Leveraging Explanations in Interactive Machine Learning: An Overview

29 July 2022

Papers citing "Leveraging Explanations in Interactive Machine Learning: An Overview"

25 / 125 papers shown

Title
Teaching Categories to Human Learners with Visual Explanations Oisin Mac Aodha Shihan Su Yuxin Chen Pietro Perona Yisong Yue 97 70 0 20 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 81 3,922 0 06 Feb 2018
Plan Explanations as Model Reconciliation -- An Empirical Study Tathagata Chakraborti S. Sreedharan Sachin Grover S. Kambhampati 54 47 0 03 Feb 2018
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation Menaka Narayanan Emily Chen Jeffrey He Been Kim S. Gershman Finale Doshi-Velez FAtt XAI 83 241 0 02 Feb 2018
Snorkel: Rapid Training Data Creation with Weak Supervision Alexander Ratner Stephen H. Bach Henry R. Ehrenberg Jason Alan Fries Sen Wu Christopher Ré 62 1,021 0 28 Nov 2017
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability Mike Wu M. C. Hughes S. Parbhoo Maurizio Zazzi Volker Roth Finale Doshi-Velez AI4CE 114 281 0 16 Nov 2017
The (Un)reliability of saliency methods Pieter-Jan Kindermans Sara Hooker Julius Adebayo Maximilian Alber Kristof T. Schütt Sven Dähne D. Erhan Been Kim FAtt XAI 79 683 0 02 Nov 2017
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 73 2,332 0 01 Nov 2017
Human-in-the-loop Artificial Intelligence Fabio Massimo Zanzotto 51 266 0 23 Oct 2017
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 265 2,248 0 24 Jun 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 217 4,229 0 22 Jun 2017
Attention Is All You Need Ashish Vaswani Noam M. Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan Gomez Lukasz Kaiser Illia Polosukhin 3DV 435 129,831 0 12 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 524 21,613 0 22 May 2017
Learning Certifiably Optimal Rule Lists for Categorical Data E. Angelino Nicholas Larus-Stone Daniel Alabi Margo Seltzer Cynthia Rudin 73 195 0 06 Apr 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 134 2,854 0 14 Mar 2017
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations A. Ross M. C. Hughes Finale Doshi-Velez FAtt 103 585 0 10 Mar 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 115 5,920 0 04 Mar 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 209 19,796 0 07 Oct 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 577 16,828 0 16 Feb 2016
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification Been Kim Cynthia Rudin J. Shah 50 321 0 03 Mar 2015
Supersparse Linear Integer Models for Optimized Medical Scoring Systems Berk Ustun Cynthia Rudin 58 352 0 15 Feb 2015
Neural Machine Translation by Jointly Learning to Align and Translate Dzmitry Bahdanau Kyunghyun Cho Yoshua Bengio AIMat 383 27,205 0 01 Sep 2014
Interpreting Tree Ensembles with inTrees Houtao Deng 65 241 0 23 Aug 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 167 7,252 0 20 Dec 2013
How to Explain Individual Classification Decisions D. Baehrens T. Schroeter Stefan Harmeling M. Kawanabe K. Hansen K. Müller FAtt 104 1,098 0 06 Dec 2009