Explaining Bayesian Neural Networks

Explaining Bayesian Neural Networks

23 August 2021

Marina M.-C. Höhne

Adelaida Creosteanu

Klaus-Robert Muller

Frederick Klauschen

Shinichi Nakajima

Papers citing "Explaining Bayesian Neural Networks"

17 / 17 papers shown

Title
Efficient Model Compression for Bayesian Neural Networks Diptarka Saha Zihe Liu Feng Liang BDL 19 0 0 01 Nov 2024
Sanity Checks for Explanation Uncertainty Matias Valdenegro-Toro Mihir Mulye FAtt 18 0 0 25 Mar 2024
Uncertainty Quantification for Gradient-based Explanations in Neural Networks Mihir Mulye Matias Valdenegro-Toro UQCV FAtt 31 0 0 25 Mar 2024
Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs Elham Kiyani M. Kooshkbaghi K. Shukla R. Koneru Zhen Li L. Bravo A. Ghoshal George Karniadakis M. Karttunen AI4CE 17 4 0 18 Jul 2023
Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science P. Bommer M. Kretschmer Anna Hedström Dilyara Bareeva Marina M.-C. Höhne 24 37 0 01 Mar 2023
BALANCE: Bayesian Linear Attribution for Root Cause Localization Chaoyu Chen Hang Yu Zhichao Lei Jianguo Li Shaokang Ren Tingkai Zhang Si-Yu Hu Jianchao Wang Wenhui Shi 21 7 0 31 Jan 2023
DORA: Exploring Outlier Representations in Deep Neural Networks Kirill Bykov Mayukh Deb Dennis Grinwald Klaus-Robert Muller Marina M.-C. Höhne 11 12 0 09 Jun 2022
Explainable Deep Learning Methods in Medical Image Classification: A Survey Cristiano Patrício João C. Neves Luís F. Teixeira XAI 11 52 0 10 May 2022
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond Anna Hedström Leander Weber Dilyara Bareeva Daniel G. Krakowczyk Franz Motzkus Wojciech Samek Sebastian Lapuschkin Marina M.-C. Höhne XAI ELM 11 167 0 14 Feb 2022
Visualizing the Diversity of Representations Learned by Bayesian Neural Networks Dennis Grinwald Kirill Bykov Shinichi Nakajima Marina M.-C. Höhne 8 5 0 26 Jan 2022
Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model Jing Zhang Yuchao Dai Mehrtash Harandi Yiran Zhong Nick Barnes Richard I. Hartley UQCV 11 1 0 22 Nov 2021
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout Yamen Ali Aiham Taleb Marina M.-C. Höhne C. Lippert SSL 3DPC 11 6 0 29 Sep 2021
NoiseGrad: Enhancing Explanations by Introducing Stochasticity to Model Weights Kirill Bykov Anna Hedström Shinichi Nakajima Marina M.-C. Höhne FAtt 6 34 0 18 Jun 2021
Fixing the train-test resolution discrepancy: FixEfficientNet Hugo Touvron Andrea Vedaldi Matthijs Douze Hervé Jégou AAML 176 110 0 18 Mar 2020
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism M. Shoeybi M. Patwary Raul Puri P. LeGresley Jared Casper Bryan Catanzaro MoE 243 1,791 0 17 Sep 2019
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 228 2,231 0 24 Jun 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,042 0 06 Jun 2015