Information Bottleneck : Theory and Applications in Deep Learning

• Tác giả: Bernhard Geiger, Gernot Kubin
• Ngôn ngữ: eng
• ISBN-13: 978-3036508023
• Thông tin xuất bản: Basel, Switzerland : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Mô tả vật lý: 1 electronic resource (274 p.)
• Bộ sưu tập: Tài liệu truy cập mở
• ID: 230337
• Chủ đề: 1. Information technology industries

Tóm tắt

 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.