Towards Robust Interpretability with Self-Explaining Neural Networks

20 June 2018

David Alvarez-Melis

Papers citing "Towards Robust Interpretability with Self-Explaining Neural Networks"

50 / 507 papers shown

Title
Measuring Association Between Labels and Free-Text Rationales Sarah Wiegreffe Ana Marasović Noah A. Smith 282 170 0 24 Oct 2020
A Framework to Learn with Interpretation Jayneel Parekh Pavlo Mozharovskyi Florence dÁlché-Buc AI4CE FAtt 25 30 0 19 Oct 2020
Altruist: Argumentative Explanations through Local Interpretations of Predictive Models Ioannis Mollas Nick Bassiliades Grigorios Tsoumakas 11 13 0 15 Oct 2020
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI Alon Jacovi Ana Marasović Tim Miller Yoav Goldberg 252 426 0 15 Oct 2020
Human-interpretable model explainability on high-dimensional data Damien de Mijolla Christopher Frye M. Kunesch J. Mansir Ilya Feige FAtt 17 8 0 14 Oct 2020
Learning Propagation Rules for Attribution Map Generation Yiding Yang Jiayan Qiu Xiuming Zhang Dacheng Tao Xinchao Wang FAtt 38 17 0 14 Oct 2020
Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension Ekta Sood Simon Tannert Diego Frassinelli Andreas Bulling Ngoc Thang Vu HAI 24 54 0 13 Oct 2020
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects Amir-Hossein Karimi Gilles Barthe Bernhard Schölkopf Isabel Valera FaML 14 172 0 08 Oct 2020
Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers Hanjie Chen Yangfeng Ji AAML VLM 13 63 0 01 Oct 2020
Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task Han-Ching Wu Wenjie Ruan Jiangtao Wang Dingchang Zheng Bei Liu ... Xiangfei Chai Jian Chen Kunwei Li Shaolin Li A. Helal 32 25 0 30 Sep 2020
A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing Ping Lang Xiongjun Fu M. Martorella Jian Dong Rui Qin Xianpeng Meng M. Xie 26 39 0 29 Sep 2020
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors Yi-Shan Lin Wen-Chuan Lee Z. Berkay Celik XAI 29 93 0 22 Sep 2020
Transparency and granularity in the SP Theory of Intelligence and its realisation in the SP Computer Model J. Wolff 11 5 0 07 Sep 2020
How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks Thomas Fel David Vigouroux Rémi Cadène Thomas Serre XAI FAtt 26 31 0 07 Sep 2020
How Useful Are the Machine-Generated Interpretations to General Users? A Human Evaluation on Guessing the Incorrectly Predicted Labels Hua Shen Ting-Hao 'Kenneth' Huang FAtt HAI 9 56 0 26 Aug 2020
Counterfactual Explanations for Machine Learning on Multivariate Time Series Data E. Ates Burak Aksar V. Leung A. Coskun AI4TS 51 65 0 25 Aug 2020
DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data Zhaocheng Liu Qiang Liu Haoli Zhang Yuntian Chen 11 12 0 22 Aug 2020
iCaps: An Interpretable Classifier via Disentangled Capsule Networks Dahuin Jung Jonghyun Lee Jihun Yi Sungroh Yoon 28 12 0 20 Aug 2020
Trust and Medical AI: The challenges we face and the expertise needed to overcome them Thomas P. Quinn M. Senadeera Stephan Jacobs S. Coghlan Vuong Le 16 122 0 18 Aug 2020
Tackling COVID-19 through Responsible AI Innovation: Five Steps in the Right Direction David Leslie 27 67 0 15 Aug 2020
Deep Active Learning by Model Interpretability Qiang Liu Zhaocheng Liu Xiaofang Zhu Yeliang Xiu 8 4 0 23 Jul 2020
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance Mattia Carletti M. Terzi Gian Antonio Susto 36 42 0 21 Jul 2020
timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers Felix Mujkanovic Vanja Doskoc Martin Schirneck Patrick Schäfer Tobias Friedrich FAtt AI4TS 19 23 0 15 Jul 2020
Learning Invariances for Interpretability using Supervised VAE An-phi Nguyen María Rodríguez Martínez DRL 14 2 0 15 Jul 2020
On quantitative aspects of model interpretability An-phi Nguyen María Rodríguez Martínez 16 114 0 15 Jul 2020
Concept Learners for Few-Shot Learning Kaidi Cao Maria Brbic J. Leskovec VLM OffRL 27 4 0 14 Jul 2020
Concept Bottleneck Models Pang Wei Koh Thao Nguyen Y. S. Tang Stephen Mussmann Emma Pierson Been Kim Percy Liang 40 776 0 09 Jul 2020
Gaussian Process Regression with Local Explanation Yuya Yoshikawa Tomoharu Iwata FAtt 8 18 0 03 Jul 2020
Drug discovery with explainable artificial intelligence José Jiménez-Luna F. Grisoni G. Schneider 30 625 0 01 Jul 2020
Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes Loc Trinh Michael Tsang Sirisha Rambhatla Yan Liu 6 6 0 28 Jun 2020
Not all Failure Modes are Created Equal: Training Deep Neural Networks for Explicable (Mis)Classification Alberto Olmo Sailik Sengupta S. Kambhampati 25 6 0 26 Jun 2020
Generative causal explanations of black-box classifiers Matthew R. O’Shaughnessy Gregory H. Canal Marissa Connor Mark A. Davenport Christopher Rozell CML 30 73 0 24 Jun 2020
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction Esther Puyol-Antón Cheng Chen J. Clough B. Ruijsink B. Sidhu ... M. Elliott Vishal S. Mehta Daniel Rueckert C. Rinaldi A. King 19 32 0 24 Jun 2020
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey Arun Das P. Rad XAI 24 593 0 16 Jun 2020
A Semiparametric Approach to Interpretable Machine Learning Numair Sani Jaron J. R. Lee Razieh Nabi I. Shpitser 15 6 0 08 Jun 2020
Higher-Order Explanations of Graph Neural Networks via Relevant Walks Thomas Schnake Oliver Eberle Jonas Lederer Shinichi Nakajima Kristof T. Schütt Klaus-Robert Muller G. Montavon 32 215 0 05 Jun 2020
DeepCoDA: personalized interpretability for compositional health data Thomas P. Quinn Dang Nguyen Santu Rana Sunil R. Gupta Svetha Venkatesh 9 12 0 02 Jun 2020
Explainable Artificial Intelligence: a Systematic Review Giulia Vilone Luca Longo XAI 22 266 0 29 May 2020
Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport Kyle Swanson L. Yu Tao Lei OT 29 37 0 27 May 2020
The best way to select features? Xin Man Ernest P. Chan 16 60 0 26 May 2020
Interpretable and Accurate Fine-grained Recognition via Region Grouping Zixuan Huang Yin Li 12 138 0 21 May 2020
Towards Interpretable Deep Learning Models for Knowledge Tracing Yu Lu De-Wu Wang Qinggang Meng Penghe Chen 17 36 0 13 May 2020
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds M. Kovalev Lev V. Utkin AAML 21 31 0 05 May 2020
Evaluating and Aggregating Feature-based Model Explanations Umang Bhatt Adrian Weller J. M. F. Moura XAI 33 218 0 01 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 38 370 0 30 Apr 2020
TSInsight: A local-global attribution framework for interpretability in time-series data Shoaib Ahmed Siddiqui Dominique Mercier Andreas Dengel Sheraz Ahmed FAtt AI4TS 11 12 0 06 Apr 2020
Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection Hanjie Chen Guangtao Zheng Yangfeng Ji FAtt 36 91 0 04 Apr 2020
Born-Again Tree Ensembles Thibaut Vidal Toni Pacheco Maximilian Schiffer 62 53 0 24 Mar 2020
Invariant Rationalization Shiyu Chang Yang Zhang Mo Yu Tommi Jaakkola 179 201 0 22 Mar 2020
Neural Generators of Sparse Local Linear Models for Achieving both Accuracy and Interpretability Yuya Yoshikawa Tomoharu Iwata 16 7 0 13 Mar 2020