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SAM: The Sensitivity of Attribution Methods to Hyperparameters

4 March 2020

Papers citing "SAM: The Sensitivity of Attribution Methods to Hyperparameters"

23 / 23 papers shown

Title
Towards Explainable Artificial Intelligence Wojciech Samek K. Müller XAI 75 442 0 26 Sep 2019
Adversarial Robustness as a Prior for Learned Representations Logan Engstrom Andrew Ilyas Shibani Santurkar Dimitris Tsipras Brandon Tran Aleksander Madry OOD AAML 61 63 0 03 Jun 2019
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening Nan Wu Jason Phang Jungkyu Park Yiqiu Shen Zhe Huang ... S. G. Kim Laura Heacock Linda Moy Kyunghyun Cho Krzysztof J. Geras MedIm 47 499 0 20 Mar 2019
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 141 1,970 0 08 Oct 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 79 528 0 21 Jun 2018
RISE: Randomized Input Sampling for Explanation of Black-box Models Vitali Petsiuk Abir Das Kate Saenko FAtt 181 1,171 0 19 Jun 2018
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning Pranav Rajpurkar Jeremy Irvin Kaylie Zhu Brandon Yang Hershel Mehta ... Aarti Bagul C. Langlotz K. Shpanskaya M. Lungren A. Ng LM&MA 83 2,709 0 14 Nov 2017
The (Un)reliability of saliency methods Pieter-Jan Kindermans Sara Hooker Julius Adebayo Maximilian Alber Kristof T. Schütt Sven Dähne D. Erhan Been Kim FAtt XAI 101 687 0 02 Nov 2017
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 291 2,266 0 24 Jun 2017
Explaining Recurrent Neural Network Predictions in Sentiment Analysis L. Arras G. Montavon K. Müller Wojciech Samek FAtt 61 354 0 22 Jun 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 310 12,117 0 19 Jun 2017
Real Time Image Saliency for Black Box Classifiers P. Dabkowski Y. Gal 67 591 0 22 May 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 76 1,525 0 11 Apr 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 188 6,015 0 04 Mar 2017
Grad-CAM: Why did you say that? Ramprasaath R. Selvaraju Abhishek Das Ramakrishna Vedantam Michael Cogswell Devi Parikh Dhruv Batra FAtt 70 475 0 22 Nov 2016
European Union regulations on algorithmic decision-making and a "right to explanation" B. Goodman Seth Flaxman FaML AILaw 67 1,902 0 28 Jun 2016
Not Just a Black Box: Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Shcherbina A. Kundaje FAtt 82 789 0 05 May 2016
Learning Deep Features for Discriminative Localization Bolei Zhou A. Khosla Àgata Lapedriza A. Oliva Antonio Torralba SSL SSeg FAtt 250 9,326 0 14 Dec 2015
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 248 4,681 0 21 Dec 2014
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images Anh Totti Nguyen J. Yosinski Jeff Clune AAML 169 3,275 0 05 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 277 14,961 1 21 Dec 2013
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 312 7,308 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 595 15,893 0 12 Nov 2013