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Haiping Huang
Dr. Haiping Huang received his Ph.D. degree in theoretical physics from the Institute of Theoretical Physics, the Chinese Academy of Sciences. He works as an associate professor at the School of Physics, Sun Yat-sen University, China. His research interests include the origin of the computational hardness of the binary perceptron model, the theory of dimension reduction in deep neural networks, and inherent symmetry breaking in unsupervised learning. In 2021, he was awarded Excellent Young Scientists Fund by National Natural Science Foundation of China.
Presents major theoretical tools for the analysis of neural networks
Provides concrete examples for the use of the theories in neural networks
Bridges old tools and frontiers in the theoretical development of neural networks
Chapter 2: Spin Glass Models and Cavity Method
Chapter 3: Variational Mean-Field Theory and Belief Propagation
Chapter 4: Monte-Carlo Simulation Methods
Chapter 5: High-Temperature Expansion Techniques
Chapter 6: Nishimori Model
Chapter 7: Random Energy Model
Chapter 8: Statistical Mechanics of Hopfield Model
Chapter 9: Replica Symmetry and Symmetry Breaking
Chapter 10: Statistical Mechanics of Restricted Boltzmann Machine
Chapter 11: Simplest Model of Unsupervised Learning with Binary Synapses
Chapter 12: Inherent-Symmetry Breaking in Unsupervised Learning
Chapter 13: Mean-Field Theory of Ising Perceptron
Chapter 14: Mean-Field Model of Multi-Layered Perceptron
Chapter 15: Mean-Field Theory of Dimension Reduction in Neural Networks
Chapter 16: Chaos Theory of Random Recurrent Networks
Chapter 17: Statistical Mechanics of Random Matrices
Chapter 18: Perspectives
Erscheinungsjahr: | 2023 |
---|---|
Fachbereich: | Astronomie |
Genre: | Physik |
Rubrik: | Naturwissenschaften & Technik |
Thema: | Lexika |
Medium: | Taschenbuch |
Seiten: | 316 |
Inhalt: |
xviii
296 S. 22 s/w Illustr. 40 farbige Illustr. 30 farbige Tab. 296 p. 62 illus. 40 illus. in color. |
ISBN-13: | 9789811675720 |
ISBN-10: | 9811675724 |
Sprache: | Englisch |
Ausstattung / Beilage: | Paperback |
Einband: | Kartoniert / Broschiert |
Autor: | Huang, Haiping |
Auflage: | 1st ed. 2021 |
Hersteller: |
Springer Singapore
Springer Nature Singapore |
Maße: | 235 x 155 x 18 mm |
Von/Mit: | Haiping Huang |
Erscheinungsdatum: | 06.01.2023 |
Gewicht: | 0,482 kg |
Haiping Huang
Dr. Haiping Huang received his Ph.D. degree in theoretical physics from the Institute of Theoretical Physics, the Chinese Academy of Sciences. He works as an associate professor at the School of Physics, Sun Yat-sen University, China. His research interests include the origin of the computational hardness of the binary perceptron model, the theory of dimension reduction in deep neural networks, and inherent symmetry breaking in unsupervised learning. In 2021, he was awarded Excellent Young Scientists Fund by National Natural Science Foundation of China.
Presents major theoretical tools for the analysis of neural networks
Provides concrete examples for the use of the theories in neural networks
Bridges old tools and frontiers in the theoretical development of neural networks
Chapter 2: Spin Glass Models and Cavity Method
Chapter 3: Variational Mean-Field Theory and Belief Propagation
Chapter 4: Monte-Carlo Simulation Methods
Chapter 5: High-Temperature Expansion Techniques
Chapter 6: Nishimori Model
Chapter 7: Random Energy Model
Chapter 8: Statistical Mechanics of Hopfield Model
Chapter 9: Replica Symmetry and Symmetry Breaking
Chapter 10: Statistical Mechanics of Restricted Boltzmann Machine
Chapter 11: Simplest Model of Unsupervised Learning with Binary Synapses
Chapter 12: Inherent-Symmetry Breaking in Unsupervised Learning
Chapter 13: Mean-Field Theory of Ising Perceptron
Chapter 14: Mean-Field Model of Multi-Layered Perceptron
Chapter 15: Mean-Field Theory of Dimension Reduction in Neural Networks
Chapter 16: Chaos Theory of Random Recurrent Networks
Chapter 17: Statistical Mechanics of Random Matrices
Chapter 18: Perspectives
Erscheinungsjahr: | 2023 |
---|---|
Fachbereich: | Astronomie |
Genre: | Physik |
Rubrik: | Naturwissenschaften & Technik |
Thema: | Lexika |
Medium: | Taschenbuch |
Seiten: | 316 |
Inhalt: |
xviii
296 S. 22 s/w Illustr. 40 farbige Illustr. 30 farbige Tab. 296 p. 62 illus. 40 illus. in color. |
ISBN-13: | 9789811675720 |
ISBN-10: | 9811675724 |
Sprache: | Englisch |
Ausstattung / Beilage: | Paperback |
Einband: | Kartoniert / Broschiert |
Autor: | Huang, Haiping |
Auflage: | 1st ed. 2021 |
Hersteller: |
Springer Singapore
Springer Nature Singapore |
Maße: | 235 x 155 x 18 mm |
Von/Mit: | Haiping Huang |
Erscheinungsdatum: | 06.01.2023 |
Gewicht: | 0,482 kg |