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Interpretation of Neural Networks is Fragile

AAAI Conference on Artificial Intelligence (AAAI), 2017

29 October 2017

Papers citing "Interpretation of Neural Networks is Fragile"

39 / 489 papers shown

Title
Evaluating Explanation Methods for Deep Learning in SecurityEuropean Symposium on Security and Privacy (EuroS&P), 2019 Alexander Warnecke Dan Arp Christian Wressnegger Konrad Rieck XAI AAML FAtt 239 117 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 216 63 0 03 Jun 2019
Certifiably Robust Interpretation in Deep Learning Alexander Levine Sahil Singla Soheil Feizi FAtt AAML 315 65 0 28 May 2019
Analyzing the Interpretability Robustness of Self-Explaining Models Haizhong Zheng Earlence Fernandes A. Prakash AAML LRM 168 7 0 27 May 2019
Robust Attribution RegularizationNeural Information Processing Systems (NeurIPS), 2019 Jiefeng Chen Xi Wu Vaibhav Rastogi Yingyu Liang S. Jha OOD 172 88 0 23 May 2019
What Do Adversarially Robust Models Look At? Takahiro Itazuri Yoshihiro Fukuhara Hirokatsu Kataoka Shigeo Morishima 101 5 0 19 May 2019
Misleading Failures of Partial-input Baselines Shi Feng Eric Wallace Jordan L. Boyd-Graber 211 0 0 14 May 2019
Interpreting Adversarial Examples with Attributes Sadaf Gulshad J. H. Metzen A. Smeulders Zeynep Akata FAtt AAML 169 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 145 38 0 16 Apr 2019
Data Shapley: Equitable Valuation of Data for Machine Learning Amirata Ghorbani James Zou TDI FedML 496 953 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 280 253 0 26 Mar 2019
Interpreting Neural Networks Using Flip Points Roozbeh Yousefzadeh D. O’Leary AAML FAtt 117 18 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 149 17 0 14 Mar 2019
Aggregating explanation methods for stable and robust explainability Laura Rieger Lars Kai Hansen AAML FAtt 284 15 0 01 Mar 2019
Functional Transparency for Structured Data: a Game-Theoretic ApproachInternational Conference on Machine Learning (ICML), 2019 Guang-He Lee Wengong Jin David Alvarez-Melis Tommi Jaakkola 148 19 0 26 Feb 2019
Seven Myths in Machine Learning Research Oscar Chang Hod Lipson 53 0 0 18 Feb 2019
Regularizing Black-box Models for Improved Interpretability Gregory Plumb Maruan Al-Shedivat Ángel Alexander Cabrera Adam Perer Eric Xing Ameet Talwalkar AAML 508 83 0 18 Feb 2019
Towards Automatic Concept-based Explanations Amirata Ghorbani James Wexler James Zou Been Kim FAtt LRM 321 20 0 07 Feb 2019
Fooling Neural Network Interpretations via Adversarial Model Manipulation Juyeon Heo Sunghwan Joo Taesup Moon AAML FAtt 373 223 0 06 Feb 2019
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning InterpretationInternational Conference on Machine Learning (ICML), 2019 Sahil Singla Eric Wallace Shi Feng Soheil Feizi FAtt 207 60 0 01 Feb 2019
Interpreting Deep Neural Networks Through Variable Importance J. Ish-Horowicz Dana Udwin Seth Flaxman Sarah Filippi Lorin Crawford FAtt 133 16 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 327 523 0 27 Jan 2019
Fooling Network Interpretation in Image Classification Akshayvarun Subramanya Vipin Pillai Hamed Pirsiavash AAML FAtt 201 7 0 06 Dec 2018
Compensated Integrated Gradients to Reliably Interpret EEG Classification Kazuki Tachikawa Yuji Kawai Jihoon Park Minoru Asada FAtt 114 1 0 21 Nov 2018
What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play Shi Feng Jordan L. Boyd-Graber HAI 260 139 0 23 Oct 2018
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values Julius Adebayo Justin Gilmer Ian Goodfellow Been Kim FAtt AAML 189 131 0 08 Oct 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 1.1K 2,202 0 08 Oct 2018
Training Machine Learning Models by Regularizing their Explanations A. Ross FaML 117 0 0 29 Sep 2018
Interpreting Neural Networks With Nearest Neighbors Eric Wallace Shi Feng Jordan L. Boyd-Graber AAML FAtt MILM 295 56 0 08 Sep 2018
DeepPINK: reproducible feature selection in deep neural networks Yang Young Lu Yingying Fan Jinchi Lv William Stafford Noble FAtt 356 137 0 04 Sep 2018
Knockoffs for the mass: new feature importance statistics with false discovery guaranteesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2018 Jaime Roquero Gimenez Amirata Ghorbani James Zou CML 153 57 0 17 Jul 2018
Adversarial Reprogramming of Neural NetworksInternational Conference on Learning Representations (ICLR), 2018 Gamaleldin F. Elsayed Ian Goodfellow Jascha Narain Sohl-Dickstein OOD AAML 174 196 0 28 Jun 2018
Gradient Similarity: An Explainable Approach to Detect Adversarial Attacks against Deep Learning J. Dhaliwal S. Shintre AAML 83 16 0 27 Jun 2018
Interpreting Deep Learning: The Machine Learning Rorschach Test? Adam S. Charles AAML HAI AI4CE 194 9 0 01 Jun 2018
Pathologies of Neural Models Make Interpretations DifficultConference on Empirical Methods in Natural Language Processing (EMNLP), 2018 Shi Feng Eric Wallace Alvin Grissom II Mohit Iyyer Pedro Rodriguez Jordan L. Boyd-Graber AAML FAtt 300 330 0 20 Apr 2018
Towards Explanation of DNN-based Prediction with Guided Feature Inversion Mengnan Du Ninghao Liu Qingquan Song Helen Zhou FAtt 361 131 0 19 Mar 2018
Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution Lingyang Chu X. Hu Juhua Hu Lanjun Wang Jian Pei 146 104 0 17 Feb 2018
Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis Takashi Matsubara T. Tashiro K. Uehara MedIm 101 45 0 18 Dec 2017
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 492 2,095 0 30 Nov 2017