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Axiomatic Attribution for Deep Networks

4 March 2017

Ankur Taly

Papers citing "Axiomatic Attribution for Deep Networks"

21 / 2,871 papers shown

Title
Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution Lingyang Chu X. Hu Juhua Hu Lanjun Wang J. Pei 65 100 0 17 Feb 2018
Influence-Directed Explanations for Deep Convolutional Networks Klas Leino S. Sen Anupam Datta Matt Fredrikson Linyi Li TDI FAtt 113 75 0 11 Feb 2018
Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks Patrick Schwab Djordje Miladinovic W. Karlen CML 107 57 0 06 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 263 4,011 0 06 Feb 2018
Evaluating neural network explanation methods using hybrid documents and morphological agreement Nina Pörner Benjamin Roth Hinrich Schütze 66 9 0 19 Jan 2018
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs W. James Murdoch Peter J. Liu Bin Yu 93 210 0 16 Jan 2018
Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation Heyi Li Yunke Tian Klaus Mueller Xin Chen FAtt 77 42 0 22 Dec 2017
Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning G. Erion Hugh Chen Scott M. Lundberg Su-In Lee 59 6 0 02 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 272 1,851 0 30 Nov 2017
Visual Feature Attribution using Wasserstein GANs Christian F. Baumgartner Lisa M. Koch K. Tezcan Jia Xi Ang E. Konukoglu GAN MedIm 103 145 0 24 Nov 2017
Towards better understanding of gradient-based attribution methods for Deep Neural Networks Marco Ancona Enea Ceolini Cengiz Öztireli Markus Gross FAtt 98 147 0 16 Nov 2017
MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis Rushil Anirudh Jayaraman J. Thiagarajan R. Sridhar T. Bremer FAtt AAML 72 12 0 15 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 114 689 0 02 Nov 2017
Interpretation of Neural Networks is Fragile Amirata Ghorbani Abubakar Abid James Zou FAtt AAML 153 875 0 29 Oct 2017
Case Study: Explaining Diabetic Retinopathy Detection Deep CNNs via Integrated Gradients Linyi Li Matt Fredrikson S. Sen Anupam Datta FAtt 22 1 0 27 Sep 2017
Axiomatic Characterization of Data-Driven Influence Measures for Classification Jakub Sliwinski Martin Strobel Yair Zick TDI 74 14 0 07 Aug 2017
MAGIX: Model Agnostic Globally Interpretable Explanations Nikaash Puri Piyush B. Gupta Pratiksha Agarwal Sukriti Verma Balaji Krishnamurthy FAtt 111 41 0 22 Jun 2017
SmoothGrad: removing noise by adding noise D. Smilkov Nikhil Thorat Been Kim F. Viégas Martin Wattenberg FAtt ODL 234 2,237 0 12 Jun 2017
Learning how to explain neural networks: PatternNet and PatternAttribution Pieter-Jan Kindermans Kristof T. Schütt Maximilian Alber K. Müller D. Erhan Been Kim Sven Dähne XAI FAtt 98 340 0 16 May 2017
Detecting Statistical Interactions from Neural Network Weights Michael Tsang Dehua Cheng Yan Liu 99 193 0 14 May 2017
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly Ethan R. Elenberg A. Dimakis Moran Feldman Amin Karbasi 113 89 0 08 Mar 2017