CXPlain: Causal Explanations for Model Interpretation under Uncertainty

27 October 2019

Papers citing "CXPlain: Causal Explanations for Model Interpretation under Uncertainty"

8 / 108 papers shown

Title
An interpretable neural network model through piecewise linear approximation Mengzhuo Guo Qingpeng Zhang Xiuwu Liao D. Zeng MILM FAtt 19 7 0 20 Jan 2020
A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data Patrick Schwab W. Karlen 33 24 0 02 Jan 2020
Contextual Prediction Difference Analysis for Explaining Individual Image Classifications Jindong Gu Volker Tresp FAtt 18 8 0 21 Oct 2019
Learning Decision Trees Recurrently Through Communication Stephan Alaniz Diego Marcos Bernt Schiele Zeynep Akata 30 16 0 05 Feb 2019
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves Patrick Schwab Lorenz Linhardt Stefan Bauer J. M. Buhmann W. Karlen CML OOD 29 131 0 03 Feb 2019
MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis Rushil Anirudh Jayaraman J. Thiagarajan R. Sridhar T. Bremer FAtt AAML 23 12 0 15 Nov 2017
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