Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting

14 October 2020

Papers citing "Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting"

16 / 16 papers shown

Title
Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models Daniela de Albuquerque John Pearson DiffM 64 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 49 4 0 14 Oct 2024
Gradient-free variational learning with conditional mixture networks Conor Heins Hao Wu Dimitrije Marković Alexander Tschantz Jeff Beck Christopher L. Buckley BDL 31 2 0 29 Aug 2024
CONFINE: Conformal Prediction for Interpretable Neural Networks Linhui Huang S. Lala N. Jha 68 2 0 01 Jun 2024
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks Yunzhen Feng Tim G. J. Rudner Nikolaos Tsilivis Julia Kempe AAML BDL 43 1 0 27 Apr 2024
Practical Layout-Aware Analog/Mixed-Signal Design Automation with Bayesian Neural Networks A. Budak Keren Zhu Yao Lai 19 3 0 27 Nov 2023
Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference Arnaud Descours Tom Huix Arnaud Guillin Manon Michel Eric Moulines Boris Nectoux BDL 32 1 0 10 Jul 2023
Quantification of Uncertainty with Adversarial Models Kajetan Schweighofer L. Aichberger Mykyta Ielanskyi G. Klambauer Sepp Hochreiter UQCV 42 14 0 06 Jul 2023
Gibbs Sampling the Posterior of Neural Networks Giovanni Piccioli Emanuele Troiani Lenka Zdeborová 41 2 0 05 Jun 2023
On Sequential Bayesian Inference for Continual Learning Samuel Kessler Adam D. Cobb Tim G. J. Rudner S. Zohren Stephen J. Roberts CLL BDL 64 6 0 04 Jan 2023
Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study David Ruhe Kaze W. K. Wong M. Cranmer Patrick Forré 16 6 0 15 Nov 2022
Split personalities in Bayesian Neural Networks: the case for full marginalisation David Yallup Will Handley Michael P. Hobson A. Lasenby Pablo Lemos 25 1 0 23 May 2022
LEO: Learning Energy-based Models in Factor Graph Optimization Paloma Sodhi Eric Dexheimer Mustafa Mukadam Stuart Anderson Michael Kaess 40 16 0 04 Aug 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
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,675 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,145 0 06 Jun 2015