Stochastic Gradient Hamiltonian Monte Carlo

17 February 2014

Papers citing "Stochastic Gradient Hamiltonian Monte Carlo"

50 / 137 papers shown

Title
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection Bang Xiang Yong Alexandra Brintrup UQCV 15 24 0 25 Feb 2022
Non-Volatile Memory Accelerated Posterior Estimation A. Wood Moshik Hershcovitch Daniel Waddington Sarel Cohen Peter Chin 11 1 0 21 Feb 2022
Interacting Contour Stochastic Gradient Langevin Dynamics Wei Deng Siqi Liang Botao Hao Guang Lin F. Liang BDL 23 10 0 20 Feb 2022
PFGE: Parsimonious Fast Geometric Ensembling of DNNs Hao Guo Jiyong Jin B. Liu FedML 11 1 0 14 Feb 2022
Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications Se Yoon Lee 17 17 0 28 Jan 2022
Maximizing information from chemical engineering data sets: Applications to machine learning Alexander Thebelt Johannes Wiebe Jan Kronqvist Calvin Tsay Ruth Misener AI4CE 32 68 0 25 Jan 2022
Surrogate Likelihoods for Variational Annealed Importance Sampling M. Jankowiak Du Phan BDL 12 13 0 22 Dec 2021
Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer Shiye Lei Zhuozhuo Tu Leszek Rutkowski Feng Zhou Li Shen Fengxiang He Dacheng Tao BDL 19 2 0 12 Dec 2021
On Convergence of Federated Averaging Langevin Dynamics Wei Deng Qian Zhang Yi-An Ma Zhao-quan Song Guang Lin FedML 22 16 0 09 Dec 2021
A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications Xinlei Zhou Han Liu Farhad Pourpanah T. Zeng Xizhao Wang UQCV UD 19 58 0 03 Nov 2021
Unsupervised PET Reconstruction from a Bayesian Perspective Chenyu Shen Wenjun Xia H. Ye Mingzheng Hou Hu Chen Yan Liu Jiliu Zhou Yi Zhang 31 3 0 29 Oct 2021
Pathologies in priors and inference for Bayesian transformers Tristan Cinquin Alexander Immer Max Horn Vincent Fortuin UQCV BDL MedIm 29 9 0 08 Oct 2021
Relative Entropy Gradient Sampler for Unnormalized Distributions Xingdong Feng Yuan Gao Jian Huang Yuling Jiao Xu Liu 28 7 0 06 Oct 2021
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO Katharina Eggensperger Philip Muller Neeratyoy Mallik Matthias Feurer René Sass Aaron Klein Noor H. Awad Marius Lindauer Frank Hutter 35 100 0 14 Sep 2021
Bayesian forecast combination using time-varying features Li Li Yanfei Kang Feng Li AI4TS 19 14 0 04 Aug 2021
Uniform minorization condition and convergence bounds for discretizations of kinetic Langevin dynamics Alain Durmus Aurélien Enfroy Eric Moulines G. Stoltz 17 17 0 30 Jul 2021
Bayesian Autoencoders for Drift Detection in Industrial Environments Bang Xiang Yong Yasmin Fathy Alexandra Brintrup UQCV 8 8 0 28 Jul 2021
Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo Vyacheslav Kungurtsev Adam D. Cobb T. Javidi Brian Jalaian 51 4 0 15 Jul 2021
Deep Gaussian Processes: A Survey Kalvik Jakkala AI4CE GP BDL 8 19 0 21 Jun 2021
Schr{ö}dinger-F{ö}llmer Sampler: Sampling without Ergodicity Jian Huang Yuling Jiao Lican Kang Xu Liao Jin Liu Yanyan Liu 27 27 0 21 Jun 2021
Differentially Private Hamiltonian Monte Carlo Ossi Raisa A. Koskela Antti Honkela 11 6 0 17 Jun 2021
Post-hoc loss-calibration for Bayesian neural networks Meet P. Vadera S. Ghosh Kenney Ng Benjamin M. Marlin UQCV BDL 28 7 0 13 Jun 2021
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect Lorenzo Noci Kevin Roth Gregor Bachmann Sebastian Nowozin Thomas Hofmann CML 22 23 0 11 Jun 2021
Learning Functional Priors and Posteriors from Data and Physics Xuhui Meng Liu Yang Zhiping Mao J. Ferrandis George Karniadakis AI4CE 27 61 0 08 Jun 2021
A Unifying and Canonical Description of Measure-Preserving Diffusions Alessandro Barp So Takao M. Betancourt Alexis Arnaudon Mark Girolami 20 17 0 06 May 2021
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie R. Laumont Valentin De Bortoli Andrés Almansa J. Delon Alain Durmus Marcelo Pereyra 16 109 0 08 Mar 2021
The shifted ODE method for underdamped Langevin MCMC James Foster Terry Lyons Harald Oberhauser 11 16 0 10 Jan 2021
A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving Di Feng Ali Harakeh Steven Waslander Klaus C. J. Dietmayer AAML UQCV EDL 24 222 0 20 Nov 2020
Failure Prediction by Confidence Estimation of Uncertainty-Aware Dirichlet Networks Theodoros Tsiligkaridis UQCV 22 7 0 19 Oct 2020
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions Wei Deng Guang Lin F. Liang BDL 34 27 0 19 Oct 2020
A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning Hongjun Wang Guanbin Li Xiaobai Liu Liang Lin GAN AAML 11 22 0 15 Oct 2020
Set Prediction without Imposing Structure as Conditional Density Estimation David W. Zhang Gertjan J. Burghouts Cees G. M. Snoek 43 17 0 08 Oct 2020
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction Wei Deng Qi Feng G. Karagiannis Guang Lin F. Liang 14 8 0 02 Oct 2020
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks Meet P. Vadera Adam D. Cobb B. Jalaeian Benjamin M. Marlin BDL UQCV 16 16 0 08 Jul 2020
Bayesian Neural Networks: An Introduction and Survey Ethan Goan Clinton Fookes BDL UQCV 21 199 0 22 Jun 2020
Predictive Coding Approximates Backprop along Arbitrary Computation Graphs Beren Millidge Alexander Tschantz Christopher L. Buckley 16 118 0 07 Jun 2020
Federated Stochastic Gradient Langevin Dynamics Khaoula El Mekkaoui Diego Mesquita P. Blomstedt Samuel Kaski FedML 11 24 0 23 Apr 2020
Stochastically Differentiable Probabilistic Programs David Tolpin Yuanshuo Zhou Hongseok Yang BDL 4 0 0 02 Mar 2020
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization Ömer Deniz Akyildiz Sotirios Sabanis 35 17 0 13 Feb 2020
Stochastic gradient Markov chain Monte Carlo Christopher Nemeth Paul Fearnhead BDL 16 135 0 16 Jul 2019
Chaining Meets Chain Rule: Multilevel Entropic Regularization and Training of Neural Nets Amir-Reza Asadi Emmanuel Abbe BDL AI4CE 21 13 0 26 Jun 2019
Walsh-Hadamard Variational Inference for Bayesian Deep Learning Simone Rossi Sébastien Marmin Maurizio Filippone BDL 19 14 0 27 May 2019
Coordination and Trajectory Prediction for Vehicle Interactions via Bayesian Generative Modeling Jiachen Li Hengbo Ma Wei Zhan M. Tomizuka 10 24 0 02 May 2019
On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models Erik Nijkamp Mitch Hill Tian Han Song-Chun Zhu Ying Nian Wu 19 151 0 29 Mar 2019
Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Learning Huy N. Chau M. Rásonyi 17 10 0 25 Mar 2019
Bayesian Cycle-Consistent Generative Adversarial Networks via Marginalizing Latent Sampling Haoran You Yu Cheng Tianheng Cheng Chunliang Li Pan Zhou GAN 13 3 0 19 Nov 2018
Metropolis-Hastings view on variational inference and adversarial training Kirill Neklyudov Evgenii Egorov Pavel Shvechikov Dmitry Vetrov GAN 13 13 0 16 Oct 2018
Deterministic Variational Inference for Robust Bayesian Neural Networks Anqi Wu Sebastian Nowozin Edward Meeds Richard E. Turner José Miguel Hernández-Lobato Alexander L. Gaunt UQCV AAML BDL 29 16 0 09 Oct 2018
Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory Jianyi Zhang Ruiyi Zhang Lawrence Carin Changyou Chen 10 46 0 05 Sep 2018
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo Marton Havasi José Miguel Hernández-Lobato J. J. Murillo-Fuentes BDL 11 96 0 14 Jun 2018