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346 sidor
2023

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.

Författare: Jenny Benois-Pineau, Romain Bourqui, Dragutin Petkovic, Georges Quenot
Format: Pocket/Paperback
ISBN: 9780323960984
Språk: Engelska
Antal sidor: 346
Utgivningsdatum: 2023-02-24
Förlag: Elsevier Science & Technology