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
iCaps: An Interpretable Classifier via Disentangled Capsule Networks Dahuin Jung Jonghyun Lee Jihun Yi Sungroh Yoon 20 12 0 20 Aug 2020
Abstracting Deep Neural Networks into Concept Graphs for Concept Level Interpretability Avinash Kori Parth Natekar Ganapathy Krishnamurthi Balaji Srinivasan 18 8 0 14 Aug 2020
Survey of XAI in digital pathology Milda Pocevičiūtė Gabriel Eilertsen Claes Lundström 6 56 0 14 Aug 2020
Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization Mara Graziani Sebastian Otalora Stephane Marchand-Maillet Henning Muller Vincent Andrearczyk AAML MedIm 16 0 0 04 Aug 2020
DeepVA: Bridging Cognition and Computation through Semantic Interaction and Deep Learning Yail Bian John E. Wenskovitch Chris North 8 11 0 31 Jul 2020
Debiasing Concept-based Explanations with Causal Analysis M. T. Bahadori David Heckerman FAtt CML 6 38 0 22 Jul 2020
Melody: Generating and Visualizing Machine Learning Model Summary to Understand Data and Classifiers Together G. Chan E. Bertini L. G. Nonato Brian Barr Claudio T. Silva 20 17 0 21 Jul 2020
Learning Invariances for Interpretability using Supervised VAE An-phi Nguyen María Rodríguez Martínez DRL 9 2 0 15 Jul 2020
On quantitative aspects of model interpretability An-phi Nguyen María Rodríguez Martínez 11 114 0 15 Jul 2020
Explaining Deep Neural Networks using Unsupervised Clustering Yu-Han Liu Sercan O. Arik SSL AI4CE 6 11 0 15 Jul 2020
Concept Learners for Few-Shot Learning Kaidi Cao Maria Brbic J. Leskovec VLM OffRL 22 4 0 14 Jul 2020
Towards causal benchmarking of bias in face analysis algorithms Guha Balakrishnan Yuanjun Xiong Wei Xia Pietro Perona CVBM 13 89 0 13 Jul 2020
A simple defense against adversarial attacks on heatmap explanations Laura Rieger Lars Kai Hansen FAtt AAML 25 37 0 13 Jul 2020
Locality Guided Neural Networks for Explainable Artificial Intelligence Randy Tan N. Khan L. Guan 11 8 0 12 Jul 2020
Concept Bottleneck Models Pang Wei Koh Thao Nguyen Y. S. Tang Stephen Mussmann Emma Pierson Been Kim Percy Liang 13 776 0 09 Jul 2020
Drug discovery with explainable artificial intelligence José Jiménez-Luna F. Grisoni G. Schneider 27 625 0 01 Jul 2020
Unifying Model Explainability and Robustness via Machine-Checkable Concepts Vedant Nanda Till Speicher John P. Dickerson Krishna P. Gummadi Muhammad Bilal Zafar AAML 4 4 0 01 Jul 2020
Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors Ruihan Zhang Prashan Madumal Tim Miller Krista A. Ehinger Benjamin I. P. Rubinstein FAtt 14 94 0 27 Jun 2020
Causality Learning: A New Perspective for Interpretable Machine Learning Guandong Xu Tri Dung Duong Q. Li S. Liu Xianzhi Wang XAI OOD CML 8 51 0 27 Jun 2020
Generative causal explanations of black-box classifiers Matthew R. O’Shaughnessy Gregory H. Canal Marissa Connor Mark A. Davenport Christopher Rozell CML 25 73 0 24 Jun 2020
Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection Michael Tsang Dehua Cheng Hanpeng Liu Xuening Feng Eric Zhou Yan Liu FAtt 8 60 0 19 Jun 2020
A generalizable saliency map-based interpretation of model outcome Shailja Thakur S. Fischmeister AAML FAtt MILM 19 2 0 16 Jun 2020
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey Arun Das P. Rad XAI 16 593 0 16 Jun 2020
Fairness Under Feature Exemptions: Counterfactual and Observational Measures Sanghamitra Dutta Praveen Venkatesh Piotr (Peter) Mardziel Anupam Datta P. Grover 6 16 0 14 Jun 2020
Explaining Predictions by Approximating the Local Decision Boundary G. Vlassopoulos T. Erven Henry Brighton Vlado Menkovski FAtt 9 8 0 14 Jun 2020
Aligning Faithful Interpretations with their Social Attribution Alon Jacovi Yoav Goldberg 10 105 0 01 Jun 2020
Explainable Artificial Intelligence: a Systematic Review Giulia Vilone Luca Longo XAI 20 266 0 29 May 2020
Explainable deep learning models in medical image analysis Amitojdeep Singh S. Sengupta Vasudevan Lakshminarayanan XAI 29 482 0 28 May 2020
Explaining Neural Networks by Decoding Layer Activations Johannes Schneider Michalis Vlachos AI4CE 14 15 0 27 May 2020
CausaLM: Causal Model Explanation Through Counterfactual Language Models Amir Feder Nadav Oved Uri Shalit Roi Reichart CML LRM 36 156 0 27 May 2020
The challenges of deploying artificial intelligence models in a rapidly evolving pandemic Yipeng Hu J. Jacob Geoffrey J. M. Parker D. Hawkes J. Hurst Danail Stoyanov OOD 14 65 0 19 May 2020
On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors Adriano Lucieri Muhammad Naseer Bajwa S. Braun M. I. Malik Andreas Dengel Sheraz Ahmed MedIm 161 64 0 05 May 2020
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? Peter Hase Mohit Bansal FAtt 18 298 0 04 May 2020
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware? Marco Melis Michele Scalas Ambra Demontis Davide Maiorca Battista Biggio Giorgio Giacinto Fabio Roli AAML FAtt 14 27 0 04 May 2020
Explaining AI-based Decision Support Systems using Concept Localization Maps Adriano Lucieri Muhammad Naseer Bajwa Andreas Dengel Sheraz Ahmed 19 26 0 04 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 29 371 0 30 Apr 2020
Corpus-level and Concept-based Explanations for Interpretable Document Classification Tian Shi Xuchao Zhang Ping Wang Chandan K. Reddy FAtt 16 8 0 24 Apr 2020
Adversarial Attacks and Defenses: An Interpretation Perspective Ninghao Liu Mengnan Du Ruocheng Guo Huan Liu Xia Hu AAML 26 8 0 23 Apr 2020
Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness? Alon Jacovi Yoav Goldberg XAI 6 564 0 07 Apr 2020
MetaPoison: Practical General-purpose Clean-label Data Poisoning W. R. Huang Jonas Geiping Liam H. Fowl Gavin Taylor Tom Goldstein 9 188 0 01 Apr 2020
Architecture Disentanglement for Deep Neural Networks Jie Hu Liujuan Cao QiXiang Ye Tong Tong Shengchuan Zhang Ke Li Feiyue Huang Rongrong Ji Ling Shao AAML 23 16 0 30 Mar 2020
A Survey of Deep Learning for Scientific Discovery M. Raghu Erica Schmidt OOD AI4CE 35 120 0 26 Mar 2020
RelatIF: Identifying Explanatory Training Examples via Relative Influence Elnaz Barshan Marc-Etienne Brunet Gintare Karolina Dziugaite TDI 27 30 0 25 Mar 2020
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Wojciech Samek G. Montavon Sebastian Lapuschkin Christopher J. Anders K. Müller XAI 44 82 0 17 Mar 2020
Metafeatures-based Rule-Extraction for Classifiers on Behavioral and Textual Data Yanou Ramon David Martens Theodoros Evgeniou S. Praet 6 8 0 10 Mar 2020
Explaining Knowledge Distillation by Quantifying the Knowledge Xu Cheng Zhefan Rao Yilan Chen Quanshi Zhang 10 119 0 07 Mar 2020
The Emerging Landscape of Explainable AI Planning and Decision Making Tathagata Chakraborti S. Sreedharan S. Kambhampati 27 112 0 26 Feb 2020
Neuron Shapley: Discovering the Responsible Neurons Amirata Ghorbani James Y. Zou FAtt TDI 25 108 0 23 Feb 2020
Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example Serena Booth Yilun Zhou Ankit J. Shah J. Shah BDL 4 2 0 19 Feb 2020
HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models Qianwen Wang W. Alexander J. Pegg Huamin Qu Min Chen VLM 17 10 0 12 Feb 2020