Interpretation of Neural Networks is Fragile

29 October 2017

Papers citing "Interpretation of Neural Networks is Fragile"

50 / 467 papers shown

Title
An Empirical Study on the Relation between Network Interpretability and Adversarial Robustness Adam Noack Isaac Ahern Dejing Dou Boyang Albert Li OOD AAML 16 10 0 07 Dec 2019
Attributional Robustness Training using Input-Gradient Spatial Alignment M. Singh Nupur Kumari Puneet Mangla Abhishek Sinha V. Balasubramanian Balaji Krishnamurthy OOD 29 10 0 29 Nov 2019
Towards a Unified Evaluation of Explanation Methods without Ground Truth Hao Zhang Jiayi Chen Haotian Xue Quanshi Zhang XAI 24 7 0 20 Nov 2019
Enhancing the Extraction of Interpretable Information for Ischemic Stroke Imaging from Deep Neural Networks Erico Tjoa Heng Guo Yuhao Lu Cuntai Guan FAtt 16 5 0 19 Nov 2019
"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations Himabindu Lakkaraju Osbert Bastani 6 249 0 15 Nov 2019
Patch augmentation: Towards efficient decision boundaries for neural networks Marcus D. Bloice P. Roth Andreas Holzinger AAML 8 2 0 08 Nov 2019
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods Dylan Slack Sophie Hilgard Emily Jia Sameer Singh Himabindu Lakkaraju FAtt AAML MLAU 30 804 0 06 Nov 2019
Explanation by Progressive Exaggeration Sumedha Singla Brian Pollack Junxiang Chen Kayhan Batmanghelich FAtt MedIm 6 103 0 01 Nov 2019
CXPlain: Causal Explanations for Model Interpretation under Uncertainty Patrick Schwab W. Karlen FAtt CML 34 205 0 27 Oct 2019
Who's responsible? Jointly quantifying the contribution of the learning algorithm and training data G. Yona Amirata Ghorbani James Zou TDI 21 12 0 09 Oct 2019
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks Mehdi Neshat Zifan Wang Bradley Alexander Fan Yang Zijian Zhang Sirui Ding Markus Wagner Xia Hu FAtt 14 1,049 0 03 Oct 2019
Interrogating the Explanatory Power of Attention in Neural Machine Translation Pooya Moradi Nishant Kambhatla Anoop Sarkar 21 16 0 30 Sep 2019
Saliency Methods for Explaining Adversarial Attacks Jindong Gu Volker Tresp FAtt AAML 8 30 0 22 Aug 2019
A Tour of Convolutional Networks Guided by Linear Interpreters Pablo Navarrete Michelini Hanwen Liu Yunhua Lu Xingqun Jiang HAI FAtt 10 7 0 14 Aug 2019
How to Manipulate CNNs to Make Them Lie: the GradCAM Case T. Viering Ziqi Wang Marco Loog E. Eisemann AAML FAtt 17 28 0 25 Jul 2019
Benchmarking Attribution Methods with Relative Feature Importance Mengjiao Yang Been Kim FAtt XAI 21 140 0 23 Jul 2019
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI Erico Tjoa Cuntai Guan XAI 56 1,413 0 17 Jul 2019
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning Fan Yang Mengnan Du Xia Hu XAI ELM 27 66 0 16 Jul 2019
A study on the Interpretability of Neural Retrieval Models using DeepSHAP Zeon Trevor Fernando Jaspreet Singh Avishek Anand FAtt AAML 16 68 0 15 Jul 2019
Towards Robust, Locally Linear Deep Networks Guang-He Lee David Alvarez-Melis Tommi Jaakkola ODL 19 48 0 07 Jul 2019
Explanations can be manipulated and geometry is to blame Ann-Kathrin Dombrowski Maximilian Alber Christopher J. Anders M. Ackermann K. Müller Pan Kessel AAML FAtt 22 329 0 19 Jun 2019
Is Attention Interpretable? Sofia Serrano Noah A. Smith 19 670 0 09 Jun 2019
ML-LOO: Detecting Adversarial Examples with Feature Attribution Puyudi Yang Jianbo Chen Cho-Jui Hsieh Jane-ling Wang Michael I. Jordan AAML 22 101 0 08 Jun 2019
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series Jeeheh Oh Jiaxuan Wang Shengpu Tang Michael Sjoding Jenna Wiens OOD 11 12 0 07 Jun 2019
Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness Walt Woods Jack H Chen C. Teuscher AAML 18 46 0 07 Jun 2019
XRAI: Better Attributions Through Regions A. Kapishnikov Tolga Bolukbasi Fernanda Viégas Michael Terry FAtt XAI 20 212 0 06 Jun 2019
Boosting Operational DNN Testing Efficiency through Conditioning Zenan Li Xiaoxing Ma Chang Xu Chun Cao Jingwei Xu Jian Lu 11 104 0 06 Jun 2019
Evaluating Explanation Methods for Deep Learning in Security Alexander Warnecke Dan Arp Christian Wressnegger Konrad Rieck XAI AAML FAtt 6 93 0 05 Jun 2019
Adversarial Robustness as a Prior for Learned Representations Logan Engstrom Andrew Ilyas Shibani Santurkar Dimitris Tsipras Brandon Tran A. Madry OOD AAML 24 63 0 03 Jun 2019
Certifiably Robust Interpretation in Deep Learning Alexander Levine Sahil Singla S. Feizi FAtt AAML 23 63 0 28 May 2019
Analyzing the Interpretability Robustness of Self-Explaining Models Haizhong Zheng Earlence Fernandes A. Prakash AAML LRM 21 7 0 27 May 2019
Robust Attribution Regularization Jiefeng Chen Xi Wu Vaibhav Rastogi Yingyu Liang S. Jha OOD 9 83 0 23 May 2019
What Do Adversarially Robust Models Look At? Takahiro Itazuri Yoshihiro Fukuhara Hirokatsu Kataoka Shigeo Morishima 19 5 0 19 May 2019
Misleading Failures of Partial-input Baselines Shi Feng Eric Wallace Jordan L. Boyd-Graber 25 0 0 14 May 2019
Interpreting Adversarial Examples with Attributes Sadaf Gulshad J. H. Metzen A. Smeulders Zeynep Akata FAtt AAML 25 6 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 11 36 0 16 Apr 2019
Data Shapley: Equitable Valuation of Data for Machine Learning Amirata Ghorbani James Zou TDI FedML 42 752 0 05 Apr 2019
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation Marco Ancona Cengiz Öztireli Markus Gross FAtt TDI 22 223 0 26 Mar 2019
Interpreting Neural Networks Using Flip Points Roozbeh Yousefzadeh D. O’Leary AAML FAtt 22 17 0 21 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 12 17 0 14 Mar 2019
Aggregating explanation methods for stable and robust explainability Laura Rieger Lars Kai Hansen AAML FAtt 37 11 0 01 Mar 2019
Functional Transparency for Structured Data: a Game-Theoretic Approach Guang-He Lee Wengong Jin David Alvarez-Melis Tommi Jaakkola 24 19 0 26 Feb 2019
Seven Myths in Machine Learning Research Oscar Chang Hod Lipson 8 0 0 18 Feb 2019
Regularizing Black-box Models for Improved Interpretability Gregory Plumb Maruan Al-Shedivat Ángel Alexander Cabrera Adam Perer Eric P. Xing Ameet Talwalkar AAML 24 79 0 18 Feb 2019
Towards Automatic Concept-based Explanations Amirata Ghorbani James Wexler James Zou Been Kim FAtt LRM 38 19 0 07 Feb 2019
Fooling Neural Network Interpretations via Adversarial Model Manipulation Juyeon Heo Sunghwan Joo Taesup Moon AAML FAtt 16 201 0 06 Feb 2019
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation Sahil Singla Eric Wallace Shi Feng S. Feizi FAtt 18 59 0 01 Feb 2019
Interpreting Deep Neural Networks Through Variable Importance J. Ish-Horowicz Dana Udwin Seth Flaxman Sarah Filippi Lorin Crawford FAtt 14 13 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 39 448 0 27 Jan 2019
Fooling Network Interpretation in Image Classification Akshayvarun Subramanya Vipin Pillai Hamed Pirsiavash AAML FAtt 4 7 0 06 Dec 2018