Stochastic Gradient Hamiltonian Monte Carlo

17 February 2014

Papers citing "Stochastic Gradient Hamiltonian Monte Carlo"

50 / 137 papers shown

Title
Boosting Statistic Learning with Synthetic Data from Pretrained Large Models Jialong Jiang Wenkang Hu Jian Huang Yuling Jiao Xu Liu DiffM 47 0 0 08 May 2025
Extended Fiducial Inference for Individual Treatment Effects via Deep Neural Networks Sehwan Kim F. Liang FedML 48 0 0 04 May 2025
A Langevin sampling algorithm inspired by the Adam optimizer B. Leimkuhler René Lohmann P. Whalley 74 0 0 26 Apr 2025
3D Student Splatting and Scooping Jialin Zhu Jiangbei Yue Feixiang He He-Nan Wang 3DGS 62 0 0 13 Mar 2025
Bayesian Computation in Deep Learning Wenlong Chen Bolian Li Ruqi Zhang Yingzhen Li BDL 75 0 0 25 Feb 2025
Logit Disagreement: OoD Detection with Bayesian Neural Networks Kevin Raina UQCV BDL UD PER 64 0 0 24 Feb 2025
Single-Step Consistent Diffusion Samplers Pascal Jutras-Dubé Patrick Pynadath Ruqi Zhang DiffM 73 0 0 17 Feb 2025
Random Reshuffling for Stochastic Gradient Langevin Dynamics Luke Shaw Peter A. Whalley 80 3 0 28 Jan 2025
Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models Daniela de Albuquerque John Pearson DiffM 56 0 0 03 Jan 2025
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics Daniel Paulin P. Whalley Neil K. Chada B. Leimkuhler BDL 41 4 0 14 Oct 2024
Multi-Fidelity Bayesian Neural Network for Uncertainty Quantification in Transonic Aerodynamic Loads Andrea Vaiuso Gabriele Immordino Marcello Righi A. Ronch AI4CE 40 0 0 08 Jul 2024
Scalable Bayesian Learning with posteriors Samuel Duffield Kaelan Donatella Johnathan Chiu Phoebe Klett Daniel Simpson BDL UQCV 62 1 0 31 May 2024
Robust Approximate Sampling via Stochastic Gradient Barker Dynamics Lorenzo Mauri Giacomo Zanella 24 3 0 14 May 2024
Scalable Bayesian inference for the generalized linear mixed model S. Berchuck Felipe A. Medeiros Sayan Mukherjee Andrea Agazzi 24 0 0 05 Mar 2024
GAD-PVI: A General Accelerated Dynamic-Weight Particle-Based Variational Inference Framework Fangyikang Wang Huminhao Zhu Chao Zhang Han Zhao Hui Qian 24 5 0 27 Dec 2023
Quantum Langevin Dynamics for Optimization Zherui Chen Yuchen Lu Hao Wang Yizhou Liu Tongyang Li AI4CE 18 9 0 27 Nov 2023
On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning Paul Scemama Ariel Kapusta 32 0 0 21 Nov 2023
Spatial Bayesian Neural Networks A. Zammit‐Mangion Michael D. Kaminski Ba-Hien Tran Maurizio Filippone Noel Cressie BDL 10 7 0 16 Nov 2023
Coreset Markov Chain Monte Carlo Naitong Chen Trevor Campbell 13 4 0 25 Oct 2023
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference Siqi Kou Lei Gan Dequan Wang Chongxuan Li Zhijie Deng BDL DiffM 21 7 0 17 Oct 2023
Benchmarking Scalable Epistemic Uncertainty Quantification in Organ Segmentation Jadie Adams Shireen Elhabian UQCV 15 5 0 15 Aug 2023
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks Felix Jimenez Matthias Katzfuss BDL UQCV 51 1 0 26 May 2023
Integrating Uncertainty into Neural Network-based Speech Enhancement Hu Fang Dennis Becker S. Wermter Timo Gerkmann UQCV 24 2 0 15 May 2023
High Accuracy Uncertainty-Aware Interatomic Force Modeling with Equivariant Bayesian Neural Networks Tim Rensmeyer Benjamin Craig D. Kramer Oliver Niggemann BDL 31 3 0 05 Apr 2023
Particle Mean Field Variational Bayes Minh-Ngoc Tran Paco Tseng Robert Kohn 24 3 0 24 Mar 2023
Free-Form Variational Inference for Gaussian Process State-Space Models Xuhui Fan Edwin V. Bonilla T. O’Kane S. Sisson 11 8 0 20 Feb 2023
Transfer Learning for Bayesian Optimization: A Survey Tianyi Bai Yang Li Yu Shen Xinyi Zhang Wentao Zhang Bin Cui BDL 27 29 0 12 Feb 2023
Physics-informed Information Field Theory for Modeling Physical Systems with Uncertainty Quantification A. Alberts Ilias Bilionis 32 12 0 18 Jan 2023
Do Bayesian Variational Autoencoders Know What They Don't Know? Misha Glazunov Apostolis Zarras UQCV BDL 20 5 0 29 Dec 2022
Particle-based Variational Inference with Preconditioned Functional Gradient Flow Hanze Dong Xi Wang Yong Lin Tong Zhang 22 19 0 25 Nov 2022
SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for Autonomous Driving Liang Peng Boqi Li Wen-Hui Yu Kailiang Yang Wenbo Shao Hong Wang AAML 23 24 0 08 Nov 2022
On the optimization and pruning for Bayesian deep learning X. Ke Yanan Fan BDL UQCV 22 1 0 24 Oct 2022
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning Zhijie Deng Feng Zhou Jun Zhu BDL 42 19 0 23 Oct 2022
On Investigating the Conservative Property of Score-Based Generative Models Chen-Hao Chao Wei-Fang Sun Bo Wun Cheng Chun-Yi Lee 25 10 0 26 Sep 2022
Conformal Methods for Quantifying Uncertainty in Spatiotemporal Data: A Survey S. Sun AI4CE 23 10 0 08 Sep 2022
Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis Gangming Zhao Quanlong Feng Chaoqi Chen Zhen Zhou Yizhou Yu 32 31 0 19 Aug 2022
Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization T. Nguyen Richard G. Baraniuk Robert M. Kirby Stanley J. Osher Bao Wang 21 9 0 01 Aug 2022
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution Zhenyi Wang Li Shen Le Fang Qiuling Suo Tiehang Duan Mingchen Gao OOD 19 38 0 15 Jul 2022
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks Uddeshya Upadhyay Shyamgopal Karthik Yanbei Chen Massimiliano Mancini Zeynep Akata UQCV BDL 21 22 0 14 Jul 2022
Sample-dependent Adaptive Temperature Scaling for Improved Calibration Thomas Joy Francesco Pinto Ser-Nam Lim Philip H. S. Torr P. Dokania UQCV 19 30 0 13 Jul 2022
Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time Jun-Kun Wang Andre Wibisono 22 9 0 05 Jul 2022
RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness Francesco Pinto Harry Yang Ser-Nam Lim Philip H. S. Torr P. Dokania UQCV 25 34 0 29 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
Bayesian Learning of Parameterised Quantum Circuits Samuel Duffield Marcello Benedetti Matthias Rosenkranz 12 11 0 15 Jun 2022
Density Regression and Uncertainty Quantification with Bayesian Deep Noise Neural Networks Daiwei Zhang Tianci Liu Jian Kang BDL UQCV 24 2 0 12 Jun 2022
BaCaDI: Bayesian Causal Discovery with Unknown Interventions Alexander Hagele Jonas Rothfuss Lars Lorch Vignesh Ram Somnath Bernhard Schölkopf Andreas Krause CML BDL 41 19 0 03 Jun 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 38 10 0 17 May 2022
Scalable computation of prediction intervals for neural networks via matrix sketching Alexander Fishkov Maxim Panov UQCV 20 1 0 06 May 2022
Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents Alessandro Barp Lancelot Da Costa G. Francca Karl J. Friston Mark Girolami Michael I. Jordan G. Pavliotis 28 25 0 20 Mar 2022
Defending Black-box Skeleton-based Human Activity Classifiers He-Nan Wang Yunfeng Diao Zichang Tan G. Guo AAML 43 10 0 09 Mar 2022