MCMC using Hamiltonian dynamics

9 June 2012

Papers citing "MCMC using Hamiltonian dynamics"

50 / 1,031 papers shown

Title
Vision-Language Navigation with Energy-Based Policy Rui Liu Wenguan Wang Y. Yang 32 3 0 18 Oct 2024
Training Neural Samplers with Reverse Diffusive KL Divergence Jiajun He Wenlin Chen Mingtian Zhang David Barber José Miguel Hernández-Lobato DiffM 26 4 0 16 Oct 2024
Exact MCMC for Intractable Proposals Dwija Kakkad Dootika Vats 22 0 0 14 Oct 2024
Probabilistic Degeneracy Detection for Point-to-Plane Error Minimization Johan Hatleskog Kostas Alexis 3DPC 32 0 0 14 Oct 2024
Variational Diffusion Posterior Sampling with Midpoint Guidance Badr Moufad Yazid Janati Lisa Bedin Alain Durmus Randal Douc Eric Moulines Jimmy Olsson DiffM 25 1 0 13 Oct 2024
On Divergence Measures for Training GFlowNets Tiago da Silva Eliezer de Souza da Silva Diego Mesquita BDL 29 1 0 12 Oct 2024
Physics and Deep Learning in Computational Wave Imaging Youzuo Lin Shihang Feng J. Theiler Yinpeng Chen Umberto Villa Jing Rao John Greenhall Cristian Pantea M. Anastasio B. Wohlberg 29 0 0 10 Oct 2024
Identifying latent disease factors differently expressed in patient subgroups using group factor analysis Fabio S. Ferreira John Ashburner Arabella Bouzigues Chatrin Suksasilp Lucy L. Russell ... Fermin Moreno Barbara Borroni Samuel Kaski Jonathan D. Rohrer J. Mourão-Miranda CML 46 0 0 10 Oct 2024
Mixing of the No-U-Turn Sampler and the Geometry of Gaussian Concentration Nawaf Bou-Rabee Stefan Oberdörster 23 1 0 09 Oct 2024
Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs? Son Luu Zuheng Xu Nikola Surjanovic Miguel Biron-Lattes Trevor Campbell Alexandre Bouchard-Coté 18 0 0 04 Oct 2024
Thermodynamic Bayesian Inference Maxwell Aifer Samuel Duffield Kaelan Donatella Denis Melanson Phoebe Klett Zach Belateche Gavin Crooks Antonio J. Martinez Patrick J. Coles 31 3 0 02 Oct 2024
Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation Omkar Kulkarni Rohitash Chandra CML AI4TS BDL 32 0 0 01 Oct 2024
Learning Multimodal Latent Generative Models with Energy-Based Prior Shiyu Yuan Jiali Cui Hanao Li Tian Han 24 0 0 30 Sep 2024
Functional Stochastic Gradient MCMC for Bayesian Neural Networks Mengjing Wu Junyu Xuan Jie Lu BDL 13 0 0 25 Sep 2024
EQ-CBM: A Probabilistic Concept Bottleneck with Energy-based Models and Quantized Vectors Sangwon Kim Dasom Ahn B. Ko In-su Jang Kwang-Ju Kim 20 4 0 22 Sep 2024
Implicit Dynamical Flow Fusion (IDFF) for Generative Modeling Mohammad R. Rezaei Rahul G. Krishnan Milos R. Popovic M. Lankarany DiffM 24 0 0 22 Sep 2024
Learning with Sparsely Permuted Data: A Robust Bayesian Approach Abhisek Chakraborty Saptati Datta 25 0 0 16 Sep 2024
HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative models Tingwei Meng Zongren Zou Jérome Darbon George Karniadakis DiffM 35 2 0 15 Sep 2024
Empowering Bayesian Neural Networks with Functional Priors through Anchored Ensembling for Mechanics Surrogate Modeling Applications Javad Ghorbanian Nicholas Casaprima Audrey Olivier 28 0 0 08 Sep 2024
Latent Space Energy-based Neural ODEs Sheng Cheng Deqian Kong Jianwen Xie Kookjin Lee Ying Nian Wu Yezhou Yang DiffM 99 1 0 05 Sep 2024
Amortized Bayesian Multilevel Models Daniel Habermann Marvin Schmitt Lars Kühmichel Andreas Bulling Stefan T. Radev Paul-Christian Burkner 50 3 0 23 Aug 2024
Adaptive Stereographic MCMC Cameron Bell Krzystof Łatuszyński Gareth O. Roberts 22 0 0 21 Aug 2024
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise Yilong Hou Xi’an Li Jinran Wu You-Gan Wang 61 1 0 18 Aug 2024
Learning to Explore for Stochastic Gradient MCMC Seunghyun Kim Seohyeon Jung Seonghyeon Kim Juho Lee BDL 33 1 0 17 Aug 2024
Incremental Structure Discovery of Classification via Sequential Monte Carlo Changze Huang Di Wang 14 0 0 15 Aug 2024
Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology Mario De Florio Zongren Zou Daniele E. Schiavazzi George Karniadakis 26 3 0 13 Aug 2024
Persistent Sampling: Enhancing the Efficiency of Sequential Monte Carlo M. Karamanis U. Seljak 24 0 0 30 Jul 2024
Piecewise deterministic generative models Andrea Bertazzi Alain Durmus Dario Shariatian Umut Simsekli Éric Moulines DiffM 27 0 0 28 Jul 2024
Enhanced SMC $^2$ : Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals Conor Rosato Joshua Murphy Alessandro Varsi P. Horridge Simon Maskell 29 4 0 24 Jul 2024
Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models Hannah Rosa Friesacher O. Engkvist Lewis H. Mervin Yves Moreau Adam Arany 26 0 0 19 Jul 2024
Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review Masatoshi Uehara Yulai Zhao Tommaso Biancalani Sergey Levine 54 22 0 18 Jul 2024
Computationally Efficient Estimation of Large Probit Models Patrick Ding Guido Imbens Zhaonan Qu Yinyu Ye 29 0 0 12 Jul 2024
Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation Yifei Zong D. Barajas-Solano A. Tartakovsky 41 1 0 05 Jul 2024
Exact Bayesian Gaussian Cox Processes Using Random Integral Bingjing Tang Julia Palacios 23 0 0 28 Jun 2024
Torchtree: flexible phylogenetic model development and inference using PyTorch Mathieu Fourment Matthew Macaulay Christiaan J. Swanepoel Xiang Ji M. Suchard Frederick A Matsen IV BDL 29 0 0 26 Jun 2024
Recursive variational Gaussian approximation with the Whittle likelihood for linear non-Gaussian state space models Bao Anh Vu David Gunawan Andrew Zammit-Mangion 19 1 0 23 Jun 2024
Bayesian neural networks for predicting uncertainty in full-field material response G. Pasparakis Lori Graham-Brady Michael D. Shields AI4CE 37 4 0 21 Jun 2024
Enhancing Dropout-based Bayesian Neural Networks with Multi-Exit on FPGA Hao Chen Liam Castelli Martin Ferianc Hongyu Zhou Shuanglong Liu Wayne Luk Hongxiang Fan 28 0 0 20 Jun 2024
Gradient Estimation via Differentiable Metropolis-Hastings Gaurav Arya Moritz Schauer Ruben Seyer OT BDL 35 0 0 20 Jun 2024
Robust Inference of Dynamic Covariance Using Wishart Processes and Sequential Monte Carlo Hester Huijsdens D. Leeftink Linda Geerligs Max Hinne 19 0 0 07 Jun 2024
A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks K. Shukla Juan Diego Toscano Zhicheng Wang Zongren Zou George Karniadakis 24 73 0 05 Jun 2024
Sampling From Multiscale Densities With Delayed Rejection Generalized Hamiltonian Monte Carlo Gilad Turok Chirag Modi Bob Carpenter BDL 30 3 0 04 Jun 2024
One-Shot Federated Learning with Bayesian Pseudocoresets Tim d'Hondt Mykola Pechenizkiy Robert Peharz FedML 26 0 0 04 Jun 2024
Evaluating Bayesian deep learning for radio galaxy classification Devina Mohan Anna M. M. Scaife UQCV BDL 39 1 0 28 May 2024
EM Distillation for One-step Diffusion Models Sirui Xie Zhisheng Xiao Diederik P. Kingma Tingbo Hou Ying Nian Wu Kevin Patrick Murphy Tim Salimans Ben Poole Ruiqi Gao VLM DiffM 37 24 0 27 May 2024
Faster Sampling via Stochastic Gradient Proximal Sampler Xunpeng Huang Difan Zou Yian Ma Hanze Dong Tong Zhang 52 3 0 27 May 2024
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows A. Cabezas Louis Sharrock Christopher Nemeth 34 1 0 23 May 2024
A Direct Importance Sampling-based Framework for Rare Event Uncertainty Quantification in Non-Gaussian Spaces Elsayed M. Eshra Konstantinos G. Papakonstantinou Hamed Nikbakht 23 0 0 23 May 2024
Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification Kaizheng Wang Fabio Cuzzolin Keivan K1 Shariatmadar David Moens Hans Hallez UQCV BDL 65 6 0 23 May 2024
Generalized Laplace Approximation Yinsong Chen Samson S. Yu Zhong Li Chee Peng Lim BDL 51 0 0 22 May 2024