"Why Should I Trust You?": Explaining the Predictions of Any Classifier

16 February 2016

Papers citing ""Why Should I Trust You?": Explaining the Predictions of Any Classifier"

50 / 4,309 papers shown

Title
Evaluating and Aggregating Feature-based Model Explanations Umang Bhatt Adrian Weller J. M. F. Moura XAI 38 218 0 01 May 2020
The Grammar of Interactive Explanatory Model Analysis Hubert Baniecki Dariusz Parzych P. Biecek 24 44 0 01 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 49 371 0 30 Apr 2020
Neural Additive Models: Interpretable Machine Learning with Neural Nets Rishabh Agarwal Levi Melnick Nicholas Frosst Xuezhou Zhang Ben Lengerich R. Caruana Geoffrey E. Hinton 46 406 0 29 Apr 2020
An Explainable Deep Learning-based Prognostic Model for Rotating Machinery Namkyoung Lee M. Azarian M. Pecht 16 14 0 28 Apr 2020
Time Series Forecasting With Deep Learning: A Survey Bryan Lim S. Zohren AI4TS AI4CE 59 1,192 0 28 Apr 2020
Sequential Interpretability: Methods, Applications, and Future Direction for Understanding Deep Learning Models in the Context of Sequential Data B. Shickel Parisa Rashidi AI4TS 33 17 0 27 Apr 2020
An Extension of LIME with Improvement of Interpretability and Fidelity Sheng Shi Yangzhou Du Wei Fan FAtt 16 8 0 26 Apr 2020
Adversarial Attacks and Defenses: An Interpretation Perspective Ninghao Liu Mengnan Du Ruocheng Guo Huan Liu Xia Hu AAML 31 8 0 23 Apr 2020
Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs Sungsoo Ray Hong Jessica Hullman E. Bertini HAI 22 191 0 23 Apr 2020
Learning a Formula of Interpretability to Learn Interpretable Formulas M. Virgolin A. D. Lorenzo Eric Medvet Francesca Randone 25 33 0 23 Apr 2020
Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks Narinder Singh Punn Sonali Agarwal 15 191 0 23 Apr 2020
Perturb More, Trap More: Understanding Behaviors of Graph Neural Networks Chaojie Ji Ruxin Wang Hongyan Wu 31 7 0 21 Apr 2020
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision Damien Teney Ehsan Abbasnejad Anton Van Den Hengel OOD SSL CML 37 118 0 20 Apr 2020
How recurrent networks implement contextual processing in sentiment analysis Niru Maheswaranathan David Sussillo 22 22 0 17 Apr 2020
CrossCheck: Rapid, Reproducible, and Interpretable Model Evaluation Dustin L. Arendt Zhuanyi Huang Prasha Shrestha Ellyn Ayton M. Glenski Svitlana Volkova 32 8 0 16 Apr 2020
Explaining Regression Based Neural Network Model Mégane Millan Catherine Achard FAtt 24 3 0 15 Apr 2020
Deep Learning Models for Multilingual Hate Speech Detection Sai Saket Aluru Binny Mathew Punyajoy Saha Animesh Mukherjee 16 148 0 14 Apr 2020
Complaint-driven Training Data Debugging for Query 2.0 Weiyuan Wu Lampros Flokas Eugene Wu Jiannan Wang 32 43 0 12 Apr 2020
Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness? Alon Jacovi Yoav Goldberg XAI 48 571 0 07 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 19 12 0 06 Apr 2020
Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection Hanjie Chen Guangtao Zheng Yangfeng Ji FAtt 38 92 0 04 Apr 2020
Attribution in Scale and Space Shawn Xu Subhashini Venugopalan Mukund Sundararajan FAtt BDL 14 71 0 03 Apr 2020
Under the Hood of Neural Networks: Characterizing Learned Representations by Functional Neuron Populations and Network Ablations Richard Meyes Constantin Waubert de Puiseau Andres Felipe Posada-Moreno Tobias Meisen AI4CE 30 21 0 02 Apr 2020
NBDT: Neural-Backed Decision Trees Alvin Wan Lisa Dunlap Daniel Ho Jihan Yin Scott Lee Henry Jin Suzanne Petryk Sarah Adel Bargal Joseph E. Gonzalez 26 99 0 01 Apr 2020
Ontology-based Interpretable Machine Learning for Textual Data Phung Lai Nhathai Phan Han Hu Anuja Badeti David Newman Dejing Dou 19 8 0 01 Apr 2020
Code Prediction by Feeding Trees to Transformers Seohyun Kim Jinman Zhao Yuchi Tian S. Chandra 48 217 0 30 Mar 2020
A Survey of Deep Learning for Scientific Discovery M. Raghu Erica Schmidt OOD AI4CE 47 120 0 26 Mar 2020
Plausible Counterfactuals: Auditing Deep Learning Classifiers with Realistic Adversarial Examples Alejandro Barredo Arrieta Javier Del Ser AAML 15 22 0 25 Mar 2020
Layerwise Knowledge Extraction from Deep Convolutional Networks S. Odense Artur Garcez FAtt 26 9 0 19 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 51 82 0 17 Mar 2020
Directions for Explainable Knowledge-Enabled Systems Shruthi Chari Daniel Gruen Oshani Seneviratne D. McGuinness XAI 18 32 0 17 Mar 2020
Foundations of Explainable Knowledge-Enabled Systems Shruthi Chari Daniel Gruen Oshani Seneviratne D. McGuinness 41 28 0 17 Mar 2020
Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI L. Arras Ahmed Osman Wojciech Samek XAI AAML 21 150 0 16 Mar 2020
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions Zebin Yang Aijun Zhang Agus Sudjianto FAtt 19 126 0 16 Mar 2020
Self-Supervised Discovering of Interpretable Features for Reinforcement Learning Wenjie Shi Gao Huang Shiji Song Zhuoyuan Wang Tingyu Lin Cheng Wu SSL 28 18 0 16 Mar 2020
Model Agnostic Multilevel Explanations Karthikeyan N. Ramamurthy B. Vinzamuri Yunfeng Zhang Amit Dhurandhar 29 41 0 12 Mar 2020
xCos: An Explainable Cosine Metric for Face Verification Task Yu-sheng Lin Zhe-Yu Liu Yu-An Chen Yu-Siang Wang Ya-Liang Chang Winston H. Hsu CVBM 33 46 0 11 Mar 2020
Fairness by Explicability and Adversarial SHAP Learning James M. Hickey Pietro G. Di Stefano V. Vasileiou FAtt FedML 33 19 0 11 Mar 2020
Machine Learning for Intelligent Optical Networks: A Comprehensive Survey Rentao Gu Zeyuan Yang Yuefeng Ji 27 109 0 11 Mar 2020
IROF: a low resource evaluation metric for explanation methods Laura Rieger Lars Kai Hansen 28 55 0 09 Mar 2020
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation Raha Moraffah Mansooreh Karami Ruocheng Guo A. Raglin Huan Liu CML ELM XAI 29 213 0 09 Mar 2020
Link Prediction using Graph Neural Networks for Master Data Management Balaji Ganesan Srinivas Parkala Neeraj R Singh Sumit Bhatia Gayatri Mishra Matheen Ahmed Pasha Hima Patel Somashekar Naganna AI4CE 35 11 0 07 Mar 2020
MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers Wei Song Xuezixiang Li Sadia Afroz D. Garg Dmitry Kuznetsov Heng Yin AAML 55 27 0 06 Mar 2020
What went wrong and when? Instance-wise Feature Importance for Time-series Models S. Tonekaboni Shalmali Joshi Kieran Campbell David Duvenaud Anna Goldenberg FAtt OOD AI4TS 56 14 0 05 Mar 2020
ViCE: Visual Counterfactual Explanations for Machine Learning Models Oscar Gomez Steffen Holter Jun Yuan E. Bertini AAML 59 93 0 05 Mar 2020
EXPLAIN-IT: Towards Explainable AI for Unsupervised Network Traffic Analysis Andrea Morichetta P. Casas Marco Mellia 15 55 0 03 Mar 2020
Two Decades of AI4NETS-AI/ML for Data Networks: Challenges & Research Directions P. Casas GNN 19 8 0 03 Mar 2020
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness Ahmadreza Jeddi M. Shafiee Michelle Karg C. Scharfenberger A. Wong OOD AAML 72 63 0 02 Mar 2020
A Study on Multimodal and Interactive Explanations for Visual Question Answering Kamran Alipour J. Schulze Yi Yao Avi Ziskind Giedrius Burachas 32 27 0 01 Mar 2020