Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning

11 February 2019

Jianyi Zhang

Papers citing "Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning"

50 / 87 papers shown

Title
JaxSGMC: Modular stochastic gradient MCMC in JAX Stephan Thaler Paul Fuchs Ana Cukarska Julija Zavadlav BDL 48 2 0 16 May 2025
Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Review Toghrul Abbasli Kentaroh Toyoda Yuan Wang Leon Witt Muhammad Asif Ali Yukai Miao Dan Li Qingsong Wei UQCV 94 0 0 25 Apr 2025
Temporal Score Analysis for Understanding and Correcting Diffusion Artifacts Yu Cao Zengqun Zhao Ioannis Patras Shaogang Gong DiffM 62 0 0 20 Mar 2025
Enhancing Gradient-based Discrete Sampling via Parallel Tempering Luxu Liang Yuhang Jia Feng Zhou 65 0 0 26 Feb 2025
Bayesian Computation in Deep Learning Wenlong Chen Bolian Li Ruqi Zhang Yingzhen Li BDL 75 0 0 25 Feb 2025
Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations Haoyang Zheng Guang Lin AI4CE 58 0 0 01 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 160 1 0 28 Jan 2025
BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models Yibin Wang Haochen Shi Ligong Han Dimitris N. Metaxas Hao Wang BDL UQLM 116 8 0 28 Jan 2025
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics Daniel Paulin P. Whalley Neil K. Chada B. Leimkuhler BDL 52 4 0 14 Oct 2024
Provable Convergence and Limitations of Geometric Tempering for Langevin Dynamics Omar Chehab Anna Korba Austin Stromme Adrien Vacher 37 3 0 13 Oct 2024
Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani Marvin Pfortner Tobias Weber Philipp Hennig UQCV 71 1 0 07 Jun 2024
Scalable Bayesian Learning with posteriors Samuel Duffield Kaelan Donatella Johnathan Chiu Phoebe Klett Daniel Simpson BDL UQCV 65 1 0 31 May 2024
Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics Haoyang Zheng Hengrong Du Qi Feng Wei Deng Guang Lin 44 4 0 13 May 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 48 5 0 04 Mar 2024
Bayesian Neural Network For Personalized Federated Learning Parameter Selection Mengen Luo E. Kuruoglu FedML 42 0 0 25 Feb 2024
Uncertainty quantification in fine-tuned LLMs using LoRA ensembles Oleksandr Balabanov Hampus Linander UQCV 43 15 0 19 Feb 2024
Assessing Uncertainty Estimation Methods for 3D Image Segmentation under Distribution Shifts Masoumeh Javanbakhat Md Tasnimul Hasan Cristoph Lippert UQCV OOD 31 1 0 10 Feb 2024
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference Siqi Kou Lei Gan Dequan Wang Chongxuan Li Zhijie Deng BDL DiffM 23 7 0 17 Oct 2023
Function-Space Regularization for Deep Bayesian Classification J. Lin Joe Watson Pascal Klink Jan Peters UQCV BDL 46 1 0 12 Jul 2023
Quantification of Uncertainty with Adversarial Models Kajetan Schweighofer L. Aichberger Mykyta Ielanskyi Günter Klambauer Sepp Hochreiter UQCV 44 14 0 06 Jul 2023
Gibbs Sampling the Posterior of Neural Networks Giovanni Piccioli Emanuele Troiani Lenka Zdeborová 46 2 0 05 Jun 2023
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference Insung Kong Dongyoon Yang Jongjin Lee Ilsang Ohn Gyuseung Baek Yongdai Kim BDL 36 4 0 24 May 2023
High Accuracy Uncertainty-Aware Interatomic Force Modeling with Equivariant Bayesian Neural Networks Tim Rensmeyer Benjamin Craig D. Kramer Oliver Niggemann BDL 51 3 0 05 Apr 2023
Implicit Visual Bias Mitigation by Posterior Estimate Sharpening of a Bayesian Neural Network Rebecca S Stone Nishant Ravikumar A. Bulpitt David C. Hogg BDL 41 0 0 29 Mar 2023
To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning Ildus Sadrtdinov Dmitrii Pozdeev Dmitry Vetrov E. Lobacheva 37 4 0 06 Mar 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 33 5 0 15 Feb 2023
Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD than Constant Stepsize Mert Gurbuzbalaban Yuanhan Hu Umut Simsekli Lingjiong Zhu LRM 23 1 0 10 Feb 2023
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples Qizhang Li Yiwen Guo W. Zuo Hao Chen AAML 39 36 0 10 Feb 2023
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes Ba-Hien Tran Babak Shahbaba Stephan Mandt Maurizio Filippone SyDa BDL UQCV 34 5 0 09 Feb 2023
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls Stephan Thaler Gregor Doehner Julija Zavadlav 40 21 0 15 Dec 2022
Calibrating AI Models for Wireless Communications via Conformal Prediction K. Cohen Sangwoo Park Osvaldo Simeone S. Shamai 34 6 0 15 Dec 2022
Non-reversible Parallel Tempering for Deep Posterior Approximation Wei Deng Qian Zhang Qi Feng F. Liang Guang Lin 31 4 0 20 Nov 2022
On the optimization and pruning for Bayesian deep learning X. Ke Yanan Fan BDL UQCV 42 1 0 24 Oct 2022
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning Zhijie Deng Feng Zhou Jun Zhu BDL 50 19 0 23 Oct 2022
Personalizing Text-to-Image Generation via Aesthetic Gradients Víctor Gallego 50 16 0 25 Sep 2022
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs Emilia Magnani Nicholas Kramer Runa Eschenhagen Lorenzo Rosasco Philipp Hennig UQCV BDL 21 9 0 02 Aug 2022
Assaying Out-Of-Distribution Generalization in Transfer Learning F. Wenzel Andrea Dittadi Peter V. Gehler Carl-Johann Simon-Gabriel Max Horn ... Chris Russell Thomas Brox Bernt Schiele Bernhard Schölkopf Francesco Locatello OOD OODD AAML 64 71 0 19 Jul 2022
Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors Gianluca Detommaso Alberto Gasparin A. Wilson Cédric Archambeau UQCV BDL 34 3 0 17 Jul 2022
RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness Francesco Pinto Harry Yang Ser-Nam Lim Philip Torr P. Dokania UQCV 35 34 0 29 Jun 2022
Federated Learning with Uncertainty via Distilled Predictive Distributions Shreyansh P. Bhatt Aishwarya Gupta Piyush Rai FedML 39 11 0 15 Jun 2022
Functional Ensemble Distillation Coby Penso Idan Achituve Ethan Fetaya FedML 37 2 0 05 Jun 2022
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling Lei Cheng Feng Yin Sergios Theodoridis S. Chatzis Tsung-Hui Chang 70 75 0 28 May 2022
How Tempering Fixes Data Augmentation in Bayesian Neural Networks Gregor Bachmann Lorenzo Noci Thomas Hofmann BDL AAML 82 8 0 27 May 2022
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors Ravid Shwartz-Ziv Micah Goldblum Hossein Souri Sanyam Kapoor Chen Zhu Yann LeCun A. Wilson UQCV BDL 64 43 0 20 May 2022
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility Hoileong Lee Fadhel Ayed Paul Jung Juho Lee Hongseok Yang François Caron 51 10 0 17 May 2022
A PAC-Bayes oracle inequality for sparse neural networks Maximilian F. Steffen Mathias Trabs UQCV 23 2 0 26 Apr 2022
Knowledge Removal in Sampling-based Bayesian Inference Shaopeng Fu Fengxiang He Dacheng Tao BDL MU 30 27 0 24 Mar 2022
Cyclical Variational Bayes Monte Carlo for Efficient Multi-Modal Posterior Distributions Evaluation F. Igea Alice Cicirello 24 8 0 23 Feb 2022
Interacting Contour Stochastic Gradient Langevin Dynamics Wei Deng Siqi Liang Botao Hao Guang Lin F. Liang BDL 43 10 0 20 Feb 2022
Model Architecture Adaption for Bayesian Neural Networks Duo Wang Yiren Zhao Ilia Shumailov Robert D. Mullins UQCV OOD BDL 31 0 0 09 Feb 2022