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
Metafeatures-based Rule-Extraction for Classifiers on Behavioral and Textual Data Yanou Ramon David Martens Theodoros Evgeniou S. Praet 13 8 0 10 Mar 2020
ResNets, NeuralODEs and CT-RNNs are Particular Neural Regulatory Networks Radu Grosu 16 1 0 26 Feb 2020
Self-explaining AI as an alternative to interpretable AI Daniel C. Elton 8 56 0 12 Feb 2020
DANCE: Enhancing saliency maps using decoys Y. Lu Wenbo Guo Xinyu Xing William Stafford Noble AAML 34 14 0 03 Feb 2020
Regularizing Reasons for Outfit Evaluation with Gradient Penalty Xingxing Zou Zhizhong Li Ke Bai Dahua Lin W. Wong 21 6 0 02 Feb 2020
Black Box Explanation by Learning Image Exemplars in the Latent Feature Space Riccardo Guidotti A. Monreale Stan Matwin D. Pedreschi FAtt 11 67 0 27 Jan 2020
An interpretable neural network model through piecewise linear approximation Mengzhuo Guo Qingpeng Zhang Xiuwu Liao D. Zeng MILM FAtt 19 7 0 20 Jan 2020
A Formal Approach to Explainability Lior Wolf Tomer Galanti Tamir Hazan FAtt GAN 6 22 0 15 Jan 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 38 300 0 08 Jan 2020
Meta Decision Trees for Explainable Recommendation Systems Eyal Shulman Lior Wolf 18 18 0 19 Dec 2019
Sanity Checks for Saliency Metrics Richard J. Tomsett Daniel Harborne Supriyo Chakraborty Prudhvi K. Gurram Alun D. Preece XAI 16 167 0 29 Nov 2019
Attributional Robustness Training using Input-Gradient Spatial Alignment M. Singh Nupur Kumari Puneet Mangla Abhishek Sinha V. Balasubramanian Balaji Krishnamurthy OOD 26 10 0 29 Nov 2019
Towards a Unified Evaluation of Explanation Methods without Ground Truth Hao Zhang Jiayi Chen Haotian Xue Quanshi Zhang XAI 24 7 0 20 Nov 2019
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models Benjamin J. Lengerich S. Tan C. Chang Giles Hooker R. Caruana 21 39 0 12 Nov 2019
Sobolev Independence Criterion Youssef Mroueh Tom Sercu Mattia Rigotti Inkit Padhi Cicero Nogueira dos Santos 11 5 0 31 Oct 2019
Weight of Evidence as a Basis for Human-Oriented Explanations David Alvarez-Melis Hal Daumé Jennifer Wortman Vaughan Hanna M. Wallach XAI FAtt 21 20 0 29 Oct 2019
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control Mo Yu Shiyu Chang Yang Zhang Tommi Jaakkola 21 140 0 29 Oct 2019
A Game Theoretic Approach to Class-wise Selective Rationalization Shiyu Chang Yang Zhang Mo Yu Tommi Jaakkola 22 60 0 28 Oct 2019
How a minimal learning agent can infer the existence of unobserved variables in a complex environment K. Ried B. Eva Thomas Müller H. Briegel 9 15 0 15 Oct 2019
Interpretable Text Classification Using CNN and Max-pooling Hao Cheng Xiaoqing Yang Zang Li Yanghua Xiao Yucheng Lin FAtt 6 5 0 14 Oct 2019
MonoNet: Towards Interpretable Models by Learning Monotonic Features An-phi Nguyen María Rodríguez Martínez FAtt 16 13 0 30 Sep 2019
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques Vijay Arya Rachel K. E. Bellamy Pin-Yu Chen Amit Dhurandhar Michael Hind ... Karthikeyan Shanmugam Moninder Singh Kush R. Varshney Dennis L. Wei Yunfeng Zhang XAI 8 390 0 06 Sep 2019
The Partial Response Network: a neural network nomogram P. Lisboa S. Ortega-Martorell Sadie Cashman I. Olier 20 2 0 16 Aug 2019
TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems Wenbo Guo Lun Wang Xinyu Xing Min Du D. Song 22 227 0 02 Aug 2019
Interpretability Beyond Classification Output: Semantic Bottleneck Networks M. Losch Mario Fritz Bernt Schiele UQCV 31 60 0 25 Jul 2019
Benchmarking Attribution Methods with Relative Feature Importance Mengjiao Yang Been Kim FAtt XAI 21 140 0 23 Jul 2019
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations Thibault Laugel Marie-Jeanne Lesot Christophe Marsala X. Renard Marcin Detyniecki 19 194 0 22 Jul 2019
Towards Robust, Locally Linear Deep Networks Guang-He Lee David Alvarez-Melis Tommi Jaakkola ODL 19 48 0 07 Jul 2019
On Validating, Repairing and Refining Heuristic ML Explanations Alexey Ignatiev Nina Narodytska Sasha Rubin FAtt LRM 6 62 0 04 Jul 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 22 329 0 19 Jun 2019
Issues with post-hoc counterfactual explanations: a discussion Thibault Laugel Marie-Jeanne Lesot Christophe Marsala Marcin Detyniecki CML 107 44 0 11 Jun 2019
Is Attention Interpretable? Sofia Serrano Noah A. Smith 14 670 0 09 Jun 2019
Model Agnostic Contrastive Explanations for Structured Data Amit Dhurandhar Tejaswini Pedapati Avinash Balakrishnan Pin-Yu Chen Karthikeyan Shanmugam Ruchi Puri FAtt 20 82 0 31 May 2019
Certifiably Robust Interpretation in Deep Learning Alexander Levine Sahil Singla S. Feizi FAtt AAML 23 63 0 28 May 2019
EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction Diane Bouchacourt Ludovic Denoyer FAtt 21 21 0 28 May 2019
Analyzing the Interpretability Robustness of Self-Explaining Models Haizhong Zheng Earlence Fernandes A. Prakash AAML LRM 18 7 0 27 May 2019
Not All Features Are Equal: Feature Leveling Deep Neural Networks for Better Interpretation Yingjing Lu Runde Yang MILM 16 2 0 24 May 2019
TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical Features Mohsin Munir Shoaib Ahmed Siddiqui Ferdinand Küsters Dominique Mercier Andreas Dengel Sheraz Ahmed AI4TS 12 19 0 15 May 2019
Consensus-based Interpretable Deep Neural Networks with Application to Mortality Prediction Shaeke Salman S. N. Payrovnaziri Xiuwen Liu Pablo Rengifo-Moreno Zhe He 14 0 0 14 May 2019
When Deep Learning Met Code Search J. Cambronero Hongyu Li Seohyun Kim Koushik Sen S. Chandra CLIP 24 218 0 09 May 2019
Visualizing Deep Networks by Optimizing with Integrated Gradients Zhongang Qi Saeed Khorram Fuxin Li FAtt 17 122 0 02 May 2019
Explainability in Human-Agent Systems A. Rosenfeld A. Richardson XAI 27 203 0 17 Apr 2019
Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra A. Chakraborty Sung Hak Lim M. Nojiri 37 43 0 03 Apr 2019
Explaining Anomalies Detected by Autoencoders Using SHAP Liat Antwarg Ronnie Mindlin Miller Bracha Shapira Lior Rokach FAtt TDI 16 86 0 06 Mar 2019
Functional Transparency for Structured Data: a Game-Theoretic Approach Guang-He Lee Wengong Jin David Alvarez-Melis Tommi Jaakkola 24 19 0 26 Feb 2019
Regularizing Black-box Models for Improved Interpretability Gregory Plumb Maruan Al-Shedivat Ángel Alexander Cabrera Adam Perer Eric P. Xing Ameet Talwalkar AAML 24 79 0 18 Feb 2019
Neural Network Attributions: A Causal Perspective Aditya Chattopadhyay Piyushi Manupriya Anirban Sarkar V. Balasubramanian CML 6 143 0 06 Feb 2019
Fooling Neural Network Interpretations via Adversarial Model Manipulation Juyeon Heo Sunghwan Joo Taesup Moon AAML FAtt 13 201 0 06 Feb 2019
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation Sahil Singla Eric Wallace Shi Feng S. Feizi FAtt 18 59 0 01 Feb 2019
Fairwashing: the risk of rationalization Ulrich Aïvodji Hiromi Arai O. Fortineau Sébastien Gambs Satoshi Hara Alain Tapp FaML 19 142 0 28 Jan 2019