Explainable Deep Learning AI
methods and challenges
$331.19
- Paperback
346 pages
- Release Date
24 February 2023
Summary
Demystifying Deep Learning: A Guide to Explainable AI
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 meth…
Book Details
| ISBN-13: | 9780323960984 |
|---|---|
| ISBN-10: | 0323960987 |
| Author: | Jenny Benois-Pineau, Romain Bourqui, Dragutin Petkovic, Georges Quenot |
| Publisher: | Elsevier Science & Technology |
| Imprint: | Academic Press Inc |
| Format: | Paperback |
| Number of Pages: | 346 |
| Release Date: | 24 February 2023 |
| Weight: | 720g |
| Dimensions: | 235mm x 191mm |
About The Author
Jenny Benois-Pineau
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.
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.
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