Interpretation of Neural Networks is Fragile

29 October 2017

Papers citing "Interpretation of Neural Networks is Fragile"

50 / 467 papers shown

Title
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 29 48 0 19 Oct 2020
Evaluating Attribution Methods using White-Box LSTMs Sophie Hao FAtt XAI 8 8 0 16 Oct 2020
FAR: A General Framework for Attributional Robustness Adam Ivankay Ivan Girardi Chiara Marchiori P. Frossard OOD 31 22 0 14 Oct 2020
Learning Propagation Rules for Attribution Map Generation Yiding Yang Jiayan Qiu Mingli Song Dacheng Tao Xinchao Wang FAtt 30 17 0 14 Oct 2020
Neural Gaussian Mirror for Controlled Feature Selection in Neural Networks Xin Xing Yu Gui Chenguang Dai Jun S. Liu AAML 13 4 0 13 Oct 2020
Gradient-based Analysis of NLP Models is Manipulable Junlin Wang Jens Tuyls Eric Wallace Sameer Singh AAML FAtt 21 58 0 12 Oct 2020
Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization Alexander Katzmann O. Taubmann Stephen Ahmad Alexander Muhlberg M. Sühling H. Groß MedIm 11 19 0 09 Oct 2020
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects Amir-Hossein Karimi Gilles Barthe Bernhard Schölkopf Isabel Valera FaML 14 172 0 08 Oct 2020
Information-Theoretic Visual Explanation for Black-Box Classifiers Jihun Yi Eunji Kim Siwon Kim Sungroh Yoon FAtt 20 6 0 23 Sep 2020
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors Yi-Shan Lin Wen-Chuan Lee Z. Berkay Celik XAI 26 93 0 22 Sep 2020
Reconstructing Actions To Explain Deep Reinforcement Learning Xuan Chen Zifan Wang Yucai Fan Bonan Jin Piotr (Peter) Mardziel Carlee Joe-Wong Anupam Datta FAtt 8 2 0 17 Sep 2020
Evolutionary Selective Imitation: Interpretable Agents by Imitation Learning Without a Demonstrator Roy Eliya J. Herrmann 9 2 0 17 Sep 2020
Are Interpretations Fairly Evaluated? A Definition Driven Pipeline for Post-Hoc Interpretability Ninghao Liu Yunsong Meng Xia Hu Tie Wang Bo Long XAI FAtt 23 7 0 16 Sep 2020
How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks Thomas Fel David Vigouroux Rémi Cadène Thomas Serre XAI FAtt 26 31 0 07 Sep 2020
Model extraction from counterfactual explanations Ulrich Aivodji Alexandre Bolot Sébastien Gambs MIACV MLAU 27 51 0 03 Sep 2020
Relevance Attack on Detectors Sizhe Chen Fan He Xiaolin Huang Kun Zhang AAML 24 16 0 16 Aug 2020
Can We Trust Your Explanations? Sanity Checks for Interpreters in Android Malware Analysis Ming Fan Wenying Wei Xiaofei Xie Yang Liu X. Guan Ting Liu FAtt AAML 14 36 0 13 Aug 2020
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability Dylan Slack Sophie Hilgard Sameer Singh Himabindu Lakkaraju FAtt 8 161 0 11 Aug 2020
Fairwashing Explanations with Off-Manifold Detergent Christopher J. Anders Plamen Pasliev Ann-Kathrin Dombrowski K. Müller Pan Kessel FAtt FaML 8 94 0 20 Jul 2020
A simple defense against adversarial attacks on heatmap explanations Laura Rieger Lars Kai Hansen FAtt AAML 25 37 0 13 Jul 2020
Regional Image Perturbation Reduces $L_p$ Norms of Adversarial Examples While Maintaining Model-to-model Transferability Utku Ozbulak Jonathan Peck W. D. Neve Bart Goossens Yvan Saeys Arnout Van Messem AAML 10 2 0 07 Jul 2020
Unifying Model Explainability and Robustness via Machine-Checkable Concepts Vedant Nanda Till Speicher John P. Dickerson Krishna P. Gummadi Muhammad Bilal Zafar AAML 4 4 0 01 Jul 2020
Proper Network Interpretability Helps Adversarial Robustness in Classification Akhilan Boopathy Sijia Liu Gaoyuan Zhang Cynthia Liu Pin-Yu Chen Shiyu Chang Luca Daniel AAML FAtt 19 66 0 26 Jun 2020
Influence Functions in Deep Learning Are Fragile S. Basu Phillip E. Pope S. Feizi TDI 28 219 0 25 Jun 2020
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey Arun Das P. Rad XAI 16 593 0 16 Jun 2020
On Saliency Maps and Adversarial Robustness Puneet Mangla Vedant Singh V. Balasubramanian AAML 10 16 0 14 Jun 2020
Explainable Artificial Intelligence: a Systematic Review Giulia Vilone Luca Longo XAI 20 266 0 29 May 2020
Explaining Neural Networks by Decoding Layer Activations Johannes Schneider Michalis Vlachos AI4CE 14 15 0 27 May 2020
NILE : Natural Language Inference with Faithful Natural Language Explanations Sawan Kumar Partha P. Talukdar XAI LRM 6 159 0 25 May 2020
Multi-Task Learning in Histo-pathology for Widely Generalizable Model Jevgenij Gamper Navid Alemi Koohbanani Nasir M. Rajpoot 14 7 0 09 May 2020
Towards Frequency-Based Explanation for Robust CNN Zifan Wang Yilin Yang Ankit Shrivastava Varun Rawal Zihao Ding AAML FAtt 11 47 0 06 May 2020
Evaluating and Aggregating Feature-based Model Explanations Umang Bhatt Adrian Weller J. M. F. Moura XAI 28 218 0 01 May 2020
Hide-and-Seek: A Template for Explainable AI Thanos Tagaris A. Stafylopatis 6 6 0 30 Apr 2020
Corpus-level and Concept-based Explanations for Interpretable Document Classification Tian Shi Xuchao Zhang Ping Wang Chandan K. Reddy FAtt 18 8 0 24 Apr 2020
Adversarial Attacks and Defenses: An Interpretation Perspective Ninghao Liu Mengnan Du Ruocheng Guo Huan Liu Xia Hu AAML 26 8 0 23 Apr 2020
Live Trojan Attacks on Deep Neural Networks Robby Costales Chengzhi Mao R. Norwitz Bryan Kim Junfeng Yang AAML 6 21 0 22 Apr 2020
Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness? Alon Jacovi Yoav Goldberg XAI 8 564 0 07 Apr 2020
PanNuke Dataset Extension, Insights and Baselines Jevgenij Gamper Navid Alemi Koohbanani Ksenija Benes S. Graham Mostafa Jahanifar S. Khurram A. Azam K. Hewitt Nasir M. Rajpoot 110 174 0 24 Mar 2020
Robust Out-of-distribution Detection for Neural Networks Jiefeng Chen Yixuan Li Xi Wu Yingyu Liang S. Jha OODD 158 84 0 21 Mar 2020
Heat and Blur: An Effective and Fast Defense Against Adversarial Examples Haya Brama Tal Grinshpoun AAML 8 6 0 17 Mar 2020
Model Agnostic Multilevel Explanations K. Ramamurthy B. Vinzamuri Yunfeng Zhang Amit Dhurandhar 13 42 0 12 Mar 2020
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation Raha Moraffah Mansooreh Karami Ruocheng Guo A. Raglin Huan Liu CML ELM XAI 27 212 0 09 Mar 2020
SAM: The Sensitivity of Attribution Methods to Hyperparameters Naman Bansal Chirag Agarwal Anh Nguyen FAtt 16 0 0 04 Mar 2020
A Distributional Framework for Data Valuation Amirata Ghorbani Michael P. Kim James Y. Zou TDI 12 125 0 27 Feb 2020
Neuron Shapley: Discovering the Responsible Neurons Amirata Ghorbani James Y. Zou FAtt TDI 25 108 0 23 Feb 2020
Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example Serena Booth Yilun Zhou Ankit J. Shah J. Shah BDL 6 2 0 19 Feb 2020
Interpreting Interpretations: Organizing Attribution Methods by Criteria Zifan Wang Piotr (Peter) Mardziel Anupam Datta Matt Fredrikson XAI FAtt 6 17 0 19 Feb 2020
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks Joseph D. Janizek Pascal Sturmfels Su-In Lee FAtt 27 143 0 10 Feb 2020
DANCE: Enhancing saliency maps using decoys Y. Lu Wenbo Guo Xinyu Xing William Stafford Noble AAML 32 14 0 03 Feb 2020
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution Nikaash Puri Sukriti Verma Piyush B. Gupta Dhruv Kayastha Shripad Deshmukh Balaji Krishnamurthy Sameer Singh FAtt AAML 11 75 0 23 Dec 2019