MCMC using Hamiltonian dynamics

9 June 2012

Papers citing "MCMC using Hamiltonian dynamics"

50 / 1,032 papers shown

Title
Learning Physics between Digital Twins with Low-Fidelity Models and Physics-Informed Gaussian Processes Michail Spitieris I. Steinsland AI4CE 11 0 0 16 Jun 2022
CARD: Classification and Regression Diffusion Models Xizewen Han Huangjie Zheng Mingyuan Zhou DiffM 38 108 0 15 Jun 2022
Benchmarking Bayesian neural networks and evaluation metrics for regression tasks B. Staber Sébastien Da Veiga UQCV BDL 37 3 0 08 Jun 2022
Certified Dimension Reduction for Bayesian Updating with the Cross-Entropy Method Max Ehre Rafael Flock M. Fußeder I. Papaioannou D. Štraub 11 3 0 07 Jun 2022
Functional Ensemble Distillation Coby Penso Idan Achituve Ethan Fetaya FedML 23 2 0 05 Jun 2022
Asymptotic Properties for Bayesian Neural Network in Besov Space Kyeongwon Lee Jaeyong Lee BDL 8 4 0 01 Jun 2022
Nested sampling for physical scientists G. Ashton N. Bernstein Johannes Buchner Xi Chen Gábor Csányi ... Leah F. South J. Veitch Philipp Wacker D. Wales David Yallup 36 76 0 31 May 2022
Mean Field inference of CRFs based on GAT LingHong Xing Xiangxiang Ma Guangsheng Luo 13 0 0 29 May 2022
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference R. Grumitt B. Dai U. Seljak BDL 24 12 0 27 May 2022
RL with KL penalties is better viewed as Bayesian inference Tomasz Korbak Ethan Perez Christopher L. Buckley OffRL 30 71 0 23 May 2022
A Proximal Algorithm for Sampling from Non-convex Potentials Jiaming Liang Yongxin Chen 26 4 0 20 May 2022
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks Agustinus Kristiadi Runa Eschenhagen Philipp Hennig BDL 19 12 0 20 May 2022
Continuously-Tempered PDMP Samplers Matthew Sutton R. Salomone Augustin Chevallier Paul Fearnhead 16 1 0 19 May 2022
An importance sampling approach for reliable and efficient inference in Bayesian ordinary differential equation models Juho Timonen Nikolas Siccha Benjamin B. Bales Harri Lähdesmäki Aki Vehtari 4 3 0 18 May 2022
MixFlows: principled variational inference via mixed flows Zuheng Xu Na Chen Trevor Campbell 55 8 0 16 May 2022
$On the use of a local $\hat{R}$ to improve MCMC convergence diagnostic$ On the use of a local $\hat{R}$ to improve MCMC convergence diagnostic Théo Moins Julyan Arbel A. Dutfoy Stéphane Girard 17 12 0 13 May 2022
Learning Multitask Gaussian Bayesian Networks Shuai Liu Yixuan Qiu Baojuan Li Huaning Wang Xiangyu Chang 14 2 0 11 May 2022
Hamiltonian Monte Carlo Particle Swarm Optimizer Omatharv Bharat Vaidya Rithvik Terence DSouza Snehanshu Saha S. Dhavala Swagatam Das 14 0 0 08 May 2022
Variational Inference for Nonlinear Inverse Problems via Neural Net Kernels: Comparison to Bayesian Neural Networks, Application to Topology Optimization Vahid Keshavarzzadeh Robert M. Kirby A. Narayan BDL 13 2 0 07 May 2022
Probabilistic learning constrained by realizations using a weak formulation of Fourier transform of probability measures Christian Soize 8 5 0 06 May 2022
A Survey on Uncertainty Toolkits for Deep Learning Maximilian Pintz Joachim Sicking Maximilian Poretschkin Maram Akila ELM 28 6 0 02 May 2022
Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation Rebecca S Stone Nishant Ravikumar A. Bulpitt David C. Hogg 21 14 0 20 Apr 2022
An Energy-Based Prior for Generative Saliency Jing Zhang Jianwen Xie Nick Barnes Ping Li 35 3 0 19 Apr 2022
A Variational Approach to Bayesian Phylogenetic Inference Cheng Zhang IV FrederickA.Matsen BDL 16 17 0 16 Apr 2022
Hierarchical Embedded Bayesian Additive Regression Trees Bruna D. Wundervald Andrew C. Parnell Katarina Domijan 20 1 0 14 Apr 2022
Inference over radiative transfer models using variational and expectation maximization methods D. Svendsen Daniel Hernández-Lobato Luca Martino Valero Laparra Á. Moreno-Martínez Gustau Camps-Valls 37 4 0 07 Apr 2022
Energy-based Latent Aligner for Incremental Learning K. J. Joseph Salman Khan F. Khan Rao Muhammad Anwer V. Balasubramanian CLL 28 46 0 28 Mar 2022
Computed Tomography Reconstruction using Generative Energy-Based Priors Martin Zach Erich Kobler T. Pock DiffM MedIm 11 12 0 23 Mar 2022
Quantum-enhanced Markov chain Monte Carlo David Layden G. Mazzola R. Mishmash M. Motta P. Wocjan Jin-Sung Kim S. Sheldon 19 54 0 23 Mar 2022
Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem Amir Emad Marvasti Ehsan Emad Marvasti Ulas Bagci OOD 8 0 0 23 Mar 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
Generating Independent Replicates Directly from the Posterior Distribution for a Class of Spatial Latent Gaussian Process Models J. Bradley Madelyn Clinch 11 0 0 18 Mar 2022
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC Anna Kuzina Max Welling Jakub M. Tomczak AAML DRL 31 12 0 18 Mar 2022
Generalized Score Matching for Regression Jiazhen Xu J. Scealy A. Wood Tao Zou 17 5 0 18 Mar 2022
Econometric Modeling of Intraday Electricity Market Price with Inadequate Historical Data Saeed Mohammadi M. Hesamzadeh 6 4 0 11 Mar 2022
MAGI: A Package for Inference of Dynamic Systems from Noisy and Sparse Data via Manifold-constrained Gaussian Processes Samuel W. K. Wong Shihao Yang S. C. Kou AI4CE SyDa 11 9 0 11 Mar 2022
Bayesian inference via sparse Hamiltonian flows Na Chen Zuheng Xu Trevor Campbell 21 14 0 11 Mar 2022
Structure and Distribution Metric for Quantifying the Quality of Uncertainty: Assessing Gaussian Processes, Deep Neural Nets, and Deep Neural Operators for Regression Ethan Pickering T. Sapsis UQCV 14 6 0 09 Mar 2022
Variational Inference with Locally Enhanced Bounds for Hierarchical Models Tomas Geffner Justin Domke 16 5 0 08 Mar 2022
A Proximal Algorithm for Sampling Jiaming Liang Yongxin Chen 9 17 0 28 Feb 2022
Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten Q. Nguyen Ryutaro Oikawa D. Divakaran M. Chan K. H. Low MU 17 37 0 28 Feb 2022
COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics Lianhui Qin Sean Welleck Daniel Khashabi Yejin Choi AI4CE 44 144 0 23 Feb 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 24 12 0 22 Feb 2022
Efficient computation of the volume of a polytope in high-dimensions using Piecewise Deterministic Markov Processes Augustin Chevallier F. Cazals Paul Fearnhead 9 13 0 18 Feb 2022
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection Cristian Challu Peihong Jiang Ying Nian Wu Laurent Callot BDL AI4TS 11 25 0 15 Feb 2022
DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories Christian Moya Shiqi Zhang Meng Yue Guang Lin 14 42 0 15 Feb 2022
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo I. Peis Chao Ma José Miguel Hernández-Lobato BDL DRL 14 14 0 09 Feb 2022
Adjoint-aided inference of Gaussian process driven differential equations Paterne Gahungu Christopher W. Lanyon Mauricio A. Alvarez Engineer Bainomugisha M. Smith Richard D. Wilkinson 16 5 0 09 Feb 2022
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network Nikita Kotelevskii A. Artemenkov Kirill Fedyanin Fedor Noskov Alexander Fishkov Artem Shelmanov Artem Vazhentsev Aleksandr Petiushko Maxim Panov UQCV BDL 48 25 0 07 Feb 2022
Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations Jaehyeong Jo Seul Lee Sung Ju Hwang DiffM 22 210 0 05 Feb 2022