Towards Robust Interpretability with Self-Explaining Neural Networks

20 June 2018

David Alvarez-Melis

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

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Title
Interpretable Image Classification with Differentiable Prototypes Assignment Dawid Rymarczyk Lukasz Struski Michal Górszczak K. Lewandowska Jacek Tabor Bartosz Zieliñski 34 98 0 06 Dec 2021
Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations Wolfgang Stammer Marius Memmel P. Schramowski Kristian Kersting 91 26 0 04 Dec 2021
Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability Yan Kang Yang Liu Yuezhou Wu Guoqiang Ma Qiang Yang 13 39 0 22 Nov 2021
Acquisition of Chess Knowledge in AlphaZero Thomas McGrath A. Kapishnikov Nenad Tomašev Adam Pearce Demis Hassabis Been Kim Ulrich Paquet Vladimir Kramnik 28 159 0 17 Nov 2021
Few-Shot Self-Rationalization with Natural Language Prompts Ana Marasović Iz Beltagy Doug Downey Matthew E. Peters LRM 26 106 0 16 Nov 2021
Image Classification with Consistent Supporting Evidence Peiqi Wang Ruizhi Liao Daniel Moyer Seth Berkowitz Steven Horng Polina Golland 42 2 0 13 Nov 2021
Self-Interpretable Model with TransformationEquivariant Interpretation Yipei Wang Xiaoqian Wang 38 23 0 09 Nov 2021
Defense Against Explanation Manipulation Ruixiang Tang Ninghao Liu Fan Yang Na Zou Xia Hu AAML 44 11 0 08 Nov 2021
Scanflow: A multi-graph framework for Machine Learning workflow management, supervision, and debugging Gusseppe Bravo Rocca Peini Liu Jordi Guitart Ajay Dholakia David Ellison Jeffrey Falkanger M. Hodak 30 6 0 04 Nov 2021
On the Effectiveness of Interpretable Feedforward Neural Network Miles Q. Li Benjamin C. M. Fung Adel Abusitta FaML AI4CE 28 3 0 03 Nov 2021
Instructive artificial intelligence (AI) for human training, assistance, and explainability Nicholas Kantack Nina Cohen Nathan D. Bos Corey Lowman James Everett Timothy Endres 14 1 0 02 Nov 2021
On Quantitative Evaluations of Counterfactuals Frederik Hvilshoj Alexandros Iosifidis Ira Assent 19 10 0 30 Oct 2021
Understanding Interlocking Dynamics of Cooperative Rationalization Mo Yu Yang Zhang Shiyu Chang Tommi Jaakkola 20 41 0 26 Oct 2021
Human-Centered Explainable AI (XAI): From Algorithms to User Experiences Q. V. Liao R. Varshney 23 224 0 20 Oct 2021
Interpreting Deep Learning Models in Natural Language Processing: A Review Xiaofei Sun Diyi Yang Xiaoya Li Tianwei Zhang Yuxian Meng Han Qiu Guoyin Wang Eduard H. Hovy Jiwei Li 17 44 0 20 Oct 2021
A Field Guide to Scientific XAI: Transparent and Interpretable Deep Learning for Bioinformatics Research Thomas P. Quinn Sunil R. Gupta Svetha Venkatesh Vuong Le OOD 16 2 0 13 Oct 2021
Self-explaining Neural Network with Concept-based Explanations for ICU Mortality Prediction Sayantan Kumar Sean C. Yu Thomas Kannampallil Zachary B. Abrams Andrew Michelson Philip R. O. Payne FAtt 16 7 0 09 Oct 2021
Deep Neural Networks and Tabular Data: A Survey V. Borisov Tobias Leemann Kathrin Seßler Johannes Haug Martin Pawelczyk Gjergji Kasneci LMTD 45 647 0 05 Oct 2021
Trustworthy AI: From Principles to Practices Bo-wen Li Peng Qi Bo Liu Shuai Di Jingen Liu Jiquan Pei Jinfeng Yi Bowen Zhou 119 356 0 04 Oct 2021
An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness Moninder Singh Gevorg Ghalachyan Kush R. Varshney R. Bryant 18 9 0 29 Sep 2021
Optimising for Interpretability: Convolutional Dynamic Alignment Networks Moritz D Boehle Mario Fritz Bernt Schiele 12 2 0 27 Sep 2021
AI Explainability 360: Impact and Design 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 25 14 0 24 Sep 2021
Toward a Unified Framework for Debugging Concept-based Models A. Bontempelli Fausto Giunchiglia Andrea Passerini Stefano Teso 20 4 0 23 Sep 2021
Self-explaining variational posterior distributions for Gaussian Process models Sarem Seitz BDL 16 0 0 08 Sep 2021
Impossibility Results in AI: A Survey Mario Brčič Roman V. Yampolskiy 21 25 0 01 Sep 2021
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation Srishti Gautam Marina M.-C. Höhne Stine Hansen Robert Jenssen Michael C. Kampffmeyer 27 49 0 27 Aug 2021
Towards Self-Explainable Graph Neural Network Enyan Dai Suhang Wang 36 84 0 26 Aug 2021
A Framework for Learning Ante-hoc Explainable Models via Concepts Anirban Sarkar Deepak Vijaykeerthy Anindya Sarkar V. Balasubramanian LRM BDL 22 46 0 25 Aug 2021
Longitudinal Distance: Towards Accountable Instance Attribution Rosina O. Weber Prateek Goel S. Amiri G. Simpson 16 0 0 23 Aug 2021
VAE-CE: Visual Contrastive Explanation using Disentangled VAEs Y. Poels Vlado Menkovski CoGe DRL 32 3 0 20 Aug 2021
Challenges for cognitive decoding using deep learning methods A. Thomas Christopher Ré R. Poldrack AI4CE 24 6 0 16 Aug 2021
Harnessing value from data science in business: ensuring explainability and fairness of solutions Krzysztof Chomiak Michal Miktus 13 0 0 10 Aug 2021
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms Martin Pawelczyk Sascha Bielawski J. V. D. Heuvel Tobias Richter Gjergji Kasneci CML 16 105 0 02 Aug 2021
Resisting Out-of-Distribution Data Problem in Perturbation of XAI Luyu Qiu Yi Yang Caleb Chen Cao Jing Liu Yueyuan Zheng H. Ngai J. H. Hsiao Lei Chen 9 18 0 27 Jul 2021
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks Lucie Charlotte Magister Dmitry Kazhdan Vikash Singh Pietro Lió 35 48 0 25 Jul 2021
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions Eike Petersen Yannik Potdevin Esfandiar Mohammadi Stephan Zidowitz Sabrina Breyer ... Sandra Henn Ludwig Pechmann M. Leucker P. Rostalski Christian Herzog FaML AILaw OOD 32 21 0 20 Jul 2021
Learning interaction rules from multi-animal trajectories via augmented behavioral models Keisuke Fujii Naoya Takeishi Kazushi Tsutsui Emyo Fujioka Nozomi Nishiumi ... Hiroyoshi Kohno K. Yoda S. Takahashi S. Hiryu Yoshinobu Kawahara 27 13 0 12 Jul 2021
When and How to Fool Explainable Models (and Humans) with Adversarial Examples Jon Vadillo Roberto Santana Jose A. Lozano SILM AAML 36 12 0 05 Jul 2021
Towards Fully Interpretable Deep Neural Networks: Are We There Yet? Sandareka Wickramanayake W. Hsu M. Lee FaML AI4CE 13 3 0 24 Jun 2021
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning Yang Liu Sujay Khandagale Colin White W. Neiswanger 37 65 0 23 Jun 2021
NoiseGrad: Enhancing Explanations by Introducing Stochasticity to Model Weights Kirill Bykov Anna Hedström Shinichi Nakajima Marina M.-C. Höhne FAtt 17 34 0 18 Jun 2021
It's FLAN time! Summing feature-wise latent representations for interpretability An-phi Nguyen María Rodríguez Martínez FAtt 13 0 0 18 Jun 2021
Keep CALM and Improve Visual Feature Attribution Jae Myung Kim Junsuk Choe Zeynep Akata Seong Joon Oh FAtt 344 20 0 15 Jun 2021
Entropy-based Logic Explanations of Neural Networks Pietro Barbiero Gabriele Ciravegna Francesco Giannini Pietro Lió Marco Gori S. Melacci FAtt XAI 25 78 0 12 Jun 2021
Local Explanation of Dialogue Response Generation Yi-Lin Tuan Connor Pryor Wenhu Chen Lise Getoor Luu Anh Tuan 25 11 0 11 Jun 2021
Locally Sparse Neural Networks for Tabular Biomedical Data Junchen Yang Ofir Lindenbaum Y. Kluger 24 33 0 11 Jun 2021
On the Lack of Robust Interpretability of Neural Text Classifiers Muhammad Bilal Zafar Michele Donini Dylan Slack Cédric Archambeau Sanjiv Ranjan Das K. Kenthapadi AAML 11 21 0 08 Jun 2021
Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution Jiacheng Xu Greg Durrett 38 16 0 03 Jun 2021
To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods E. Amparore Alan Perotti P. Bajardi FAtt 17 68 0 01 Jun 2021
DISSECT: Disentangled Simultaneous Explanations via Concept Traversals Asma Ghandeharioun Been Kim Chun-Liang Li Brendan Jou B. Eoff Rosalind W. Picard AAML 30 53 0 31 May 2021