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A Unified Approach to Interpreting Model Predictions

22 May 2017

Papers citing "A Unified Approach to Interpreting Model Predictions"

50 / 3,916 papers shown

Title
Efficient Search for Diverse Coherent Explanations Chris Russell 80 241 0 02 Jan 2019
Explanatory Graphs for CNNs Quanshi Zhang Xin Eric Wang Ruiming Cao Ying Nian Wu Feng Shi Song-Chun Zhu FAtt GNN 44 3 0 18 Dec 2018
Explaining Neural Networks Semantically and Quantitatively Runjin Chen Hao Chen Ge Huang Jie Ren Quanshi Zhang FAtt 62 56 0 18 Dec 2018
Interactive Naming for Explaining Deep Neural Networks: A Formative Study M. Hamidi-Haines Zhongang Qi Alan Fern Fuxin Li Prasad Tadepalli FAtt HAI 50 11 0 18 Dec 2018
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability Xiaowei Huang Daniel Kroening Wenjie Ruan Marta Kwiatkowska Youcheng Sun Emese Thamo Min Wu Xinping Yi AAML 132 51 0 18 Dec 2018
Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery Xiaoxiao Li Nicha Dvornek Yuan Zhou Juntang Zhuang P. Ventola James S. Duncan 462 19 0 14 Dec 2018
Can I trust you more? Model-Agnostic Hierarchical Explanations Michael Tsang Youbang Sun Dongxu Ren Yan Liu FAtt 53 26 0 12 Dec 2018
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems Sina Mohseni Niloofar Zarei Eric D. Ragan 122 102 0 28 Nov 2018
Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions Denis A. Gudovskiy Alec Hodgkinson Takuya Yamaguchi Yasunori Ishii Sotaro Tsukizawa FAtt 74 13 0 19 Nov 2018
On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection Vivian Lai Chenhao Tan 98 380 0 19 Nov 2018
Interpretable Credit Application Predictions With Counterfactual Explanations Rory Mc Grath Luca Costabello Chan Le Van Paul Sweeney F. Kamiab Zhao Shen Freddy Lecue FAtt 83 110 0 13 Nov 2018
TED: Teaching AI to Explain its Decisions Michael Hind Dennis L. Wei Murray Campbell Noel Codella Amit Dhurandhar Aleksandra Mojsilović Karthikeyan N. Ramamurthy Kush R. Varshney 80 111 0 12 Nov 2018
Explaining Deep Learning Models - A Bayesian Non-parametric Approach Wenbo Guo Sui Huang Yunzhe Tao Masashi Sugiyama Lin Lin BDL 56 47 0 07 Nov 2018
Deep Weighted Averaging Classifiers Dallas Card Michael J.Q. Zhang Hao Tang 96 41 0 06 Nov 2018
Progressive Disclosure: Designing for Effective Transparency Aaron Springer Ling Huang 65 16 0 06 Nov 2018
"I had a solid theory before but it's falling apart": Polarizing Effects of Algorithmic Transparency Aaron Springer S. Whittaker 32 6 0 06 Nov 2018
Explaining Explanations in AI Brent Mittelstadt Chris Russell Sandra Wachter XAI 135 666 0 04 Nov 2018
Explaining Machine Learning Models using Entropic Variable Projection François Bachoc Fabrice Gamboa Max Halford Jean-Michel Loubes Laurent Risser FAtt 87 5 0 18 Oct 2018
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values Julius Adebayo Justin Gilmer Ian Goodfellow Been Kim FAtt AAML 82 129 0 08 Oct 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 208 1,973 0 08 Oct 2018
On the Art and Science of Machine Learning Explanations Patrick Hall FAtt XAI 92 30 0 05 Oct 2018
Local Interpretable Model-agnostic Explanations of Bayesian Predictive Models via Kullback-Leibler Projections Tomi Peltola FAtt BDL 76 40 0 05 Oct 2018
Interpreting Layered Neural Networks via Hierarchical Modular Representation C. Watanabe 84 19 0 03 Oct 2018
A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees Klaus Broelemann Gjergji Kasneci 84 20 0 25 Sep 2018
An Adaptive Locally Connected Neuron Model: Focusing Neuron F. Boray Tek 29 6 0 31 Aug 2018
Shedding Light on Black Box Machine Learning Algorithms: Development of an Axiomatic Framework to Assess the Quality of Methods that Explain Individual Predictions Milo Honegger 52 35 0 15 Aug 2018
iNNvestigate neural networks! Maximilian Alber Sebastian Lapuschkin P. Seegerer Miriam Hagele Kristof T. Schütt G. Montavon Wojciech Samek K. Müller Sven Dähne Pieter-Jan Kindermans 79 349 0 13 Aug 2018
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data Jianbo Chen Le Song Martin J. Wainwright Michael I. Jordan FAtt TDI 117 217 0 08 Aug 2018
Techniques for Interpretable Machine Learning Mengnan Du Ninghao Liu Helen Zhou FaML 101 1,097 0 31 Jul 2018
Explicating feature contribution using Random Forest proximity distances Leanne S. Whitmore Anthe George Corey M. Hudson FAtt 59 7 0 17 Jul 2018
A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees Min Wu Matthew Wicker Wenjie Ruan Xiaowei Huang Marta Kwiatkowska AAML 91 111 0 10 Jul 2018
Model Agnostic Supervised Local Explanations Gregory Plumb Denali Molitor Ameet Talwalkar FAtt LRM MILM 171 200 0 09 Jul 2018
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices Till Speicher Hoda Heidari Nina Grgic-Hlaca Krishna P. Gummadi Adish Singla Adrian Weller Muhammad Bilal Zafar FaML 106 265 0 02 Jul 2018
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities Marinka Zitnik Francis Nguyen Bo Wang J. Leskovec Anna Goldenberg Michael M. Hoffman LM&MA AI4CE 77 467 0 30 Jun 2018
Deep learning in business analytics and operations research: Models, applications and managerial implications Mathias Kraus Stefan Feuerriegel A. Oztekin 77 295 0 28 Jun 2018
Optimal Piecewise Local-Linear Approximations Kartik Ahuja W. Zame M. Schaar FAtt 57 1 0 27 Jun 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 121 529 0 21 Jun 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 140 948 0 20 Jun 2018
Instance-Level Explanations for Fraud Detection: A Case Study Dennis Collaris L. M. Vink J. V. Wijk 81 31 0 19 Jun 2018
Binary Classification in Unstructured Space With Hypergraph Case-Based Reasoning Alexandre Quemy 40 7 0 16 Jun 2018
Hierarchical interpretations for neural network predictions Chandan Singh W. James Murdoch Bin Yu 84 146 0 14 Jun 2018
A Note about: Local Explanation Methods for Deep Neural Networks lack Sensitivity to Parameter Values Mukund Sundararajan Ankur Taly FAtt 46 21 0 11 Jun 2018
Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP) Linwei Hu Jie Chen V. Nair Agus Sudjianto FAtt 69 64 0 02 Jun 2018
How Important Is a Neuron? Kedar Dhamdhere Mukund Sundararajan Qiqi Yan FAtt GNN 77 131 0 30 May 2018
Teaching Meaningful Explanations Noel Codella Michael Hind Karthikeyan N. Ramamurthy Murray Campbell Amit Dhurandhar Kush R. Varshney Dennis L. Wei Aleksandra Mojsilović FAtt XAI 63 7 0 29 May 2018
Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models Jacob R. Kauffmann K. Müller G. Montavon DRL 77 98 0 16 May 2018
A Symbolic Approach to Explaining Bayesian Network Classifiers Andy Shih Arthur Choi Adnan Darwiche FAtt 93 243 0 09 May 2018
Visualizing the Feature Importance for Black Box Models Giuseppe Casalicchio Christoph Molnar B. Bischl FAtt 49 183 0 18 Apr 2018
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the $L_0$ Norm Wenjie Ruan Min Wu Youcheng Sun Xiaowei Huang Daniel Kroening Marta Kwiatkowska AAML 65 39 0 16 Apr 2018
Understanding Community Structure in Layered Neural Networks C. Watanabe Kaoru Hiramatsu K. Kashino 138 22 0 13 Apr 2018