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"

8 / 8 papers shown

Title
Robust Classification by Coupling Data Mollification with Label Smoothing Markus Heinonen Ba-Hien Tran Michael Kampffmeyer Maurizio Filippone 65 0 0 03 Jun 2024
Cold Posteriors through PAC-Bayes Konstantinos Pitas Julyan Arbel 21 5 0 22 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
Regularising for invariance to data augmentation improves supervised learning Aleksander Botev Matthias Bauer Soham De 30 14 0 07 Mar 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
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