Explaining high-dimensional text classifiers

22 November 2023

Papers citing "Explaining high-dimensional text classifiers"

20 / 20 papers shown

Title
A Survey of Human-in-the-loop for Machine Learning Xingjiao Wu Luwei Xiao Yixuan Sun Junhang Zhang Tianlong Ma Liangbo He SyDa 101 525 0 02 Aug 2021
The Dimpled Manifold Model of Adversarial Examples in Machine Learning A. Shamir Odelia Melamed Oriel BenShmuel AAML 54 50 0 18 Jun 2021
Fairwashing Explanations with Off-Manifold Detergent Christopher J. Anders Plamen Pasliev Ann-Kathrin Dombrowski K. Müller Pan Kessel FAtt FaML 50 97 0 20 Jul 2020
You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion R. Schuster Congzheng Song Eran Tromer Vitaly Shmatikov SILM AAML 100 156 0 05 Jul 2020
Shapley explainability on the data manifold Christopher Frye Damien de Mijolla T. Begley Laurence Cowton Megan Stanley Ilya Feige FAtt TDI 41 99 0 01 Jun 2020
Towards Explainable NLP: A Generative Explanation Framework for Text Classification Hui Liu Qingyu Yin William Yang Wang 89 148 0 01 Nov 2018
Interpreting Neural Networks With Nearest Neighbors Eric Wallace Shi Feng Jordan L. Boyd-Graber AAML FAtt MILM 103 54 0 08 Sep 2018
Explaining Image Classifiers by Counterfactual Generation C. Chang Elliot Creager Anna Goldenberg David Duvenaud VLM 73 264 0 20 Jul 2018
Pathologies of Neural Models Make Interpretations Difficult Shi Feng Eric Wallace Alvin Grissom II Mohit Iyyer Pedro Rodriguez Jordan L. Boyd-Graber AAML FAtt 76 321 0 20 Apr 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 224 3,186 0 01 Feb 2018
Evasion Attacks against Machine Learning at Test Time Battista Biggio Igino Corona Davide Maiorca B. Nelson Nedim Srndic Pavel Laskov Giorgio Giacinto Fabio Roli AAML 157 2,151 0 21 Aug 2017
A causal framework for explaining the predictions of black-box sequence-to-sequence models David Alvarez-Melis Tommi Jaakkola CML 348 204 0 06 Jul 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,906 0 22 May 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 121 1,857 0 20 May 2017
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations A. Ross M. C. Hughes Finale Doshi-Velez FAtt 120 589 0 10 Mar 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 188 5,989 0 04 Mar 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,976 0 16 Feb 2016
Practical Black-Box Attacks against Machine Learning Nicolas Papernot Patrick McDaniel Ian Goodfellow S. Jha Z. Berkay Celik A. Swami MLAU AAML 75 3,677 0 08 Feb 2016
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 270 14,927 1 21 Dec 2013
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 312 7,295 0 20 Dec 2013