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A statistical theory of cold posteriors in deep neural networks

13 August 2020

Papers citing "A statistical theory of cold posteriors in deep neural networks"

49 / 49 papers shown

Title
Addressing the Inconsistency in Bayesian Deep Learning via Generalized Laplace Approximation Yinsong Chen Samson S. Yu Zhong Li Chee Peng Lim BDL 78 0 0 01 Jul 2025
Textual Bayes: Quantifying Uncertainty in LLM-Based Systems Brendan Leigh Ross Noël Vouitsis Atiyeh Ashari Ghomi Rasa Hosseinzadeh Ji Xin ... Yi Sui Shiyi Hou Kin Kwan Leung Gabriel Loaiza-Ganem Jesse C. Cresswell 49 0 0 11 Jun 2025
Are vision language models robust to uncertain inputs? Xi Wang Eric Nalisnick AAML VLM Presented at ResearchTrend Connect \| VLM on 18 Jun 2025 140 1 0 17 May 2025
Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles Andrew Millard Zheng Zhao Joshua Murphy Simon Maskell UQCV BDL 118 0 0 16 May 2025
A Weighted-likelihood framework for class imbalance in Bayesian prediction models Stanley E. Lazic 82 0 0 23 Apr 2025
Learning Hyperparameters via a Data-Emphasized Variational Objective Ethan Harvey Mikhail Petrov Michael C. Hughes 109 0 0 03 Feb 2025
Diffusion Priors for Variational Likelihood Estimation and Image Denoising Jun Cheng Shan Tan DiffM 104 0 0 23 Oct 2024
Temperature Optimization for Bayesian Deep Learning Kenyon Ng Chris van der Heide Liam Hodgkinson Susan Wei BDL 109 0 0 08 Oct 2024
Predictive performance of power posteriors Yann McLatchie Edwin Fong David T. Frazier Jeremias Knoblauch 56 2 0 16 Aug 2024
Scalable Bayesian Learning with posteriors Samuel Duffield Kaelan Donatella Johnathan Chiu Phoebe Klett Daniel Simpson BDL UQCV 173 2 0 31 May 2024
Making Better Use of Unlabelled Data in Bayesian Active Learning Freddie Bickford-Smith Adam Foster Tom Rainforth 97 4 0 26 Apr 2024
Variational Bayesian Last Layers James Harrison John Willes Jasper Snoek BDL UQCV 141 34 0 17 Apr 2024
On Uncertainty Quantification for Near-Bayes Optimal Algorithms Ziyu Wang Chris Holmes UQCV 97 3 0 28 Mar 2024
Can a Confident Prior Replace a Cold Posterior? Martin Marek Brooks Paige Pavel Izmailov UQCV BDL 61 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 131 35 0 01 Feb 2024
Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models Qiang Liu Nils Thuerey DiffM AI4CE 65 16 0 08 Dec 2023
If there is no underfitting, there is no Cold Posterior Effect Yijie Zhang Yi-Shan Wu Luis A. Ortega A. Masegosa UQCV 61 1 0 02 Oct 2023
A Primer on Bayesian Neural Networks: Review and Debates Federico Danieli Konstantinos Pitas M. Vladimirova Vincent Fortuin BDL AAML 103 20 0 28 Sep 2023
The fine print on tempered posteriors Konstantinos Pitas Julyan Arbel 63 1 0 11 Sep 2023
Function-Space Regularization for Deep Bayesian Classification J. Lin Joe Watson Pascal Klink Jan Peters UQCV BDL 66 1 0 12 Jul 2023
Non-reversible Parallel Tempering for Deep Posterior Approximation Wei Deng Qian Zhang Qi Feng F. Liang Guang Lin 64 4 0 20 Nov 2022
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes Liam Hodgkinson Christopher van der Heide Fred Roosta Michael W. Mahoney UQCV 65 6 0 14 Oct 2022
Identify ambiguous tasks combining crowdsourced labels by weighting Areas Under the Margin Tanguy Lefort Benjamin Charlier Alexis Joly Joseph Salmon 73 5 0 30 Sep 2022
Robustness to corruption in pre-trained Bayesian neural networks Xi Wang Laurence Aitchison OOD UQCV 54 5 0 24 Jun 2022
Cold Posteriors through PAC-Bayes Konstantinos Pitas Julyan Arbel 89 5 0 22 Jun 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 92 51 0 30 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 69 13 0 22 Feb 2022
Interacting Contour Stochastic Gradient Langevin Dynamics Wei Deng Siqi Liang Botao Hao Guang Lin F. Liang BDL 79 10 0 20 Feb 2022
Theoretical characterization of uncertainty in high-dimensional linear classification Lucas Clarté Bruno Loureiro Florent Krzakala Lenka Zdeborová 78 21 0 07 Feb 2022
Posterior temperature optimized Bayesian models for inverse problems in medical imaging M. Laves Malte Tolle Alexander Schlaefer Sandy Engelhardt 76 10 0 02 Feb 2022
How Infinitely Wide Neural Networks Can Benefit from Multi-task Learning -- an Exact Macroscopic Characterization Jakob Heiss Josef Teichmann Hanna Wutte MLT 53 2 0 31 Dec 2021
Weight Pruning and Uncertainty in Radio Galaxy Classification Devina Mohan A. Scaife UQCV 73 0 0 23 Nov 2021
Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model Jing Zhang Yuchao Dai Mehrtash Harandi Yiran Zhong Nick Barnes Leonid Sigal UQCV 60 1 0 22 Nov 2021
Periodic Activation Functions Induce Stationarity Lassi Meronen Martin Trapp Arno Solin BDL 84 21 0 26 Oct 2021
Bayesian neural network unit priors and generalized Weibull-tail property M. Vladimirova Julyan Arbel Stéphane Girard BDL 84 9 0 06 Oct 2021
Deep Classifiers with Label Noise Modeling and Distance Awareness Vincent Fortuin Mark Collier F. Wenzel J. Allingham J. Liu Dustin Tran Balaji Lakshminarayanan Jesse Berent Rodolphe Jenatton E. Kokiopoulou UQCV 71 11 0 06 Oct 2021
Non-asymptotic estimates for TUSLA algorithm for non-convex learning with applications to neural networks with ReLU activation function Dongjae Lim Ariel Neufeld Sotirios Sabanis Ying Zhang 80 20 0 19 Jul 2021
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect Lorenzo Noci Kevin Roth Gregor Bachmann Sebastian Nowozin Thomas Hofmann CML 77 26 0 11 Jun 2021
Posterior Temperature Optimization in Variational Inference for Inverse Problems M. Laves Malte Tolle Alexander Schlaefer Sandy Engelhardt 92 3 0 11 Jun 2021
Model Selection for Bayesian Autoencoders Ba-Hien Tran Simone Rossi Dimitrios Milios Pietro Michiardi Edwin V. Bonilla Maurizio Filippone BDL 79 13 0 11 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 83 41 0 10 Jun 2021
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks Dong-Young Lim Sotirios Sabanis 95 12 0 28 May 2021
What Are Bayesian Neural Network Posteriors Really Like? Pavel Izmailov Sharad Vikram Matthew D. Hoffman A. Wilson UQCV BDL 81 388 0 29 Apr 2021
Bayesian OOD detection with aleatoric uncertainty and outlier exposure Xi Wang Laurence Aitchison UD 72 15 0 24 Feb 2021
Bayesian Neural Network Priors Revisited Vincent Fortuin Adrià Garriga-Alonso Sebastian W. Ober F. Wenzel Gunnar Rätsch Richard Turner Mark van der Wilk Laurence Aitchison BDL UQCV 133 141 0 12 Feb 2021
Variational Laplace for Bayesian neural networks Ali Unlu Laurence Aitchison UQCV BDL 30 0 0 20 Nov 2020
Semi-supervised learning objectives as log-likelihoods in a generative model of data curation Stoil Ganev Laurence Aitchison 31 4 0 13 Aug 2020
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes Sebastian W. Ober Laurence Aitchison BDL 112 60 0 17 May 2020