Description: Explainable Deep Learning AI by Jenny Benois-Pineau, Romain Bourqui, Dragutin Petkovic, Georges Quenot Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems. The book overviews XAI and then covers a number of specific technical works and approaches for deep learning, ranging from general XAI methods to specific XAI applications, and finally, with user-oriented evaluation approaches. It also explores the main categories of explainable AI – deep learning, which become the necessary condition in various applications of artificial intelligence.The groups of methods such as back-propagation and perturbation-based methods are explained, and the application to various kinds of data classification are presented. FORMAT Paperback LANGUAGE English CONDITION Brand New Author Biography Jenny Benois-Pineau is a professor of computer science at the University of Bordeaux and head of the "Video Analysis and Indexing" research group of the "Image and Sound" team of LABRI UMR 58000 Université Bordeaux / CNRS / IPB-ENSEIRB. She was deputy scientific director of theme B of the French national research unit CNRS GDR ISIS (2008-2015) and is currently in charge of international relations at the College of Sciences and Technologies of the University of Bordeaux. She obtained her doctorate in Signals and Systems in Moscow and her Habilitation to Direct Research in Computer Science and Image Processing at the University of Nantes in France. Her subjects of interest include image and video analysis and indexing, artificial intelligence methods applied to image recognition. Since 2009 hes been an Associate Professor in the Computer Science Department of the IUT ("Technical School"), University of Bordeaux (Talence), France. He is also deputy director of the BKB ("Bench to Knowledge and Beyond") team of LaBRI. Dragutin Petkovic is Professor in the Computer Science department at San Francisco State University, USA. Senior researcher at CNRS, leader of the MRIM group. Works at the Laboratory of Informatics of Grenoble and Multimedia Information Indexing and Retrieval Group. Table of Contents 1. Introduction2. Explainable Deep Learning: Methods, Concepts and New Developments3. Compact Visualization of DNN Classification Performances for Interpretation and Improvement4. Explaining How Deep Neural Networks Forget by Deep Visualization5. Characterizing a scene recognition model by identifying the effect of input features via semantic- wise attribution6. A Feature Understanding Method for Explanation of Image Classification by Convolutional Neural Networks7. Explainable Deep Learning for decrypting disease signature in Multiple Sclerosis8. Explanation of CNN Image Classifiers with Hiding Parts9. Remove to Improve?10. Explaining CNN classifier using Association Rule Mining Methods on time-series11. A Methodology to compare XAI Explanations on Natural Language Processing12. Improving Malware Detection with Explainable Machine Learning13. AI Explainability. A Bridge between Machine Vision and Natural Language Processing14. Explainable Deep Learning for Multimedia Indexing and Retrieval15. User Tests and Techniques for the Post-Hoc Explainability of Deep Learning Models16. Conclusion Feature Provides an overview of main approaches to Explainable Artificial Intelligence (XAI) in Deep Learning area, including the most popular techniques and their use, concluding with challenges and exciting future directions of XAI Explores the latest developments in general XAI methods for Deep Learning Explains how XAI for Deep Learning is applied to various domains like images, medicine, and natural language processing Provides an overview of how XAI systems are tested and evaluated especially with real users, a critical need in XAI Details ISBN0323960987 Short Title Explainable Deep Learning AI Publisher Elsevier Science & Technology Language English Year 2023 ISBN-10 0323960987 ISBN-13 9780323960984 Format Paperback Subtitle Methods and Challenges Imprint Academic Press Inc Place of Publication Oxford Country of Publication United Kingdom Author Georges Quenot Edited by Georges Quenot Pages 346 Publication Date 2023-02-24 AU Release Date 2023-02-24 NZ Release Date 2023-02-24 UK Release Date 2023-02-24 Alternative 9780323993883 DEWEY 006.31 Audience Tertiary & Higher Education We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:159966326;
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Author: Jenny Benois-Pineau, Romain Bourqui, Dragutin Petkovic
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Book Title: Explainable Deep Learning AI
