
Information Bottleneck
Theory and Applications in Deep Learning
MDPI AG (Publisher)
Published on 15. June 2021
Book
Hardback
274 pages
978-3-0365-0802-3 (ISBN)
Description
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.
More details
Language
English
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 250 mm
Width: 175 mm
Thickness: 22 mm
Weight
885 gr
ISBN-13
978-3-0365-0802-3 (9783036508023)
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Schweitzer Classification