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Credal Bayesian Deep Learning

19 February 2023

Papers citing "Credal Bayesian Deep Learning"

30 / 30 papers shown

Title
CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks Kaizheng Wang Keivan K1 Shariatmadar Shireen Kudukkil Manchingal Fabio Cuzzolin David Moens Hans Hallez UQCV BDL 242 14 0 28 Jan 2025
Conformalized Credal Regions for Classification with Ambiguous Ground Truth Michele Caprio David Stutz Shuo Li Arnaud Doucet UQCV 205 5 0 07 Nov 2024
Using Semantic Information for Defining and Detecting OOD Inputs Ramneet Kaur Xiayan Ji Souradeep Dutta Michele Caprio Yahan Yang E. Bernardis O. Sokolsky Insup Lee OODD 71 7 0 21 Feb 2023
Physics Constrained Motion Prediction with Uncertainty Quantification Renukanandan Tumu Lars Lindemann Truong X. Nghiem Rahul Mangharam 65 19 0 02 Feb 2023
An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers Thierry Denoeux UQCV 135 12 0 01 Aug 2022
Neural network model for imprecise regression with interval dependent variables K. Tretiak G. Schollmeyer S. Ferson 44 13 0 06 Jun 2022
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification Sanyam Kapoor Wesley J. Maddox Pavel Izmailov A. Wilson BDL UD 76 51 0 30 Mar 2022
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification Romain Egele R. Maulik Krishnan Raghavan Bethany Lusch Isabelle M Guyon Prasanna Balaprakash UQCV OOD BDL 123 47 0 26 Oct 2021
Dynamic Precise and Imprecise Probability Kinematics Michele Caprio Ruobin Gong 39 10 0 08 Oct 2021
BNNpriors: A library for Bayesian neural network inference with different prior distributions Vincent Fortuin Adrià Garriga-Alonso Mark van der Wilk Laurence Aitchison BDL UQCV 58 25 0 14 May 2021
What Are Bayesian Neural Network Posteriors Really Like? Pavel Izmailov Sharad Vikram Matthew D. Hoffman A. Wilson UQCV BDL 72 385 0 29 Apr 2021
Distances between probability distributions of different dimensions Yuhan Cai Lek-Heng Lim UD OOD 61 50 0 01 Nov 2020
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup Jang-Hyun Kim Wonho Choo Hyun Oh Song AAML 89 390 0 15 Sep 2020
Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users Laurent Valentin Jospin Wray Buntine F. Boussaïd Hamid Laga Bennamoun OOD BDL UQCV 84 628 0 14 Jul 2020
Bayesian Neural Networks: An Introduction and Survey Ethan Goan Clinton Fookes BDL UQCV 62 205 0 22 Jun 2020
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts Bertrand Charpentier Daniel Zügner Stephan Günnemann UQCV UD EDL BDL 72 184 0 16 Jun 2020
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction Sangdon Park Osbert Bastani Nikolai Matni Insup Lee UQCV 224 70 0 31 Dec 2019
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods Eyke Hüllermeier Willem Waegeman PER UD 244 1,415 0 21 Oct 2019
Deep Evidential Regression Alexander Amini Wilko Schwarting A. Soleimany Daniela Rus EDL PER BDL UD UQCV 81 440 0 07 Oct 2019
MNIST-C: A Robustness Benchmark for Computer Vision Norman Mu Justin Gilmer 57 212 0 05 Jun 2019
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations Dan Hendrycks Thomas G. Dietterich OOD VLM 174 3,435 0 28 Mar 2019
Evidential Deep Learning to Quantify Classification Uncertainty Murat Sensoy Lance M. Kaplan M. Kandemir OOD UQCV EDL BDL 182 996 0 05 Jun 2018
Understanding Measures of Uncertainty for Adversarial Example Detection Lewis Smith Y. Gal UQCV 91 362 0 22 Mar 2018
Judicious Judgment Meets Unsettling Updating: Dilation, Sure Loss, and Simpson's Paradox Ruobin Gong Xiangxu Meng 35 37 0 24 Dec 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 283 8,904 0 25 Aug 2017
On Calibration of Modern Neural Networks Chuan Guo Geoff Pleiss Yu Sun Kilian Q. Weinberger UQCV 299 5,833 0 14 Jun 2017
Model Selection in Bayesian Neural Networks via Horseshoe Priors S. Ghosh Finale Doshi-Velez BDL 67 119 0 29 May 2017
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? Alex Kendall Y. Gal BDL OOD UD UQCV PER 356 4,709 0 15 Mar 2017
Large-Sample Learning of Bayesian Networks is NP-Hard D. M. Chickering Christopher Meek David Heckerman BDL 129 795 0 19 Oct 2012
A tutorial on conformal prediction Glenn Shafer V. Vovk 452 1,144 0 21 Jun 2007