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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 / 4,971 papers shown

Title
Interpretability with Accurate Small Models Abhishek Ghose Balaraman Ravindran 110 1 0 04 May 2019
Visualizing Deep Networks by Optimizing with Integrated Gradients Zhongang Qi Saeed Khorram Fuxin Li FAtt 83 124 0 02 May 2019
Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance Giles Hooker L. Mentch Siyu Zhou 93 159 0 01 May 2019
Interpretable multiclass classification by MDL-based rule lists Hugo Manuel Proença M. Leeuwen 56 48 0 01 May 2019
To believe or not to believe: Validating explanation fidelity for dynamic malware analysis Li-Wei Chen Carter Yagemann Evan Downing AAML FAtt 37 3 0 30 Apr 2019
Factor Analysis in Fault Diagnostics Using Random Forest Nagdev Amruthnath Tarun Gupta 14 10 0 30 Apr 2019
A scalable saliency-based Feature selection method with instance level information Brais Cancela V. Bolón-Canedo Amparo Alonso-Betanzos João Gama FAtt 62 13 0 30 Apr 2019
Adversarial Training and Robustness for Multiple Perturbations Florian Tramèr Dan Boneh AAML SILM 99 380 0 30 Apr 2019
"Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations Hui Fen Tan Kuangyan Song Yiming Sun Yujia Zhang Madeilene Udell FAtt 115 19 0 29 Apr 2019
Evaluating Recurrent Neural Network Explanations L. Arras Ahmed Osman K. Müller Wojciech Samek XAI FAtt 117 88 0 26 Apr 2019
Applying machine learning to improve simulations of a chaotic dynamical system using empirical error correction P. Watson AI4Cl AI4CE 64 65 0 24 Apr 2019
Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning Peng Xu Zhaohong Deng Chen Cui Te Zhang K. Choi Suhang Gu Jun Wang Shitong Wang 46 32 0 24 Apr 2019
Explaining a prediction in some nonlinear models Cosimo Izzo FAtt 19 0 0 21 Apr 2019
Explaining Deep Classification of Time-Series Data with Learned Prototypes Alan H. Gee Diego Garcia-Olano Joydeep Ghosh D. Paydarfar AI4TS 103 67 0 18 Apr 2019
"Why did you do that?": Explaining black box models with Inductive Synthesis Görkem Paçaci David Johnson S. McKeever A. Hamfelt 35 6 0 17 Apr 2019
Explainability in Human-Agent Systems A. Rosenfeld A. Richardson XAI 83 206 0 17 Apr 2019
HARK Side of Deep Learning -- From Grad Student Descent to Automated Machine Learning O. Gencoglu M. Gils E. Guldogan Chamin Morikawa Mehmet Süzen M. Gruber J. Leinonen H. Huttunen 98 36 0 16 Apr 2019
Counterfactual Visual Explanations Yash Goyal Ziyan Wu Jan Ernst Dhruv Batra Devi Parikh Stefan Lee CML 95 511 0 16 Apr 2019
Enhancing Decision Tree based Interpretation of Deep Neural Networks through L1-Orthogonal Regularization Nina Schaaf Marco F. Huber Johannes Maucher 110 36 0 10 Apr 2019
Enhancing Time Series Momentum Strategies Using Deep Neural Networks Bryan Lim S. Zohren Stephen J. Roberts AIFin AI4TS 72 90 0 09 Apr 2019
Software and application patterns for explanation methods Maximilian Alber 80 11 0 09 Apr 2019
Regression Concept Vectors for Bidirectional Explanations in Histopathology Mara Graziani Vincent Andrearczyk Henning Muller 91 81 0 09 Apr 2019
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations Christian A. Scholbeck Christoph Molnar C. Heumann B. Bischl Giuseppe Casalicchio 100 27 0 08 Apr 2019
Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability Christoph Molnar Giuseppe Casalicchio B. Bischl FAtt 54 60 0 08 Apr 2019
Visualization of Convolutional Neural Networks for Monocular Depth Estimation Junjie Hu Yan Zhang Takayuki Okatani MDE 124 83 0 06 Apr 2019
A Categorisation of Post-hoc Explanations for Predictive Models John Mitros Brian Mac Namee XAI CML 28 1 0 04 Apr 2019
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations Fred Hohman Haekyu Park Caleb Robinson Duen Horng Chau FAtt 3DH HAI 90 217 0 04 Apr 2019
Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions Li-Wei Chen AAML 28 1 0 03 Apr 2019
Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks Woo-Jeoung Nam Shir Gur Jaesik Choi Lior Wolf Seong-Whan Lee FAtt 80 99 0 01 Apr 2019
VINE: Visualizing Statistical Interactions in Black Box Models M. Britton FAtt 63 22 0 01 Apr 2019
Interpreting Black Box Models via Hypothesis Testing Collin Burns Jesse Thomason Wesley Tansey FAtt 65 9 0 29 Mar 2019
Do Not Trust Additive Explanations Alicja Gosiewska P. Biecek 73 42 0 27 Mar 2019
On Attribution of Recurrent Neural Network Predictions via Additive Decomposition Mengnan Du Ninghao Liu Fan Yang Shuiwang Ji Helen Zhou FAtt 71 51 0 27 Mar 2019
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation Marco Ancona Cengiz Öztireli Markus Gross FAtt TDI 121 229 0 26 Mar 2019
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values K. Aas Martin Jullum Anders Løland FAtt TDI 90 633 0 25 Mar 2019
On the Robustness of Deep K-Nearest Neighbors Chawin Sitawarin David Wagner AAML OOD 140 58 0 20 Mar 2019
NeuralHydrology -- Interpreting LSTMs in Hydrology Frederik Kratzert M. Herrnegger D. Klotz Sepp Hochreiter Günter Klambauer 60 86 0 19 Mar 2019
Natural Language Interaction with Explainable AI Models Arjun Reddy Akula S. Todorovic J. Chai Song-Chun Zhu 74 23 0 13 Mar 2019
GNNExplainer: Generating Explanations for Graph Neural Networks Rex Ying Dylan Bourgeois Jiaxuan You Marinka Zitnik J. Leskovec LLMAG 163 1,336 0 10 Mar 2019
Explaining Anomalies Detected by Autoencoders Using SHAP Liat Antwarg Ronnie Mindlin Miller Bracha Shapira Lior Rokach FAtt TDI 74 86 0 06 Mar 2019
Copying Machine Learning Classifiers Irene Unceta Jordi Nin O. Pujol 96 18 0 05 Mar 2019
Deep learning in bioinformatics: introduction, application, and perspective in big data era Yu Li Chao Huang Lizhong Ding Zhongxiao Li Yijie Pan Xin Gao AI4CE 96 302 0 28 Feb 2019
Reliable Deep Grade Prediction with Uncertainty Estimation Qian Hu Huzefa Rangwala 53 39 0 26 Feb 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin S. Wäldchen Alexander Binder G. Montavon Wojciech Samek K. Müller 106 1,022 0 26 Feb 2019
Functional Transparency for Structured Data: a Game-Theoretic Approach Guang-He Lee Wengong Jin David Alvarez-Melis Tommi Jaakkola 67 19 0 26 Feb 2019
Saliency Learning: Teaching the Model Where to Pay Attention Reza Ghaeini Xiaoli Z. Fern Hamed Shahbazi Prasad Tadepalli FAtt XAI 102 31 0 22 Feb 2019
Explaining a black-box using Deep Variational Information Bottleneck Approach Seo-Jin Bang P. Xie Heewook Lee Wei Wu Eric Xing XAI FAtt 77 77 0 19 Feb 2019
Regularizing Black-box Models for Improved Interpretability Gregory Plumb Maruan Al-Shedivat Ángel Alexander Cabrera Adam Perer Eric Xing Ameet Talwalkar AAML 95 80 0 18 Feb 2019
STRIP: A Defence Against Trojan Attacks on Deep Neural Networks Yansong Gao Chang Xu Derui Wang Shiping Chen Damith C. Ranasinghe Surya Nepal AAML 84 819 0 18 Feb 2019
Significance Tests for Neural Networks Enguerrand Horel K. Giesecke 57 56 0 16 Feb 2019