"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 / 4,310 papers shown

Title
Explaining the Road Not Taken Hua Shen Ting-Hao 'Kenneth' Huang FAtt XAI 37 9 0 27 Mar 2021
The Shapley Value of coalition of variables provides better explanations Salim I. Amoukou Nicolas Brunel Tangi Salaun FAtt TDI 27 5 0 24 Mar 2021
Exercise? I thought you said Éxtra Fries': Leveraging Sentence Demarcations and Multi-hop Attention for Meme Affect Analysis Shraman Pramanick Md. Shad Akhtar Tanmoy Chakraborty 30 17 0 23 Mar 2021
Efficient sign language recognition system and dataset creation method based on deep learning and image processing Alvaro Leandro Cavalcante Carneiro Lucas de Brito Silva Denis Henrique Pinheiro Salvadeo SLR 11 5 0 22 Mar 2021
Explainability: Relevance based Dynamic Deep Learning Algorithm for Fault Detection and Diagnosis in Chemical Processes P. Agarwal Melih Tamer H. Budman AAML 23 43 0 22 Mar 2021
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals Sainyam Galhotra Romila Pradhan Babak Salimi CML 30 105 0 22 Mar 2021
Hardware Acceleration of Explainable Machine Learning using Tensor Processing Units Zhixin Pan Prabhat Mishra 35 18 0 22 Mar 2021
Data Cleansing for Deep Neural Networks with Storage-efficient Approximation of Influence Functions Kenji Suzuki Yoshiyuki Kobayashi T. Narihira TDI 31 5 0 22 Mar 2021
Interpreting Deep Learning Models with Marginal Attribution by Conditioning on Quantiles M. Merz Ronald Richman A. Tsanakas M. Wüthrich FAtt 17 11 0 22 Mar 2021
Local Interpretations for Explainable Natural Language Processing: A Survey Siwen Luo Hamish Ivison S. Han Josiah Poon MILM 48 48 0 20 Mar 2021
Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles G. Cantareira R. Mello F. Paulovich AAML 29 9 0 18 Mar 2021
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations Pau Rodríguez López Massimo Caccia Alexandre Lacoste L. Zamparo I. Laradji Laurent Charlin David Vazquez AAML 42 55 0 18 Mar 2021
Interpretability of a Deep Learning Model in the Application of Cardiac MRI Segmentation with an ACDC Challenge Dataset Adrianna Janik J. Dodd Georgiana Ifrim Kris Sankaran Kathleen M. Curran 33 26 0 15 Mar 2021
Explainable AI by BAPC -- Before and After correction Parameter Comparison F. Sobieczky Manuela Geiß 16 1 0 12 Mar 2021
Explaining Network Intrusion Detection System Using Explainable AI Framework Shraddha Mane Dattaraj J. Rao AAML 33 69 0 12 Mar 2021
Severity Quantification and Lesion Localization of COVID-19 on CXR using Vision Transformer Gwanghyun Kim Sangjoon Park Y. Oh J. Seo Sang Min Lee Jin Hwan Kim Sungjun Moon Jae-Kwang Lim J. C. Ye ViT MedIm 43 4 0 12 Mar 2021
Interpretable Machine Learning: Moving From Mythos to Diagnostics Valerie Chen Jeffrey Li Joon Sik Kim Gregory Plumb Ameet Talwalkar 32 29 0 10 Mar 2021
Deep Learning for Android Malware Defenses: a Systematic Literature Review Yue Liu Chakkrit Tantithamthavorn Li Li Yepang Liu AAML 35 77 0 09 Mar 2021
Explanations in Autonomous Driving: A Survey Daniel Omeiza Helena Webb Marina Jirotka Lars Kunze 11 214 0 09 Mar 2021
Forest Guided Smoothing I. Verdinelli Larry A. Wasserman 33 3 0 08 Mar 2021
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications Yu-Liang Chou Catarina Moreira P. Bruza Chun Ouyang Joaquim A. Jorge CML 52 176 0 07 Mar 2021
Explanations for Occluded Images Hana Chockler Daniel Kroening Youcheng Sun 30 21 0 05 Mar 2021
Ensembles of Random SHAPs Lev V. Utkin A. Konstantinov FAtt 21 20 0 04 Mar 2021
Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework Cheng Yang Jiawei Liu C. Shi 25 125 0 04 Mar 2021
Evaluating Robustness of Counterfactual Explanations André Artelt Valerie Vaquet Riza Velioglu Fabian Hinder Johannes Brinkrolf M. Schilling Barbara Hammer 14 46 0 03 Mar 2021
Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook Eren Kurshan Hongda Shen GNN 37 32 0 02 Mar 2021
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations N. Jethani Mukund Sudarshan Yindalon Aphinyanagphongs Rajesh Ranganath FAtt 88 69 0 02 Mar 2021
Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook Eren Kurshan Honda Shen Haojie Yu GNN FaML 40 26 0 02 Mar 2021
Interpretable Multi-Modal Hate Speech Detection Prashanth Vijayaraghavan Hugo Larochelle D. Roy 24 35 0 02 Mar 2021
Significance tests of feature relevance for a black-box learner Ben Dai Xiaotong Shen Wei Pan 26 26 0 02 Mar 2021
Contrastive Explanations for Model Interpretability Alon Jacovi Swabha Swayamdipta Shauli Ravfogel Yanai Elazar Yejin Choi Yoav Goldberg 49 95 0 02 Mar 2021
Explainable AI in Credit Risk Management Branka Hadji Misheva Jörg Osterrieder Ali Hirsa O. Kulkarni Stephen Lin 43 64 0 01 Mar 2021
Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning Mark Hamilton Scott M. Lundberg Lei Zhang Stephanie Fu William T. Freeman FAtt 35 10 0 28 Feb 2021
Towards Robust and Reliable Algorithmic Recourse Sohini Upadhyay Shalmali Joshi Himabindu Lakkaraju 25 108 0 26 Feb 2021
If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques Mark T. Keane Eoin M. Kenny Eoin Delaney Barry Smyth CML 32 146 0 26 Feb 2021
Do Input Gradients Highlight Discriminative Features? Harshay Shah Prateek Jain Praneeth Netrapalli AAML FAtt 31 57 0 25 Feb 2021
Model-Based Domain Generalization Alexander Robey George J. Pappas Hamed Hassani OOD 45 129 0 23 Feb 2021
NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation M. Amgad Lamees A. Atteya Hagar Hussein K. Mohammed Ehab Hafiz ... Critical Care David Manthey Atlanta D. Neurology Lurie Cancer Center 47 74 0 18 Feb 2021
On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification Alejandro Barredo Arrieta S. Gil-Lopez I. Laña Miren Nekane Bilbao Javier Del Ser AI4TS 41 13 0 17 Feb 2021
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs Harini Suresh Kathleen M. Lewis John Guttag Arvind Satyanarayan FAtt 45 25 0 17 Feb 2021
What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research Markus Langer Daniel Oster Timo Speith Holger Hermanns Lena Kästner Eva Schmidt Andreas Sesing Kevin Baum XAI 68 415 0 15 Feb 2021
Player-Centered AI for Automatic Game Personalization: Open Problems Jichen Zhu Santiago Ontañón 23 25 0 15 Feb 2021
Connecting Interpretability and Robustness in Decision Trees through Separation Michal Moshkovitz Yao-Yuan Yang Kamalika Chaudhuri 33 22 0 14 Feb 2021
Variable importance scores Wei-Yin Loh Peigen Zhou FAtt 19 29 0 13 Feb 2021
What does LIME really see in images? Damien Garreau Dina Mardaoui FAtt 24 38 0 11 Feb 2021
Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching Scott Cheng-Hsin Yang Wai Keen Vong Ravi B. Sojitra Tomas Folke Patrick Shafto 24 42 0 07 Feb 2021
"Short is the Road that Leads from Fear to Hate": Fear Speech in Indian WhatsApp Groups Punyajoy Saha Binny Mathew Kiran Garimella Animesh Mukherjee 23 50 0 07 Feb 2021
Convolutional Neural Network Interpretability with General Pattern Theory Erico Tjoa Cuntai Guan FAtt AI4CE 18 6 0 05 Feb 2021
EUCA: the End-User-Centered Explainable AI Framework Weina Jin Jianyu Fan D. Gromala Philippe Pasquier Ghassan Hamarneh 42 24 0 04 Feb 2021
Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method Satya M. Muddamsetty M. N. Jahromi Andreea-Emilia Ciontos Laura M. Fenoy T. Moeslund AAML 45 26 0 26 Jan 2021