Information Bottleneck: Theory and Applications in Deep Learning - Rilegato

Rilegato

ISBN 10: 3036508023 ISBN 13: 9783036508023

Casa editrice: MDPI AG, 2021

Riassunto

The celebrated information bottleneck (IB) principle of Tishby et al. has recently enjoyed renewed attention due to its application in the area of deep learning. This collection investigates the IB principle in this new context. The individual chapters in this collection: • provide novel insights into the functional properties of the IB; • discuss the IB principle (and its derivates) as an objective for training multi-layer machine learning structures such as neural networks and decision trees; and • offer a new perspective on neural network learning via the lens of the IB framework. Our collection thus contributes to a better understanding of the IB principle specifically for deep learning and, more generally, of information-theoretic cost functions in machine learning. This paves the way toward explainable artificial intelligence.

Dettagli dell'edizione

• Editore: MDPI AG
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 3036508023
• ISBN 13: 9783036508023
• Rilegatura: Copertina rigida
• Numero di pagine: 274
• Redattore: Geiger Bernhard C, Kubin Gernot
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile