An introduction to the theory of spin glasses and neural networks

by Viktor Dotsenko

Publisher: World Scientific in Singapore, River Edge, NJ

Written in English
Cover of: An introduction to the theory of spin glasses and neural networks | Viktor Dotsenko
Published: Pages: 156 Downloads: 185
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Subjects:

  • Spin glasses -- Mathematical models.,
  • Statistical mechanics.,
  • Neural networks (Computer science)

Edition Notes

Includes bibliographical references (p. 154-156).

Other titlesTheory of spin glasses and neural networks
StatementViktor Dotsenko.
SeriesWorld Scientific lecture notes in physics ;, v. 54
Classifications
LC ClassificationsQC176.8.S68 D68 1994
The Physical Object
Paginationvii, 156 p. :
Number of Pages156
ID Numbers
Open LibraryOL1110175M
ISBN 109810218737
LC Control Number94035624

This book contains a detailed and self-contained presentation of the replica theory of infinite range spin glasses. The authors also explain recent theoretical developments, paying particular attention to new applications in the study of optimization theory and neural Range: $ - $ Spin glasses are disordered magnetic systems that have led to the development of mathematical tools with an array of real-world applications, from airline scheduling to neural networks. Spin Glasses and Complexity offers the most concise, engaging, and accessible introduction to the subject, fully explaining what spin glasses are, why they are. Spin glasses are magnetic materials. Statistical mechanics, a subfield of physics, has been a powerful tool to theoretically analyze various unique properties of spin glasses. This book is one of the first publications in the theoretically analyze various unique properties of spi interdisciplinary field. It provides readers with a sound introduction to the field and to the analytical.   Why does Deep Learning work? This is the big question on everyone's mind these days. C'mon we all know the answer already: "the long-term behavior of certain neural network models are governed by the statistical mechanism of infinite-range Ising spin-glass Hamiltonians" [1] In other words, Multilayer Neural Networks are just Spin Glasses?

Models, Algorithms and Applications. Author: Stephen W. Ellacott,John C. Mason,Iain J. Anderson; Publisher: Springer Science & Business Media ISBN: Category: Computers Page: View: DOWNLOAD NOW» This volume of research papers comprises the proceedings of the first International Conference on Mathematics of Neural Networks and Applications (MANNA), which was .   A number of new analytical techniques have been developed to establish a theory of spin glasses. Surprisingly, these techniques have turned out to offer new tools and viewpoints for the understanding of information processing problems, including neural networks. A number of new analytical techniques have been developed to establish a theory of spin glasses. Surprisingly, these techniques have offered new tools and viewpoints for the understanding of information processing problems, including neural networks, error-correcting codes, image restoration, and optimization : Hidetoshi Nishimori. This book aims to describe in simple terms the new area of statistical mechanics known as spin-glasses, encompassing systems in which quenched disorder is the dominant factor. «Back to An Introduction to the Theory of Spin Glasses and Neural Networks. Find in a Library Find An Introduction to the Theory of Spin Glasses and Neural Networks.

  Spin glasses are disordered magnetic systems that have led to the development of mathematical tools with an array of real-world applications, from airline scheduling to neural networks. A number of new analytical techniques have been developed to establish a theory of spin glasses. Surprisingly, these techniques have turned out to offer new tools and viewpoints for the understanding of information processing problems, including neural networks, error-correcting codes, image restoration, and optimization : Hidetoshi Nishimori. CONTENTS 1 Mean-field theory of phase transitions 1 Ising model 1 Order parameter and phase transition 3 Mean-field theory 4 Mean-field Hamiltonian 4 Eq. mathematics of neural networks Download mathematics of neural networks or read online books in PDF, EPUB, Tuebl, and Mobi Format. Click Download or Read Online button to get mathematics of neural networks book now. This site is like a library, Use search box .

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This book aims to describe in simple terms the new area of statistical mechanics known as spin-glasses, encompassing systems in which quenched disorder is the dominant factor. The book begins with a non-mathematical explanation of the problem, and the modern understanding of the physics of the spin-glass state is formulated in general by:   In the last four Chapters we will consider just in brief a newborn “branch” of the spin-glass theory which is called the statistical models of neural networks.

The reader interested in details should consult to the review [24] or to the book [25] and references therein. An Introduction to the Theory of Spin Glasses and Neural Networks (World Scientific Lecture Notes in Physics) by Dotsenko, V and a great selection of related books, art and collectibles available now at This book aims to describe in simple terms the new area of statistical mechanics known as spin-glasses, encompassing systems in which quenched disorder is the dominant factor.

The book begins with a non-mathematical explanation of the problem, and the modern understanding of the physics of the spin-glass state is formulated in general terms.

Cite this article. Bryngelson, J.D. An introduction to the theory of spin glasses and neural networks. J Stat P – (). Author: Joseph D. Bryngelson. Introduction to the Replica Theory of Disordered Statistical Systems Viktor Dotsenko This book describes the statistical mechanics of classical spin systems with quenched disorder.

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An Introduction to the Theory of Spin Glasses and Neural Networks. An Introduction to the Theory of Spin Glasses and Neural Networks. Metrics. Downloaded 13 times. The relevant concepts are all explained and it recquires no previous knowledge in the area from the reader.

It is a perfect introduction to the theory of neural networks for those who are iniciating in the field and have no experience with s: 1. A number of new analytical techniques have been developed to establish a theory of spin glasses.

Surprisingly, these techniques have offered new tools and viewpoints for the understanding of information processing problems, including neural networks, error-correcting codes, image restoration, and optimization problems.

However the book is essential for a solid understanding of the computational potential of neural networks. Introduction to the Theory of Neural Computation assumes that the reader is familiar with undergraduate level mathematics, but does not have any background in physics.

All of the necessary tools are introduced in the by: Abstract. The mean-field theory of spin glass models has been used as a prototype of systems with frustration and disorder. One of the most interesting related systems are models of associative memories.

In these lectures we review the main concepts developed to solve the Sherrington-Kirkpatrick model and its application to neural : Nestor Parga. An Introduction to Neural Network Modeling of the Hippocampus and Learning.

Author: Mark A. Gluck,Catherine E. Myers; Publisher: MIT Press ISBN: Category: Medical Page: View: DOWNLOAD NOW» This book is for students and researchers who have a specific interest in learning and memory and want to understand how computational models can be integrated into.

Neural Networks The concepts of neural-network models and techniques of parallel distributed processing are comprehensively presented in a three-step approach: After a brief overview of the neural structure of the brain and the history of neural-network modeling, the reader is introduced to associative memory, preceptrons, Brand: Springer-Verlag Berlin Heidelberg.

Spin-glass models of neural networks. Two dynamical models, proposed by Hopfield and Little to account for the collective behavior of neural networks, are analyzed. The long-time behavior of these models is governed by the statistical mechanics of infinite-range Ising spin-glass Hamiltonians.

flip-flops- are particular spin-glasses and the latter are indeed combinations of ferromagnets and antiferromag-nets), hoping that such a bridge plays as a concrete prescription to capture the beauty of robotics from the statistical mechanical perspective. 1 Introduction Neural networks are such a fascinating field of sci-Cited by: 4.

This book contains a detailed and self-contained presentation of the replica theory of infinite range spin glasses. The authors also explain recent theoretical developments, paying particular attention to new applications in the study of optimization theory and neural : $ Disordered Materials An Introduction.

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A substantial part of early theoretical work on spin glasses dealt with a form of mean field theory based on a set of replicas of the partition function of the system. An important, exactly solvable model of a spin glass was introduced by David Sherrington and Scott Kirkpatrick in An Introduction to the Theory of Spin Glasses and Neural Networks World Scientific Lecture Notes In Physics This book aims to describe in simple terms the new area of statistical mechanics known as spin-glasses, encompassing systems in which quenched disorder is the dominant factor.

The book begins with a non-mathematical explanation of the. Neural Networks The concepts of neural-network models and techniques of parallel distributed processing are comprehensively presented in a three-step approach: After a brief overview of the neural structure of the brain and the history of neural-network modeling, the reader is introduced to associative memory, preceptrons.

An introduction to the theory of spin glasses and neural networks. [Viktor Dotsenko] -- This book aims to describe in simple terms the new area of statistical mechanics known as spin-glasses, encompassing systems in which quenched disorder is the dominant factor. focusing on approaches from statistical physics and the theory of spin glasses.

For example, Amit et al. () analyze the long-time behavior of certain dynamical models of neural networks in terms of an Ising spin-glass Hamiltonian, and Gardner & Derrida () examine the storage capacity ofCited by: Artificial Neural Networks: An Introduction to ANN Theory and Practice P.

Braspenning (auth.), P. Braspenning, F. Thuijsman, A. Weijters (eds.) This book presents carefully revised versions of tutorial lectures given during a School on Artificial Neural Networks for the industrial world held at the University of Limburg in.

Spin glasses are disordered magnetic systems that have led to the development of mathematical tools with an array of real-world applications, from airline scheduling to neural networks.

Spin Glasses and Complexity offers the most concise, engaging, and accessible introduction to the subject, fully explaining what spin glasses are, why they are important, and how they are opening up new ways of thinking.

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Abstract. In previous manuals you have learned that order can grow out of disorder and randomness. You may have noticed a remarkable property of disordered systems—the larger number of elements that comprise a disordered system or that take part in disordered dynamics, the more dependable are laws that govern the “new order.”.

An Introduction to Neural Networks falls into a new ecological niche for texts. "An Introduction to Computer Networks" is a comprehensive text book which is focused and designed to elaborate the technical contents in the light of TCP/IP Introduction To The Theory Of Spin Glasses And Neural Networks, An av Viktor Dotsenko.

A new theory of the class of dilute magnetic alloys, called the spin glasses, is proposed which offers a simple explanation of the cusp found experimentally in the susceptibility.

Abstract: In these notes the main theoretical concepts and techniques in the field of mean-field spin-glasses are reviewed in a compact and pedagogical way, for the benefit of the graduate and undergraduate student. One particular spin-glass model is analyzed (the p-spin spherical model) by using three different approaches.

Thermodynamics, covering pure states, overlaps, overlap Cited by:. An introduction to neural computing. Author links open overlay panel Teuvo high-density semiconductor networks, and eventually new materials like the "spin glasses" offer an unforeseen capacity for computation.

if we talk Introduction to Nettral Computing 15 of the theory of "neural networks," at least then the essence of the mental Cited by:   An introduction to the theory of spin glasses and neural networks.

V. Dotsenko, World Scientific, Singapore, Author: Joseph D. Bryngelson.Mean-field theory for a spin-glass model of neural networks: TAP free energy and the paramagnetic to spin-glass transition.

K Nakanishi dag and H Takayama ddag. Published 7 December • Journal of Physics A: Mathematical and General, Vol Number