Explaining Deep Learning Models - A Bayesian Non-parametric Approach

7 November 2018

Papers citing "Explaining Deep Learning Models - A Bayesian Non-parametric Approach"

9 / 9 papers shown

Title
Generative Perturbation Analysis for Probabilistic Black-Box Anomaly Attribution T. Idé Naoki Abe 43 4 0 09 Aug 2023
A Survey on Graph-Based Deep Learning for Computational Histopathology David Ahmedt-Aristizabal M. Armin Simon Denman Clinton Fookes L. Petersson GNN AI4CE 26 108 0 01 Jul 2021
Parameterized Explainer for Graph Neural Network Dongsheng Luo Wei Cheng Dongkuan Xu Wenchao Yu Bo Zong Haifeng Chen Xiang Zhang 53 542 0 09 Nov 2020
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability Dylan Slack Sophie Hilgard Sameer Singh Himabindu Lakkaraju FAtt 29 162 0 11 Aug 2020
Generative causal explanations of black-box classifiers Matthew R. O’Shaughnessy Gregory H. Canal Marissa Connor Mark A. Davenport Christopher Rozell CML 30 73 0 24 Jun 2020
TSInsight: A local-global attribution framework for interpretability in time-series data Shoaib Ahmed Siddiqui Dominique Mercier Andreas Dengel Sheraz Ahmed FAtt AI4TS 16 12 0 06 Apr 2020
Interpretability of Blackbox Machine Learning Models through Dataview Extraction and Shadow Model creation Rupam Patir Shubham Singhal C. Anantaram Vikram Goyal 16 0 0 02 Feb 2020
Automated Dependence Plots David I. Inouye Liu Leqi Joon Sik Kim Bryon Aragam Pradeep Ravikumar 12 1 0 02 Dec 2019
CXPlain: Causal Explanations for Model Interpretation under Uncertainty Patrick Schwab W. Karlen FAtt CML 40 205 0 27 Oct 2019