Statistical Mechanics of Neural Networks
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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Format: | eBook |
Language: | English |
Published: |
Singapore
Springer Nature Singapore
2021, 2021
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Edition: | 1st ed. 2021 |
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Online Access: | |
Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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