal Likelihood for Invariance Learning Pola Schwobel Martin Jørgensen Sebastian W. Ober Mark van der Wilk BDL UQCV 13 28 0 14 Jun 2021
The Promises and Pitfalls of Deep Kernel Learning Sebastian W. Ober C. Rasmussen Mark van der Wilk UQCV BDL 13 106 0 24 Feb 2021
Improving Transformation Invariance in Contrastive Representation Learning Adam Foster Rattana Pukdee Tom Rainforth 51 22 0 19 Oct 2020
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes Sebastian W. Ober Laurence Aitchison BDL 13 60 0 17 May 2020