Statistical Mechanics of Neural Networks [E-Book] / by Haiping Huang.
This book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks. The book discusses in details important concepts and techniques including the cavity method, the mean-field theory, replica techniques, the Nishimori condition...
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Full text |
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Personal Name(s): | Huang, Haiping, author |
Edition: |
1st edition 2021. |
Imprint: |
Singapore :
Springer,
2021
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Physical Description: |
XVIII, 296 pages 62 illustrations, 40 illustrations in color (online resource) |
Note: |
englisch |
ISBN: |
9789811675706 |
DOI: |
10.1007/978-981-16-7570-6 |
Subject (LOC): |
- Introduction
- Spin glass models and cavity method
- Variational mean-field theory and belief propagation
- Monte Carlo simulation methods
- High-temperature expansion
- Nishimori line
- Random energy model
- Statistical mechanical theory of Hopfield model
- Replica symmetry and replica symmetry breaking
- Statistical mechanics of restricted Boltzmann machine
- Simplest model of unsupervised learning with binary synapses
- Inherent-symmetry breaking in unsupervised learning
- Mean-field theory of Ising Perceptron
- Mean-field model of multi-layered Perceptron
- Mean-field theory of dimension reduction
- Chaos theory of random recurrent neural networks
- Statistical mechanics of random matrices
- Perspectives.