Optimizing Data Augmentation through Bayesian Model Selection

27 May 2025

Papers citing "Optimizing Data Augmentation through Bayesian Model Selection"

46 / 46 papers shown

Title
Deep Learning is Not So Mysterious or Different Andrew Gordon Wilson 93 6 0 03 Mar 2025
Robust Classification by Coupling Data Mollification with Label Smoothing Markus Heinonen Ba-Hien Tran Michael Kampffmeyer Maurizio Filippone 160 1 0 03 Jun 2024
Variational Bayesian Last Layers James Harrison John Willes Jasper Snoek BDL UQCV 141 33 0 17 Apr 2024
Posterior Uncertainty Quantification in Neural Networks using Data Augmentation Luhuan Wu Sinead Williamson UQCV 86 7 0 18 Mar 2024
One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models Ba-Hien Tran Giulio Franzese Pietro Michiardi Maurizio Filippone DiffM 123 4 0 30 May 2023
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization Sanae Lotfi Marc Finzi Sanyam Kapoor Andres Potapczynski Micah Goldblum A. Wilson BDL MLT AI4CE 81 62 0 24 Nov 2022
Do Bayesian Neural Networks Need To Be Fully Stochastic? Mrinank Sharma Sebastian Farquhar Eric T. Nalisnick Tom Rainforth BDL 68 56 0 11 Nov 2022
Automatic Data Augmentation via Invariance-Constrained Learning Ignacio Hounie Luiz F. O. Chamon Alejandro Ribeiro 58 12 0 29 Sep 2022
Revisiting Neural Scaling Laws in Language and Vision Ibrahim Alabdulmohsin Behnam Neyshabur Xiaohua Zhai 224 111 0 13 Sep 2022
On the Strong Correlation Between Model Invariance and Generalization Weijian Deng Stephen Gould Liang Zheng OOD 75 19 0 14 Jul 2022
How Tempering Fixes Data Augmentation in Bayesian Neural Networks Gregor Bachmann Lorenzo Noci Thomas Hofmann BDL AAML 125 9 0 27 May 2022
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification Sanyam Kapoor Wesley J. Maddox Pavel Izmailov A. Wilson BDL UD 89 51 0 30 Mar 2022
Deep AutoAugment Yu Zheng Zikai Zhang Shen Yan Mi Zhang ViT 100 28 0 11 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 123 48 0 22 Feb 2022
A Theory of PAC Learnability under Transformation Invariances Hang Shao Omar Montasser Avrim Blum 76 21 0 15 Feb 2022
User-friendly introduction to PAC-Bayes bounds Pierre Alquier FedML 173 206 0 21 Oct 2021
Laplace Redux -- Effortless Bayesian Deep Learning Erik A. Daxberger Agustinus Kristiadi Alexander Immer Runa Eschenhagen Matthias Bauer Philipp Hennig BDL UQCV 226 315 0 28 Jun 2021
Data augmentation in Bayesian neural networks and the cold posterior effect Seth Nabarro Stoil Ganev Adrià Garriga-Alonso Vincent Fortuin Mark van der Wilk Laurence Aitchison BDL 77 41 0 10 Jun 2021
A Survey of Data Augmentation Approaches for NLP Steven Y. Feng Varun Gangal Jason W. Wei Sarath Chandar Soroush Vosoughi Teruko Mitamura Eduard H. Hovy AIMat 119 829 0 07 May 2021
What Are Bayesian Neural Network Posteriors Really Like? Pavel Izmailov Sharad Vikram Matthew D. Hoffman A. Wilson UQCV BDL 79 388 0 29 Apr 2021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning Alexander Immer Matthias Bauer Vincent Fortuin Gunnar Rätsch Mohammad Emtiyaz Khan BDL UQCV 142 109 0 11 Apr 2021
Direct Differentiable Augmentation Search Aoming Liu Zehao Huang Zhiwu Huang Naiyan Wang 102 34 0 09 Apr 2021
Martingale posterior distributions Edwin Fong Chris Holmes S. Walker UQCV 162 51 0 29 Mar 2021
Accounting for Variance in Machine Learning Benchmarks Xavier Bouthillier Pierre Delaunay Mirko Bronzi Assya Trofimov Brennan Nichyporuk ... Dmitriy Serdyuk Tal Arbel C. Pal Gaël Varoquaux Pascal Vincent 109 151 0 01 Mar 2021
All You Need is a Good Functional Prior for Bayesian Deep Learning Ba-Hien Tran Simone Rossi Dimitrios Milios Maurizio Filippone OOD BDL 68 61 0 25 Nov 2020
Meta Approach to Data Augmentation Optimization Ryuichiro Hataya Jan Zdenek Kazuki Yoshizoe Hideki Nakayama 79 35 0 14 Jun 2020
On the Benefits of Invariance in Neural Networks Clare Lyle Mark van der Wilk Marta Z. Kwiatkowska Y. Gal Benjamin Bloem-Reddy OOD BDL 81 96 0 01 May 2020
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks Agustinus Kristiadi Matthias Hein Philipp Hennig BDL UQCV 88 289 0 24 Feb 2020
Bayesian Deep Learning and a Probabilistic Perspective of Generalization A. Wilson Pavel Izmailov UQCV BDL OOD 136 656 0 20 Feb 2020
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty Dan Hendrycks Norman Mu E. D. Cubuk Barret Zoph Justin Gilmer Balaji Lakshminarayanan OOD UQCV 138 1,309 0 05 Dec 2019
Faster AutoAugment: Learning Augmentation Strategies using Backpropagation Ryuichiro Hataya Jan Zdenek Kazuki Yoshizoe Hideki Nakayama 77 208 0 16 Nov 2019
RandAugment: Practical automated data augmentation with a reduced search space E. D. Cubuk Barret Zoph Jonathon Shlens Quoc V. Le MQ 278 3,508 0 30 Sep 2019
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules Daniel Ho Eric Liang Ion Stoica Pieter Abbeel Xi Chen 83 404 0 14 May 2019
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features Sangdoo Yun Dongyoon Han Seong Joon Oh Sanghyuk Chun Junsuk Choe Y. Yoo OOD 629 4,814 0 13 May 2019
Fast AutoAugment Sungbin Lim Ildoo Kim Taesup Kim Chiheon Kim Sungwoong Kim 110 599 0 01 May 2019
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations Dan Hendrycks Thomas G. Dietterich OOD VLM 198 3,458 0 28 Mar 2019
Data Augmentation for Bayesian Deep Learning YueXing Wang Nicholas G. Polson Vadim Sokolov UQCV BDL 73 5 0 22 Mar 2019
Learning Invariances using the Marginal Likelihood Mark van der Wilk Matthias Bauer S. T. John J. Hensman 92 86 0 16 Aug 2018
A Kernel Theory of Modern Data Augmentation Tri Dao Albert Gu Alexander J. Ratner Virginia Smith Christopher De Sa Christopher Ré 114 193 0 16 Mar 2018
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 318 9,815 0 25 Oct 2017
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data Gintare Karolina Dziugaite Daniel M. Roy 126 820 0 31 Mar 2017
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 356 4,638 0 10 Nov 2016
Categorical Reparameterization with Gumbel-Softmax Eric Jang S. Gu Ben Poole BDL 367 5,390 0 03 Nov 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.3K 194,641 0 10 Dec 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 905 9,364 0 06 Jun 2015
Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek Hugo Larochelle Ryan P. Adams 384 7,981 0 13 Jun 2012