Quantifying Interpretability and Trust in Machine Learning Systems

20 January 2019

Papers citing "Quantifying Interpretability and Trust in Machine Learning Systems"

16 / 16 papers shown

Title
Less is More: The Influence of Pruning on the Explainability of CNNs David Weber F. Merkle Pascal Schöttle Stephan Schlögl Martin Nocker FAtt 173 1 0 17 Feb 2023
The Promise and Peril of Human Evaluation for Model Interpretability Bernease Herman 69 144 0 20 Nov 2017
The Doctor Just Won't Accept That! Zachary Chase Lipton FaML 63 101 0 20 Nov 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 254 4,281 0 22 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 22,090 0 22 May 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 219 2,910 0 14 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 410 3,820 0 28 Feb 2017
The Mythos of Model Interpretability Zachary Chase Lipton FaML 183 3,708 0 10 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,071 0 16 Feb 2016
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition G. Montavon Sebastian Lapuschkin Alexander Binder Wojciech Samek Klaus-Robert Muller FAtt 68 739 0 08 Dec 2015
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model Benjamin Letham Cynthia Rudin Tyler H. McCormick D. Madigan FAtt 72 743 0 05 Nov 2015
Evaluating the visualization of what a Deep Neural Network has learned Wojciech Samek Alexander Binder G. Montavon Sebastian Lapuschkin K. Müller XAI 139 1,200 0 21 Sep 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 282 19,129 0 20 Dec 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 314 7,321 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 603 15,907 0 12 Nov 2013
The Feature Importance Ranking Measure A. Zien Nicole Krämer Soeren Sonnenburg Gunnar Rätsch FAtt 102 147 0 23 Jun 2009