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Axiomatic Attribution for Deep Networks

4 March 2017

Ankur Taly

Papers citing "Axiomatic Attribution for Deep Networks"

50 / 2,871 papers shown

Title
Uncertainty Propagation in Deep Neural Network Using Active Subspace Weiqi Ji Zhuyin Ren C. Law UQCV 31 5 0 10 Mar 2019
GNNExplainer: Generating Explanations for Graph Neural Networks Rex Ying Dylan Bourgeois Jiaxuan You Marinka Zitnik J. Leskovec LLMAG 163 1,339 0 10 Mar 2019
Interpreting and Understanding Graph Convolutional Neural Network using Gradient-based Attribution Method Shangsheng Xie Mingming Lu FAtt GNN 78 16 0 09 Mar 2019
Interpretable Deep Learning in Drug Discovery Kristina Preuer Günter Klambauer F. Rippmann Sepp Hochreiter Thomas Unterthiner 83 89 0 07 Mar 2019
Adversarial Examples on Graph Data: Deep Insights into Attack and Defense Huijun Wu Chen Wang Y. Tyshetskiy Andrew Docherty Kai Lu Liming Zhu AAML GNN 38 6 0 05 Mar 2019
Aggregating explanation methods for stable and robust explainability Laura Rieger Lars Kai Hansen AAML FAtt 57 12 0 01 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 99 302 0 28 Feb 2019
Attention is not Explanation Sarthak Jain Byron C. Wallace FAtt 159 1,332 0 26 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 125 80 0 18 Feb 2019
Significance Tests for Neural Networks Enguerrand Horel K. Giesecke 57 56 0 16 Feb 2019
Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps Beomsu Kim Junghoon Seo Seunghyun Jeon Jamyoung Koo J. Choe Taegyun Jeon FAtt 74 70 0 13 Feb 2019
LS-Tree: Model Interpretation When the Data Are Linguistic Jianbo Chen Michael I. Jordan 64 18 0 11 Feb 2019
Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded Ramprasaath R. Selvaraju Stefan Lee Yilin Shen Hongxia Jin Shalini Ghosh Larry Heck Dhruv Batra Devi Parikh FAtt VLM 76 255 0 11 Feb 2019
Global Explanations of Neural Networks: Mapping the Landscape of Predictions Mark Ibrahim Melissa Louie C. Modarres John Paisley FAtt 97 119 0 06 Feb 2019
Neural Network Attributions: A Causal Perspective Aditya Chattopadhyay Piyushi Manupriya Anirban Sarkar V. Balasubramanian CML 102 146 0 06 Feb 2019
Fooling Neural Network Interpretations via Adversarial Model Manipulation Juyeon Heo Sunghwan Joo Taesup Moon AAML FAtt 129 206 0 06 Feb 2019
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation Sahil Singla Eric Wallace Shi Feng Soheil Feizi FAtt 71 60 0 01 Feb 2019
Interpreting Deep Neural Networks Through Variable Importance J. Ish-Horowicz Dana Udwin Seth Flaxman Sarah Filippi Lorin Crawford FAtt 63 14 0 28 Jan 2019
Testing Conditional Independence in Supervised Learning Algorithms David S. Watson Marvin N. Wright CML 98 53 0 28 Jan 2019
On the (In)fidelity and Sensitivity for Explanations Chih-Kuan Yeh Cheng-Yu Hsieh A. Suggala David I. Inouye Pradeep Ravikumar FAtt 110 456 0 27 Jan 2019
Unsupervised speech representation learning using WaveNet autoencoders J. Chorowski Ron J. Weiss Samy Bengio Aaron van den Oord SSL 76 319 0 25 Jan 2019
Learning Global Pairwise Interactions with Bayesian Neural Networks Tianyu Cui Pekka Marttinen Samuel Kaski BDL 81 17 0 24 Jan 2019
On Network Science and Mutual Information for Explaining Deep Neural Networks Brian Davis Umang Bhatt Kartikeya Bhardwaj R. Marculescu J. M. F. Moura FedML SSL FAtt 55 10 0 20 Jan 2019
Towards Aggregating Weighted Feature Attributions Umang Bhatt Pradeep Ravikumar José M. F. Moura FAtt TDI 34 13 0 20 Jan 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin Yu XAI HAI 211 1,459 0 14 Jan 2019
Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries Christian Scano Battista Biggio Giovanni Lagorio Fabio Roli A. Armando AAML 80 131 0 11 Jan 2019
Explaining Aggregates for Exploratory Analytics Fotis Savva Christos Anagnostopoulos Peter Triantafillou 63 18 0 29 Dec 2018
Feature-Wise Bias Amplification Klas Leino Emily Black Matt Fredrikson S. Sen Anupam Datta FaML 100 47 0 21 Dec 2018
Analysis Methods in Neural Language Processing: A Survey Yonatan Belinkov James R. Glass 125 558 0 21 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
Can I trust you more? Model-Agnostic Hierarchical Explanations Michael Tsang Youbang Sun Dongxu Ren Yan Liu FAtt 53 26 0 12 Dec 2018
An Empirical Study towards Understanding How Deep Convolutional Nets Recognize Falls Yan Zhang Heiko Neumann 76 5 0 05 Dec 2018
Interpretable Deep Learning under Fire Xinyang Zhang Ningfei Wang Hua Shen S. Ji Xiapu Luo Ting Wang AAML AI4CE 138 174 0 03 Dec 2018
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 319 1,063 0 29 Nov 2018
Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry Kevin McCloskey Ankur Taly Federico Monti M. Brenner Lucy J. Colwell 71 86 0 27 Nov 2018
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks David Bau Jun-Yan Zhu Hendrik Strobelt Bolei Zhou J. Tenenbaum William T. Freeman Antonio Torralba GAN 29 0 0 26 Nov 2018
Representer Point Selection for Explaining Deep Neural Networks Chih-Kuan Yeh Joon Sik Kim Ian En-Hsu Yen Pradeep Ravikumar TDI 106 254 0 23 Nov 2018
On a Sparse Shortcut Topology of Artificial Neural Networks Fenglei Fan Dayang Wang Hengtao Guo Qikui Zhu Pingkun Yan Ge Wang Hengyong Yu 139 22 0 22 Nov 2018
Compensated Integrated Gradients to Reliably Interpret EEG Classification Kazuki Tachikawa Yuji Kawai Jihoon Park Minoru Asada FAtt 27 1 0 21 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
Towards Explainable Deep Learning for Credit Lending: A Case Study C. Modarres Mark Ibrahim Melissa Louie John Paisley FaML 388 20 0 15 Nov 2018
Deep Q learning for fooling neural networks Mandar M. Kulkarni 39 0 0 13 Nov 2018
What evidence does deep learning model use to classify Skin Lesions? Xiaoxiao Li Junyan Wu Eric Z. Chen Hongda Jiang 76 9 0 02 Nov 2018
Technical Note on Transcription Factor Motif Discovery from Importance Scores (TF-MoDISco) version 0.5.6.5 Avanti Shrikumar Katherine Tian vZiga Avsec A. Shcherbina Abhimanyu Banerjee Mahfuza Sharmin Surag Nair A. Kundaje 52 135 0 31 Oct 2018
What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play Shi Feng Jordan L. Boyd-Graber HAI 82 130 0 23 Oct 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
Concise Explanations of Neural Networks using Adversarial Training P. Chalasani Jiefeng Chen Aravind Sadagopan S. Jha Xi Wu AAML FAtt 178 13 0 15 Oct 2018
What made you do this? Understanding black-box decisions with sufficient input subsets Brandon Carter Jonas W. Mueller Siddhartha Jain David K Gifford FAtt 90 78 0 09 Oct 2018