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Axiomatic Attribution for Deep Networks

4 March 2017

Ankur Taly

Papers citing "Axiomatic Attribution for Deep Networks"

50 / 2,871 papers shown

Title
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values Julius Adebayo Justin Gilmer Ian Goodfellow Been Kim FAtt AAML 82 129 0 08 Oct 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 211 1,973 0 08 Oct 2018
On the Art and Science of Machine Learning Explanations Patrick Hall FAtt XAI 92 30 0 05 Oct 2018
Interpreting Layered Neural Networks via Hierarchical Modular Representation C. Watanabe 84 19 0 03 Oct 2018
Training Machine Learning Models by Regularizing their Explanations A. Ross FaML 63 0 0 29 Sep 2018
Stakeholders in Explainable AI Alun D. Preece Daniel Harborne Dave Braines Richard J. Tomsett Supriyo Chakraborty 55 157 0 29 Sep 2018
Rethinking Self-driving: Multi-task Knowledge for Better Generalization and Accident Explanation Ability Zhihao Li Toshiyuki Motoyoshi Kazuma Sasaki T. Ogata S. Sugano LRM 77 39 0 28 Sep 2018
Response Characterization for Auditing Cell Dynamics in Long Short-term Memory Networks Ramin M. Hasani Alexander Amini Mathias Lechner Felix Naser Radu Grosu Daniela Rus 57 25 0 11 Sep 2018
Interpreting Neural Networks With Nearest Neighbors Eric Wallace Shi Feng Jordan L. Boyd-Graber AAML FAtt MILM 140 54 0 08 Sep 2018
DeepPINK: reproducible feature selection in deep neural networks Yang Young Lu Yingying Fan Jinchi Lv William Stafford Noble FAtt 178 125 0 04 Sep 2018
Dissecting Contextual Word Embeddings: Architecture and Representation Matthew E. Peters Mark Neumann Luke Zettlemoyer Wen-tau Yih 113 434 0 27 Aug 2018
Deep Learning: Computational Aspects Nicholas G. Polson Vadim Sokolov PINN BDL AI4CE 58 14 0 26 Aug 2018
Shedding Light on Black Box Machine Learning Algorithms: Development of an Axiomatic Framework to Assess the Quality of Methods that Explain Individual Predictions Milo Honegger 52 35 0 15 Aug 2018
iNNvestigate neural networks! Maximilian Alber Sebastian Lapuschkin P. Seegerer Miriam Hagele Kristof T. Schütt G. Montavon Wojciech Samek K. Müller Sven Dähne Pieter-Jan Kindermans 79 349 0 13 Aug 2018
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data Jianbo Chen Le Song Martin J. Wainwright Michael I. Jordan FAtt TDI 117 217 0 08 Aug 2018
Enabling Trust in Deep Learning Models: A Digital Forensics Case Study Aditya K Slawomir Grzonkowski NhienAn Lekhac 44 27 0 03 Aug 2018
Efficient Purely Convolutional Text Encoding Szymon Malik A. Lancucki J. Chorowski 3DV 36 1 0 03 Aug 2018
Symbolic Execution for Deep Neural Networks D. Gopinath Kaiyuan Wang Mengshi Zhang C. Păsăreanu S. Khurshid AAML 81 54 0 27 Jul 2018
Computationally Efficient Measures of Internal Neuron Importance Avanti Shrikumar Jocelin Su A. Kundaje FAtt 61 30 0 26 Jul 2018
Knockoffs for the mass: new feature importance statistics with false discovery guarantees Jaime Roquero Gimenez Amirata Ghorbani James Zou CML 88 55 0 17 Jul 2018
Model Reconstruction from Model Explanations S. Milli Ludwig Schmidt Anca Dragan Moritz Hardt FAtt 66 179 0 13 Jul 2018
Direct Uncertainty Prediction for Medical Second Opinions M. Raghu Katy Blumer Rory Sayres Ziad Obermeyer Robert D. Kleinberg S. Mullainathan Jon M. Kleinberg OOD UD 135 137 0 04 Jul 2018
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices Till Speicher Hoda Heidari Nina Grgic-Hlaca Krishna P. Gummadi Adish Singla Adrian Weller Muhammad Bilal Zafar FaML 106 265 0 02 Jul 2018
A Benchmark for Interpretability Methods in Deep Neural Networks Sara Hooker D. Erhan Pieter-Jan Kindermans Been Kim FAtt UQCV 134 684 0 28 Jun 2018
This Looks Like That: Deep Learning for Interpretable Image Recognition Chaofan Chen Oscar Li Chaofan Tao A. Barnett Jonathan Su Cynthia Rudin 330 1,193 0 27 Jun 2018
xGEMs: Generating Examplars to Explain Black-Box Models Shalmali Joshi Oluwasanmi Koyejo Been Kim Joydeep Ghosh MLAU 75 40 0 22 Jun 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 121 530 0 21 Jun 2018
RUDDER: Return Decomposition for Delayed Rewards Jose A. Arjona-Medina Michael Gillhofer Michael Widrich Thomas Unterthiner Johannes Brandstetter Sepp Hochreiter 130 222 0 20 Jun 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 140 948 0 20 Jun 2018
Contrastive Explanations with Local Foil Trees J. V. D. Waa M. Robeer J. Diggelen Matthieu J. S. Brinkhuis Mark Antonius Neerincx FAtt 79 82 0 19 Jun 2018
Maximally Invariant Data Perturbation as Explanation Satoshi Hara Kouichi Ikeno Tasuku Soma Takanori Maehara AAML 72 8 0 19 Jun 2018
Detecting and interpreting myocardial infarction using fully convolutional neural networks Nils Strodthoff C. Strodthoff 100 154 0 18 Jun 2018
Hierarchical interpretations for neural network predictions Chandan Singh W. James Murdoch Bin Yu 84 146 0 14 Jun 2018
A Note about: Local Explanation Methods for Deep Neural Networks lack Sensitivity to Parameter Values Mukund Sundararajan Ankur Taly FAtt 46 21 0 11 Jun 2018
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty Hao Zhou Yunyang Xiong Vikas Singh UQCV BDL 85 4 0 10 Jun 2018
Explainable Neural Networks based on Additive Index Models J. Vaughan Agus Sudjianto Erind Brahimi Jie Chen V. Nair 79 106 0 05 Jun 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 129 1,873 0 31 May 2018
How Important Is a Neuron? Kedar Dhamdhere Mukund Sundararajan Qiqi Yan FAtt GNN 77 131 0 30 May 2018
Semantic Network Interpretation Pei Guo Ryan Farrell MILM FAtt 34 0 0 23 May 2018
Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models Jacob R. Kauffmann K. Müller G. Montavon DRL 77 98 0 16 May 2018
Did the Model Understand the Question? Pramod Kaushik Mudrakarta Ankur Taly Mukund Sundararajan Kedar Dhamdhere ELM OOD FAtt 85 200 0 14 May 2018
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach S. Garg Irina Rish Guillermo Cecchi Palash Goyal Sarik Ghazarian Shuyang Gao Greg Ver Steeg Aram Galstyan 70 0 0 26 Apr 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 108 322 0 20 Apr 2018
Understanding Regularization to Visualize Convolutional Neural Networks Maximilian Baust Florian Ludwig Christian Rupprecht Matthias Kohl S. Braunewell FAtt 54 4 0 20 Apr 2018
Egocentric 6-DoF Tracking of Small Handheld Objects Rohit Pandey Pavel Pidlypenskyi Shuoran Yang Christine Kaeser-Chen 25 4 0 16 Apr 2018
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features Minh-Nghia Nguyen Ngo Anh Vien 59 35 0 13 Apr 2018
Generative Visual Rationales J. Seah Jennifer S. N. Tang Andy Kitchen Jonathan Seah MedIm 34 1 0 04 Apr 2018
Towards Explanation of DNN-based Prediction with Guided Feature Inversion Mengnan Du Ninghao Liu Qingquan Song Helen Zhou FAtt 106 127 0 19 Mar 2018
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation Jianbo Chen Le Song Martin J. Wainwright Michael I. Jordan MLT FAtt 186 576 0 21 Feb 2018
Finding Influential Training Samples for Gradient Boosted Decision Trees B. Sharchilev Yury Ustinovsky P. Serdyukov Maarten de Rijke TDI 74 57 0 19 Feb 2018