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Label-Free Explainability for Unsupervised Models

3 March 2022

Papers citing "Label-Free Explainability for Unsupervised Models"

37 / 37 papers shown

Title
Explaining Latent Representations with a Corpus of Examples Jonathan Crabbé Zhaozhi Qian F. Imrie M. Schaar FAtt 59 38 0 28 Oct 2021
Explaining Time Series Predictions with Dynamic Masks Jonathan Crabbé M. Schaar FAtt AI4TS 90 81 0 09 Jun 2021
Understanding Instance-based Interpretability of Variational Auto-Encoders Zhifeng Kong Kamalika Chaudhuri TDI 63 28 0 29 May 2021
Learning outside the Black-Box: The pursuit of interpretable models Jonathan Crabbé Yao Zhang W. Zame M. Schaar 35 24 0 17 Nov 2020
Captum: A unified and generic model interpretability library for PyTorch Narine Kokhlikyan Vivek Miglani Miguel Martin Edward Wang B. Alsallakh ... Alexander Melnikov Natalia Kliushkina Carlos Araya Siqi Yan Orion Reblitz-Richardson FAtt 144 846 0 16 Sep 2020
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey Arun Das P. Rad XAI 158 604 0 16 Jun 2020
Supervised Contrastive Learning Prannay Khosla Piotr Teterwak Chen Wang Aaron Sarna Yonglong Tian Phillip Isola Aaron Maschinot Ce Liu Dilip Krishnan SSL 165 4,572 0 23 Apr 2020
Building and Interpreting Deep Similarity Models Oliver Eberle Jochen Büttner Florian Kräutli K. Müller Matteo Valleriani G. Montavon 56 58 0 11 Mar 2020
A Distributional Framework for Data Valuation Amirata Ghorbani Michael P. Kim James Zou TDI 52 132 0 27 Feb 2020
Estimating Training Data Influence by Tracing Gradient Descent G. Pruthi Frederick Liu Mukund Sundararajan Satyen Kale TDI 99 417 0 19 Feb 2020
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 510 10,591 0 17 Feb 2020
A Simple Framework for Contrastive Learning of Visual Representations Ting-Li Chen Simon Kornblith Mohammad Norouzi Geoffrey E. Hinton SSL 378 18,866 0 13 Feb 2020
Inference with Deep Generative Priors in High Dimensions Jillian R. Fisher Mojtaba Sahraee-Ardakan S. Rangan Zaid Harchaoui Yejin Choi BDL 51 47 0 08 Nov 2019
Concept Saliency Maps to Visualize Relevant Features in Deep Generative Models L. Brocki N. C. Chung FAtt 45 21 0 29 Oct 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 127 6,293 0 22 Oct 2019
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI Erico Tjoa Cuntai Guan XAI 112 1,451 0 17 Jul 2019
Learning Interpretable Disentangled Representations using Adversarial VAEs Mhd Hasan Sarhan Abouzar Eslami Nassir Navab Shadi Albarqouni DRL OOD 128 21 0 17 Apr 2019
Data Shapley: Equitable Valuation of Data for Machine Learning Amirata Ghorbani James Zou TDI FedML 78 789 0 05 Apr 2019
Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey Longlong Jing Yingli Tian SSL 159 1,700 0 16 Feb 2019
Representer Point Selection for Explaining Deep Neural Networks Chih-Kuan Yeh Joon Sik Kim Ian En-Hsu Yen Pradeep Ravikumar TDI 79 253 0 23 Nov 2018
A Benchmark for Interpretability Methods in Deep Neural Networks Sara Hooker D. Erhan Pieter-Jan Kindermans Been Kim FAtt UQCV 118 683 0 28 Jun 2018
Understanding disentangling in $β$ -VAE Christopher P. Burgess I. Higgins Arka Pal Loic Matthey Nicholas Watters Guillaume Desjardins Alexander Lerchner CoGe DRL 71 831 0 10 Apr 2018
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning Nicolas Papernot Patrick McDaniel OOD AAML 149 508 0 13 Mar 2018
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 227 1,850 0 30 Nov 2017
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 129 2,361 0 01 Nov 2017
Recent Trends in Deep Learning Based Natural Language Processing Tom Young Devamanyu Hazarika Soujanya Poria Min Zhang 75 2,835 0 09 Aug 2017
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 293 2,267 0 24 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 22,018 0 22 May 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 76 1,525 0 11 Apr 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 203 3,881 0 10 Apr 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 216 2,905 0 14 Mar 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 193 6,018 0 04 Mar 2017
The Mythos of Model Interpretability Zachary Chase Lipton FaML 183 3,706 0 10 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,033 0 16 Feb 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,426 0 10 Dec 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.0K 150,312 0 22 Dec 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 314 7,316 0 20 Dec 2013