Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

30 November 2017

Justin Gilmer

Papers citing "Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)"

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Title
Disentangling Influence: Using Disentangled Representations to Audit Model Predictions Charles Marx R. L. Phillips Sorelle A. Friedler C. Scheidegger Suresh Venkatasubramanian TDI CML MLAU 11 27 0 20 Jun 2019
Incorporating Priors with Feature Attribution on Text Classification Frederick Liu Besim Avci FAtt FaML 28 120 0 19 Jun 2019
Explanations can be manipulated and geometry is to blame Ann-Kathrin Dombrowski Maximilian Alber Christopher J. Anders M. Ackermann K. Müller Pan Kessel AAML FAtt 20 328 0 19 Jun 2019
Image Counterfactual Sensitivity Analysis for Detecting Unintended Bias Emily L. Denton B. Hutchinson Margaret Mitchell Timnit Gebru Andrew Zaldivar CVBM 11 129 0 14 Jun 2019
Global and Local Interpretability for Cardiac MRI Classification J. Clough Ilkay Oksuz Esther Puyol-Antón B. Ruijsink A. King J. Schnabel 8 59 0 14 Jun 2019
Extracting Interpretable Concept-Based Decision Trees from CNNs Conner Chyung Michael Tsang Yan Liu FAtt 11 8 0 11 Jun 2019
Visualizing and Measuring the Geometry of BERT Andy Coenen Emily Reif Ann Yuan Been Kim Adam Pearce F. Viégas Martin Wattenberg MILM 21 415 0 06 Jun 2019
Concept Tree: High-Level Representation of Variables for More Interpretable Surrogate Decision Trees X. Renard Nicolas Woloszko Jonathan Aigrain Marcin Detyniecki 8 11 0 04 Jun 2019
Kandinsky Patterns Heimo Mueller Andreas Holzinger 11 31 0 03 Jun 2019
Leveraging Latent Features for Local Explanations Ronny Luss Pin-Yu Chen Amit Dhurandhar P. Sattigeri Yunfeng Zhang Karthikeyan Shanmugam Chun-Chen Tu FAtt 41 37 0 29 May 2019
Clustering and Recognition of Spatiotemporal Features through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks Kun Su Eli Shlizerman 28 13 0 29 May 2019
EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction Diane Bouchacourt Ludovic Denoyer FAtt 19 21 0 28 May 2019
Overlearning Reveals Sensitive Attributes Congzheng Song Vitaly Shmatikov 11 148 0 28 May 2019
Why do These Match? Explaining the Behavior of Image Similarity Models Bryan A. Plummer Mariya I. Vasileva Vitali Petsiuk Kate Saenko David A. Forsyth XAI FAtt 21 18 0 26 May 2019
NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation Subhashis Hazarika Haoyu Li Ko-Chih Wang Han-Wei Shen Ching-Shan Chou 23 20 0 19 Apr 2019
Deep Learning Under the Microscope: Improving the Interpretability of Medical Imaging Neural Networks Magdalini Paschali Muhammad Ferjad Naeem Walter Simson K. Steiger M. Mollenhauer Nassir Navab 11 19 0 05 Apr 2019
Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations Fred Hohman Haekyu Park Caleb Robinson Duen Horng Chau FAtt 3DH HAI 17 213 0 04 Apr 2019
Explaining Reinforcement Learning to Mere Mortals: An Empirical Study Andrew Anderson Jonathan Dodge Amrita Sadarangani Zoe Juozapaitis E. Newman Jed Irvine Souti Chattopadhyay Alan Fern Margaret Burnett FAtt 20 65 0 22 Mar 2019
Aggregating explanation methods for stable and robust explainability Laura Rieger Lars Kai Hansen AAML FAtt 27 11 0 01 Mar 2019
Regularizing Black-box Models for Improved Interpretability Gregory Plumb Maruan Al-Shedivat Ángel Alexander Cabrera Adam Perer Eric P. Xing Ameet Talwalkar AAML 14 79 0 18 Feb 2019
ProtoAttend: Attention-Based Prototypical Learning Sercan Ö. Arik Tomas Pfister 19 19 0 17 Feb 2019
Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making Carrie J. Cai Emily Reif Narayan Hegde J. Hipp Been Kim ... Martin Wattenberg F. Viégas G. Corrado Martin C. Stumpe Michael Terry 30 395 0 08 Feb 2019
Towards Automatic Concept-based Explanations Amirata Ghorbani James Wexler James Zou Been Kim FAtt LRM 27 19 0 07 Feb 2019
On the (In)fidelity and Sensitivity for Explanations Chih-Kuan Yeh Cheng-Yu Hsieh A. Suggala David I. Inouye Pradeep Ravikumar FAtt 28 445 0 27 Jan 2019
Personalized explanation in machine learning: A conceptualization J. Schneider J. Handali XAI FAtt 20 17 0 03 Jan 2019
Interactive Naming for Explaining Deep Neural Networks: A Formative Study M. Hamidi-Haines Zhongang Qi Alan Fern Fuxin Li Prasad Tadepalli FAtt HAI 14 11 0 18 Dec 2018
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability Xiaowei Huang Daniel Kroening Wenjie Ruan M. Kwiatkowska Youcheng Sun Emese Thamo Min Wu Xinping Yi AAML 16 50 0 18 Dec 2018
Can I trust you more? Model-Agnostic Hierarchical Explanations Michael Tsang Youbang Sun Dongxu Ren Yan Liu FAtt 16 25 0 12 Dec 2018
Networks for Nonlinear Diffusion Problems in Imaging Simon Arridge A. Hauptmann DiffM MedIm 8 18 0 29 Nov 2018
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems Sina Mohseni Niloofar Zarei Eric D. Ragan 23 102 0 28 Nov 2018
Scalable agent alignment via reward modeling: a research direction Jan Leike David M. Krueger Tom Everitt Miljan Martic Vishal Maini Shane Legg 28 392 0 19 Nov 2018
Secure Deep Learning Engineering: A Software Quality Assurance Perspective L. Ma Felix Juefei Xu Minhui Xue Q. Hu Sen Chen Bo-wen Li Yang Liu Jianjun Zhao Jianxiong Yin Simon See AAML 17 35 0 10 Oct 2018
What made you do this? Understanding black-box decisions with sufficient input subsets Brandon Carter Jonas W. Mueller Siddhartha Jain David K Gifford FAtt 37 77 0 09 Oct 2018
Model Cards for Model Reporting Margaret Mitchell Simone Wu Andrew Zaldivar Parker Barnes Lucy Vasserman Ben Hutchinson Elena Spitzer Inioluwa Deborah Raji Timnit Gebru 42 1,831 0 05 Oct 2018
Training Machine Learning Models by Regularizing their Explanations A. Ross FaML 18 0 0 29 Sep 2018
Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts Samuel Carton Qiaozhu Mei Paul Resnick FAtt AAML 16 34 0 01 Sep 2018
A Benchmark for Interpretability Methods in Deep Neural Networks Sara Hooker D. Erhan Pieter-Jan Kindermans Been Kim FAtt UQCV 28 670 0 28 Jun 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 15 932 0 20 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 38 1,840 0 31 May 2018
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems L. Ma Felix Juefei Xu Fuyuan Zhang Jiyuan Sun Minhui Xue ... Ting Su Li Li Yang Liu Jianjun Zhao Yadong Wang ELM 32 617 0 20 Mar 2018
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning Nicolas Papernot Patrick D. McDaniel OOD AAML 8 502 0 13 Mar 2018
The Challenge of Crafting Intelligible Intelligence Daniel S. Weld Gagan Bansal 15 241 0 09 Mar 2018
Considerations When Learning Additive Explanations for Black-Box Models S. Tan Giles Hooker Paul Koch Albert Gordo R. Caruana FAtt 22 20 0 26 Jan 2018
Embedding Deep Networks into Visual Explanations Zhongang Qi Saeed Khorram Fuxin Li 24 27 0 15 Sep 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 261 3,109 0 04 Nov 2016