What Are Bayesian Neural Network Posteriors Really Like?

29 April 2021

Pavel Izmailov

Sharad Vikram

Matthew D. Hoffman

Papers citing "What Are Bayesian Neural Network Posteriors Really Like?"

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Title
Entropy-Guided Sampling of Flat Modes in Discrete Spaces Pinaki Mohanty Riddhiman Bhattacharya Ruqi Zhang 146 0 0 05 May 2025
FedBEns: One-Shot Federated Learning based on Bayesian Ensemble Jacopo Talpini Marco Savi Giovanni Neglia FedML Presented at ResearchTrend Connect \| FedML on 07 May 2025 76 0 0 19 Mar 2025
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model Moritz A. Zanger Pascal R. van der Vaart Wendelin Bohmer M. Spaan UQCV BDL 149 0 0 14 Mar 2025
Bayesian Computation in Deep Learning Wenlong Chen Bolian Li Ruqi Zhang Yingzhen Li BDL 75 0 0 25 Feb 2025
Large Language Model Confidence Estimation via Black-Box Access Tejaswini Pedapati Amit Dhurandhar Soumya Ghosh Soham Dan P. Sattigeri 89 3 0 21 Feb 2025
Student-t processes as infinite-width limits of posterior Bayesian neural networks Francesco Caporali Stefano Favaro Dario Trevisan BDL 179 0 0 06 Feb 2025
CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling Kaiyuan Zhang Siyuan Cheng Guangyu Shen Bruno Ribeiro Shengwei An Pin-Yu Chen Xinming Zhang Ninghui Li 102 1 0 28 Jan 2025
vMF-Contact: Uncertainty-aware Evidential Learning for Probabilistic Contact-grasp in Noisy Clutter Yitian Shi Edgar Welte Maximilian Gilles Rania Rayyes 32 3 0 06 Nov 2024
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics Daniel Paulin P. Whalley Neil K. Chada B. Leimkuhler BDL 49 4 0 14 Oct 2024
Predicting Battery Capacity Fade Using Probabilistic Machine Learning Models With and Without Pre-Trained Priors Michael J. Kenney Katerina G. Malollari Sergei V. Kalinin M. Ziatdinov BDL 36 0 0 08 Oct 2024
Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning Nils Lehmann Jakob Gawlikowski Adam J. Stewart Vytautas Jancauskas Stefan Depeweg Eric T. Nalisnick N. Gottschling 42 0 0 04 Oct 2024
Uncertainty-Guided Self-Questioning and Answering for Video-Language Alignment Jin Chen Kaijing Ma Haojian Huang Jiayu Shen Han Fang Xianghao Zang Chao Ban 79 2 0 17 Sep 2024
Motion Forecasting via Model-Based Risk Minimization Aron Distelzweig Eitan Kosman Andreas Look Faris Janjoš Denesh K. Manivannan Abhinav Valada 34 3 0 16 Sep 2024
Towards Robust Uncertainty-Aware Incomplete Multi-View Classification Mulin. Chen Haojian Huang Qiang Li UQCV 28 1 0 10 Sep 2024
Gradient-free variational learning with conditional mixture networks Conor Heins Hao Wu Dimitrije Marković Alexander Tschantz Jeff Beck Christopher L. Buckley BDL 31 2 0 29 Aug 2024
Function-Space MCMC for Bayesian Wide Neural Networks Lucia Pezzetti Stefano Favaro Stefano Peluchetti BDL 133 0 0 26 Aug 2024
DutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimation Xiaowei Mao Yan Lin S. Guo Yubin Chen Xingyu Xian Haomin Wen Qisen Xu Youfang Lin Huaiyu Wan 44 1 0 23 Aug 2024
Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks Yunfeng Diao Na Zhai Changtao Miao Xun Yang Meng Wang Xun Yang Meng Wang AAML 39 1 0 30 Jul 2024
SoftCVI: Contrastive variational inference with self-generated soft labels Daniel Ward Mark Beaumont Matteo Fasiolo BDL 53 0 0 22 Jul 2024
Robust Classification by Coupling Data Mollification with Label Smoothing Markus Heinonen Ba-Hien Tran Michael Kampffmeyer Maurizio Filippone 73 0 0 03 Jun 2024
Scalable Bayesian Learning with posteriors Samuel Duffield Kaelan Donatella Johnathan Chiu Phoebe Klett Daniel Simpson BDL UQCV 62 1 0 31 May 2024
Bayesian RG Flow in Neural Network Field Theories Jessica N. Howard Marc S. Klinger Anindita Maiti A. G. Stapleton 68 1 0 27 May 2024
Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design A. N. M. N. Abeer Sanket R. Jantre Nathan M. Urban Byung-Jun Yoon 49 1 0 30 Apr 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 43 5 0 04 Mar 2024
Improved off-policy training of diffusion samplers Marcin Sendera Minsu Kim Sarthak Mittal Pablo Lemos Luca Scimeca Jarrid Rector-Brooks Alexandre Adam Yoshua Bengio Nikolay Malkin OffRL 66 17 0 07 Feb 2024
Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference Philipp Reiser Javier Enrique Aguilar A. Guthke Paul-Christian Bürkner 36 2 0 08 Dec 2023
On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning Paul Scemama Ariel Kapusta 40 0 0 21 Nov 2023
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks Sanket R. Jantre Nathan M. Urban Xiaoning Qian Byung-Jun Yoon BDL UQCV 23 4 0 06 Sep 2023
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout Jingjing Xue Min Liu Sheng Sun Yuwei Wang Hui Jiang Xue Jiang 21 7 0 14 Jul 2023
Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference Arnaud Descours Tom Huix Arnaud Guillin Manon Michel Eric Moulines Boris Nectoux BDL 32 1 0 10 Jul 2023
Gibbs Sampling the Posterior of Neural Networks Giovanni Piccioli Emanuele Troiani Lenka Zdeborová 41 2 0 05 Jun 2023
When are ensembles really effective? Ryan Theisen Hyunsuk Kim Yaoqing Yang Liam Hodgkinson Michael W. Mahoney FedML UQCV 35 15 0 21 May 2023
Measuring and Mitigating Local Instability in Deep Neural Networks Arghya Datta Subhrangshu Nandi Jingcheng Xu Greg Ver Steeg He Xie Anoop Kumar Aram Galstyan 20 3 0 18 May 2023
Neural State-Space Models: Empirical Evaluation of Uncertainty Quantification Marco Forgione Dario Piga UQCV 24 2 0 13 Apr 2023
Bayesian Federated Inference for estimating Statistical Models based on Non-shared Multicenter Data sets Marianne A Jonker H. Pazira Anthony C. C. Coolen FedML 25 5 0 15 Feb 2023
Probabilistic Circuits That Know What They Don't Know Fabrizio G. Ventola Steven Braun Zhongjie Yu Martin Mundt Kristian Kersting UQCV TPM 32 7 0 13 Feb 2023
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples Qizhang Li Yiwen Guo W. Zuo Hao Chen AAML 27 35 0 10 Feb 2023
Bayesian posterior approximation with stochastic ensembles Oleksandr Balabanov Bernhard Mehlig H. Linander BDL UQCV 27 5 0 15 Dec 2022
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls Stephan Thaler Gregor Doehner Julija Zavadlav 35 21 0 15 Dec 2022
Are you using test log-likelihood correctly? Sameer K. Deshpande Soumya K. Ghosh Tin D. Nguyen Tamara Broderick 32 7 0 01 Dec 2022
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 29 51 0 24 Nov 2022
Do Bayesian Neural Networks Need To Be Fully Stochastic? Mrinank Sharma Sebastian Farquhar Eric T. Nalisnick Tom Rainforth BDL 23 52 0 11 Nov 2022
Jump-Diffusion Langevin Dynamics for Multimodal Posterior Sampling Jacopo Guidolin Vyacheslav Kungurtsev Ondvrej Kuvzelka BDL 18 0 0 02 Nov 2022
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms Vincent Plassier Alain Durmus Eric Moulines FedML 21 6 0 31 Oct 2022
Sequential Learning Of Neural Networks for Prequential MDL J. Bornschein Yazhe Li Marcus Hutter AI4TS 27 6 0 14 Oct 2022
Machine learning in bioprocess development: From promise to practice L. M. Helleckes J. Hemmerich W. Wiechert E. Lieres A. Grünberger 21 47 0 04 Oct 2022
Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation Lars Schmarje Vasco Grossmann Claudius Zelenka S. Dippel R. Kiko ... M. Pastell J. Stracke A. Valros N. Volkmann Reinahrd Koch 43 34 0 13 Jul 2022
Cold Posteriors through PAC-Bayes Konstantinos Pitas Julyan Arbel 23 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 27 28 0 17 Jun 2022
Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling Jiri Hron Roman Novak Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 45 6 0 15 Jun 2022