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"Why Should I Trust You?": Explaining the Predictions of Any Classifier

16 February 2016

Papers citing ""Why Should I Trust You?": Explaining the Predictions of Any Classifier"

50 / 4,966 papers shown

Title
TIP: Typifying the Interpretability of Procedures Amit Dhurandhar Vijay Iyengar Ronny Luss Karthikeyan Shanmugam 73 36 0 09 Jun 2017
Context-aware, Adaptive and Scalable Android Malware Detection through Online Learning (extended version) A. Narayanan Mahinthan Chandramohan Lihui Chen Yang Liu 49 87 0 03 Jun 2017
Contextual Explanation Networks Maruan Al-Shedivat Kumar Avinava Dubey Eric Xing CML 99 83 0 29 May 2017
Detecting and Explaining Crisis Rohan Kshirsagar R. Morris Samuel R. Bowman 36 28 0 26 May 2017
Interpreting Blackbox Models via Model Extraction Osbert Bastani Carolyn Kim Hamsa Bastani FAtt 132 173 0 23 May 2017
Explaining Transition Systems through Program Induction Svetlin Penkov S. Ramamoorthy 66 5 0 23 May 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.2K 22,232 0 22 May 2017
Real Time Image Saliency for Black Box Classifiers P. Dabkowski Y. Gal 72 595 0 22 May 2017
Induction of Interpretable Possibilistic Logic Theories from Relational Data Ondrej Kuzelka Jesse Davis Steven Schockaert NAI 128 12 0 19 May 2017
Detecting Statistical Interactions from Neural Network Weights Michael Tsang Dehua Cheng Yan Liu 72 192 0 14 May 2017
A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations Josua Krause Aritra Dasgupta Jordan Swartz Yindalon Aphinyanagphongs E. Bertini FAtt 71 96 0 04 May 2017
Translating Neuralese Jacob Andreas Anca Dragan Dan Klein 142 58 0 23 Apr 2017
Interpretable 3D Human Action Analysis with Temporal Convolutional Networks Tae Soo Kim A. Reiter 3DH 51 602 0 14 Apr 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 86 1,527 0 11 Apr 2017
PreCog: Improving Crowdsourced Data Quality Before Acquisition H. Nilforoshan Jiannan Wang Eugene Wu 24 3 0 07 Apr 2017
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models Minsuk Kahng Pierre Yves Andrews Aditya Kalro Duen Horng Chau HAI 96 324 0 06 Apr 2017
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization A. Narayanan Mahinthan Chandramohan Lihui Chen Yang Liu AAML 50 86 0 06 Apr 2017
It Takes Two to Tango: Towards Theory of AI's Mind Arjun Chandrasekaran Deshraj Yadav Prithvijit Chattopadhyay Viraj Prabhu Devi Parikh 108 54 0 03 Apr 2017
Comparing Rule-Based and Deep Learning Models for Patient Phenotyping Sebastian Gehrmann Franck Dernoncourt Yeran Li Eric T. Carlson Joy T. Wu ... J. Foote E. Moseley David W. Grant P. Tyler Leo Anthony Celi 81 41 0 25 Mar 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 232 2,913 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 143 594 0 10 Mar 2017
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly Ethan R. Elenberg A. Dimakis Moran Feldman Amin Karbasi 92 89 0 08 Mar 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 213 6,035 0 04 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 424 3,828 0 28 Feb 2017
Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations Upol Ehsan Brent Harrison Larry Chan Mark O. Riedl 139 221 0 25 Feb 2017
EVE: Explainable Vector Based Embedding Technique Using Wikipedia M. A. Qureshi Derek Greene 83 34 0 22 Feb 2017
Deep Reinforcement Learning: An Overview Yuxi Li OffRL VLM 304 1,548 0 25 Jan 2017
Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning Rock Stevens H. Aggarwal Himani Arora Sanghyun Hong M. Hicks Chetan Arora SILM AAML 54 18 0 17 Jan 2017
Deep Learning for Computational Chemistry Garrett B. Goh Nathan Oken Hodas Abhinav Vishnu AI4CE 94 679 0 17 Jan 2017
Identifying Best Interventions through Online Importance Sampling Rajat Sen Karthikeyan Shanmugam A. Dimakis Sanjay Shakkottai 96 73 0 10 Jan 2017
"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach L. Arras F. Horn G. Montavon K. Müller Wojciech Samek 77 288 0 23 Dec 2016
Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets L. Micallef Iiris Sundin Pekka Marttinen Muhammad Ammad-ud-din Tomi Peltola Marta Soare Giulio Jacucci Samuel Kaski 74 28 0 07 Dec 2016
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering Yash Goyal Tejas Khot D. Summers-Stay Dhruv Batra Devi Parikh CoGe 372 3,274 0 02 Dec 2016
Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation Wojciech Samek G. Montavon Alexander Binder Sebastian Lapuschkin K. Müller FAtt AI4CE 94 48 0 24 Nov 2016
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces Vernon J. Lawhern Amelia J. Solon Nicholas R. Waytowich Stephen M. Gordon C. Hung Brent Lance OOD 159 2,924 0 23 Nov 2016
Interpretation of Prediction Models Using the Input Gradient Yotam Hechtlinger FaML AI4CE FAtt 77 85 0 23 Nov 2016
Programs as Black-Box Explanations Sameer Singh Marco Tulio Ribeiro Carlos Guestrin FAtt 68 55 0 22 Nov 2016
An unexpected unity among methods for interpreting model predictions Scott M. Lundberg Su-In Lee FAtt 103 110 0 22 Nov 2016
Grad-CAM: Why did you say that? Ramprasaath R. Selvaraju Abhishek Das Ramakrishna Vedantam Michael Cogswell Devi Parikh Dhruv Batra FAtt 100 477 0 22 Nov 2016
Mapping chemical performance on molecular structures using locally interpretable explanations Leanne S. Whitmore Anthe George Corey M. Hudson FAtt 57 12 0 22 Nov 2016
"Influence Sketching": Finding Influential Samples In Large-Scale Regressions M. Wojnowicz Ben Cruz Xuan Zhao Brian Wallace Matt Wolff Jay Luan Caleb Crable TDI 89 32 0 17 Nov 2016
Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt 68 64 0 17 Nov 2016
Low-rank Bilinear Pooling for Fine-Grained Classification Shu Kong Charless C. Fowlkes 96 347 0 16 Nov 2016
Link Prediction using Embedded Knowledge Graphs Yelong Shen Po-Sen Huang Ming-Wei Chang Jianfeng Gao 90 26 0 14 Nov 2016
Gradients of Counterfactuals Mukund Sundararajan Ankur Taly Qiqi Yan FAtt 101 104 0 08 Nov 2016
Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration Himabindu Lakkaraju Ece Kamar R. Caruana Eric Horvitz 101 152 0 28 Oct 2016
Safety Verification of Deep Neural Networks Xiaowei Huang Marta Kwiatkowska Sen Wang Min Wu AAML 252 945 0 21 Oct 2016
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 373 20,193 0 07 Oct 2016
A deep learning model for estimating story points Morakot Choetkiertikul Hoa Dam T. Tran Trang Pham A. Ghose Tim Menzies 70 173 0 02 Sep 2016
Towards Transparent AI Systems: Interpreting Visual Question Answering Models Yash Goyal Akrit Mohapatra Devi Parikh Dhruv Batra 65 74 0 31 Aug 2016