Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges

20 March 2018

Papers citing "Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges"

41 / 41 papers shown

Title
Less is More: The Influence of Pruning on the Explainability of CNNs David Weber F. Merkle Pascal Schöttle Stephan Schlögl Martin Nocker FAtt 115 1 0 17 Feb 2023
Path-Specific Counterfactual Fairness Silvia Chiappa Thomas P. S. Gillam CML FaML 63 337 0 22 Feb 2018
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs W. James Murdoch Peter J. Liu Bin Yu 64 210 0 16 Jan 2018
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text Nicholas Carlini D. Wagner AAML 91 1,077 0 05 Jan 2018
Deep Learning: A Critical Appraisal G. Marcus HAI VLM 117 1,040 0 02 Jan 2018
What do we need to build explainable AI systems for the medical domain? Andreas Holzinger Chris Biemann C. Pattichis D. Kell 66 689 0 28 Dec 2017
Adversarial Phenomenon in the Eyes of Bayesian Deep Learning Ambrish Rawat Martin Wistuba Maria-Irina Nicolae BDL AAML 49 39 0 22 Nov 2017
Towards better understanding of gradient-based attribution methods for Deep Neural Networks Marco Ancona Enea Ceolini Cengiz Öztireli Markus Gross FAtt 62 146 0 16 Nov 2017
AOGNets: Compositional Grammatical Architectures for Deep Learning Xilai Li Xi Song Tianfu Wu 59 26 0 15 Nov 2017
Towards Interpretable R-CNN by Unfolding Latent Structures Tianfu Wu Wei Sun Xilai Li Xi Song Yangqiu Song ObjD 30 20 0 14 Nov 2017
Intriguing Properties of Adversarial Examples E. D. Cubuk Barret Zoph S. Schoenholz Quoc V. Le AAML 64 84 0 08 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 91 684 0 02 Nov 2017
Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight Yen-Chen Lin Ming-Yuan Liu Min Sun Jia-Bin Huang AAML 70 48 0 02 Oct 2017
What Does Explainable AI Really Mean? A New Conceptualization of Perspectives Derek Doran Sarah Schulz Tarek R. Besold XAI 66 438 0 02 Oct 2017
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models Wojciech Samek Thomas Wiegand K. Müller XAI VLM 68 1,188 0 28 Aug 2017
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples Yinpeng Dong Hang Su Jun Zhu Fan Bao AAML 116 129 0 18 Aug 2017
A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop Andreas Holzinger M. Plass K. Holzinger G. Crişan Camelia-M. Pintea Vasile Palade 56 93 0 03 Aug 2017
Interpretable & Explorable Approximations of Black Box Models Himabindu Lakkaraju Ece Kamar R. Caruana J. Leskovec FAtt 65 254 0 04 Jul 2017
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 278 2,257 0 24 Jun 2017
A simple neural network module for relational reasoning Adam Santoro David Raposo David Barrett Mateusz Malinowski Razvan Pascanu Peter W. Battaglia Timothy Lillicrap GNN NAI 162 1,613 0 05 Jun 2017
Causal Effect Inference with Deep Latent-Variable Models Christos Louizos Uri Shalit Joris Mooij David Sontag R. Zemel Max Welling CML BDL 183 741 0 24 May 2017
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car Mariusz Bojarski Philip Yeres A. Choromańska K. Choromanski Bernhard Firner L. Jackel Urs Muller 69 400 0 25 Apr 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 74 1,517 0 11 Apr 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 180 3,865 0 10 Apr 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 169 2,878 0 14 Mar 2017
Improving Interpretability of Deep Neural Networks with Semantic Information Yinpeng Dong Hang Su Jun Zhu Bo Zhang 51 125 0 12 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 376 3,776 0 28 Feb 2017
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis L. Zintgraf Taco S. Cohen T. Adel Max Welling FAtt 132 707 0 15 Feb 2017
Automatic Rule Extraction from Long Short Term Memory Networks W. James Murdoch Arthur Szlam 55 87 0 08 Feb 2017
Understanding Neural Networks through Representation Erasure Jiwei Li Will Monroe Dan Jurafsky AAML MILM 86 564 0 24 Dec 2016
Investigating the influence of noise and distractors on the interpretation of neural networks Pieter-Jan Kindermans Kristof T. Schütt K. Müller Sven Dähne FAtt 65 125 0 22 Nov 2016
Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt 47 64 0 17 Nov 2016
Semantics derived automatically from language corpora contain human-like biases Aylin Caliskan J. Bryson Arvind Narayanan 195 2,661 0 25 Aug 2016
A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks Felix Grün Christian Rupprecht Nassir Navab Federico Tombari SSL FAtt 62 76 0 24 Jun 2016
Auditing Black-box Models for Indirect Influence Philip Adler Casey Falk Sorelle A. Friedler Gabriel Rybeck C. Scheidegger Brandon Smith Suresh Venkatasubramanian TDI MLAU 136 290 0 23 Feb 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.0K 16,931 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 66 3,676 0 08 Feb 2016
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 245 14,893 1 21 Dec 2013
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 293 7,279 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 519 15,861 0 12 Nov 2013
How to Explain Individual Classification Decisions D. Baehrens T. Schroeter Stefan Harmeling M. Kawanabe K. Hansen K. Müller FAtt 126 1,102 0 06 Dec 2009