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Axiomatic Attribution for Deep Networks

4 March 2017

Ankur Taly

Papers citing "Axiomatic Attribution for Deep Networks"

50 / 2,872 papers shown

Title
ML-LOO: Detecting Adversarial Examples with Feature Attribution Puyudi Yang Jianbo Chen Cho-Jui Hsieh Jane-ling Wang Michael I. Jordan AAML 98 101 0 08 Jun 2019
Unsupervised Representation Learning of DNA Sequences Vishal Agarwal N. Reddy Ashish Anand BDL SSL DRL 35 11 0 07 Jun 2019
XRAI: Better Attributions Through Regions A. Kapishnikov Tolga Bolukbasi Fernanda Viégas Michael Terry FAtt XAI 81 213 0 06 Jun 2019
Evaluating Explanation Methods for Deep Learning in Security Alexander Warnecke Dan Arp Christian Wressnegger Konrad Rieck XAI AAML FAtt 71 94 0 05 Jun 2019
c-Eval: A Unified Metric to Evaluate Feature-based Explanations via Perturbation Minh Nhat Vu Truc D. T. Nguyen Nhathai Phan Ralucca Gera My T. Thai AAML FAtt 79 22 0 05 Jun 2019
Interpretable and Differentially Private Predictions Frederik Harder Matthias Bauer Mijung Park FAtt 74 53 0 05 Jun 2019
Adversarial Robustness as a Prior for Learned Representations Logan Engstrom Andrew Ilyas Shibani Santurkar Dimitris Tsipras Brandon Tran Aleksander Madry OOD AAML 104 63 0 03 Jun 2019
Explainability Techniques for Graph Convolutional Networks Federico Baldassarre Hossein Azizpour GNN FAtt 178 272 0 31 May 2019
Certifiably Robust Interpretation in Deep Learning Alexander Levine Sahil Singla Soheil Feizi FAtt AAML 104 65 0 28 May 2019
A Cross-Domain Transferable Neural Coherence Model Peng Xu H. Saghir Jin Sung Kang Teng Long A. Bose Yanshuai Cao Jackie C.K. Cheung 119 46 0 28 May 2019
Deep Learning for Bug-Localization in Student Programs Rahul Gupta Aditya Kanade S. Shevade 33 4 0 28 May 2019
EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction Diane Bouchacourt Ludovic Denoyer FAtt 74 21 0 28 May 2019
Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling Emmanuel Noutahi Dominique Beaini Julien Horwood Sébastien Giguère Prudencio Tossou AI4CE 188 34 0 28 May 2019
A Simple Saliency Method That Passes the Sanity Checks Arushi Gupta Sanjeev Arora AAML XAI FAtt 30 11 0 27 May 2019
Structure Learning for Neural Module Networks Vardaan Pahuja Jie Fu Sarath Chandar C. Pal 69 7 0 27 May 2019
Analyzing the Interpretability Robustness of Self-Explaining Models Haizhong Zheng Earlence Fernandes A. Prakash AAML LRM 76 7 0 27 May 2019
Rearchitecting Classification Frameworks For Increased Robustness Varun Chandrasekaran Brian Tang Nicolas Papernot Kassem Fawaz S. Jha Xi Wu AAML OOD 100 8 0 26 May 2019
Robust Attribution Regularization Jiefeng Chen Xi Wu Vaibhav Rastogi Yingyu Liang S. Jha OOD 59 83 0 23 May 2019
Interpreting a Recurrent Neural Network's Predictions of ICU Mortality Risk L. Ho M. Aczon D. Ledbetter R. Wetzel 46 3 0 23 May 2019
Computationally Efficient Feature Significance and Importance for Machine Learning Models Enguerrand Horel K. Giesecke FAtt 55 9 0 23 May 2019
Interpreting Adversarially Trained Convolutional Neural Networks Tianyuan Zhang Zhanxing Zhu AAML GAN FAtt 130 161 0 23 May 2019
What Do Adversarially Robust Models Look At? Takahiro Itazuri Yoshihiro Fukuhara Hirokatsu Kataoka Shigeo Morishima 32 5 0 19 May 2019
How Case Based Reasoning Explained Neural Networks: An XAI Survey of Post-Hoc Explanation-by-Example in ANN-CBR Twins Mark T. Keane Eoin M. Kenny 130 82 0 17 May 2019
Consensus-based Interpretable Deep Neural Networks with Application to Mortality Prediction Shaeke Salman S. N. Payrovnaziri Xiuwen Liu Pablo Rengifo-Moreno Zhe He 26 0 0 14 May 2019
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use S. Tonekaboni Shalmali Joshi M. Mccradden Anna Goldenberg 103 403 0 13 May 2019
Explainable AI for Trees: From Local Explanations to Global Understanding Scott M. Lundberg G. Erion Hugh Chen A. DeGrave J. Prutkin B. Nair R. Katz J. Himmelfarb N. Bansal Su-In Lee FAtt 127 292 0 11 May 2019
On the Connection Between Adversarial Robustness and Saliency Map Interpretability Christian Etmann Sebastian Lunz Peter Maass Carola-Bibiane Schönlieb AAML FAtt 71 162 0 10 May 2019
Unsupervised Detection of Distinctive Regions on 3D Shapes Xianzhi Li Lequan Yu Chi-Wing Fu Daniel Cohen-Or Pheng-Ann Heng 3DPC 95 20 0 05 May 2019
Temporal Graph Convolutional Networks for Automatic Seizure Detection Ian Covert B. Krishnan I. Najm Jiening Zhan Matthew Shore J. Hixson M. Po 60 71 0 03 May 2019
Visualizing Deep Networks by Optimizing with Integrated Gradients Zhongang Qi Saeed Khorram Fuxin Li FAtt 85 126 0 02 May 2019
Full-Gradient Representation for Neural Network Visualization Suraj Srinivas François Fleuret MILM FAtt 126 278 0 02 May 2019
"Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations Hui Fen Tan Kuangyan Song Yiming Sun Yujia Zhang Madeilene Udell FAtt 124 19 0 29 Apr 2019
Property Inference for Deep Neural Networks D. Gopinath Hayes Converse C. Păsăreanu Ankur Taly 74 8 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
Explaining a prediction in some nonlinear models Cosimo Izzo FAtt 52 0 0 21 Apr 2019
Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation Fabian Eitel Emily Soehler J. Bellmann-Strobl A. Brandt K. Ruprecht ... M. Weygandt J. Haynes M. Scheel Friedemann Paul K. Ritter 82 134 0 18 Apr 2019
Generating Minimal Adversarial Perturbations with Integrated Adaptive Gradients Yatie Xiao Chi-Man Pun AAML GAN TTA 21 0 0 12 Apr 2019
Software and application patterns for explanation methods Maximilian Alber 80 11 0 09 Apr 2019
Diabetes Mellitus Forecasting Using Population Health Data in Ontario, Canada Mathieu Ravaut Hamed Sadeghi Kin Kwan Leung Anthony L. Caterini L. Rosella OOD 34 6 0 08 Apr 2019
Visualization of Convolutional Neural Networks for Monocular Depth Estimation Junjie Hu Yan Zhang Takayuki Okatani MDE 130 83 0 06 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 102 218 0 04 Apr 2019
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents Christian Rupprecht Cyril Ibrahim C. Pal 96 32 0 02 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 91 99 0 01 Apr 2019
Interpreting Black Box Models via Hypothesis Testing Collin Burns Jesse Thomason Wesley Tansey FAtt 80 9 0 29 Mar 2019
Bridging Adversarial Robustness and Gradient Interpretability Beomsu Kim Junghoon Seo Taegyun Jeon AAML 86 40 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 123 231 0 26 Mar 2019
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet Wieland Brendel Matthias Bethge SSL FAtt 213 562 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
Attribution-driven Causal Analysis for Detection of Adversarial Examples Susmit Jha Sunny Raj S. Fernandes Sumit Kumar Jha S. Jha Gunjan Verma B. Jalaeian A. Swami AAML 75 17 0 14 Mar 2019