Statistical Mechanics Of Neural Networks

Author : Haiping Huang
Publisher : Springer Nature
Page : 302 pages
File Size : 51,9 Mb
Release : 2022-01-04
Category : Science
ISBN : 9789811675706

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Statistical Mechanics of Neural Networks by Haiping Huang Pdf

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, variational methods, the dynamical mean-field theory, unsupervised learning, associative memory models, perceptron models, the chaos theory of recurrent neural networks, and eigen-spectrums of neural networks, walking new learners through the theories and must-have skillsets to understand and use neural networks. The book focuses on quantitative frameworks of neural network models where the underlying mechanisms can be precisely isolated by physics of mathematical beauty and theoretical predictions. It is a good reference for students, researchers, and practitioners in the area of neural networks.

Author : P. S. Neelakanta,Dolores DeGroff
Publisher : CRC Press
Page : 194 pages
File Size : 45,7 Mb
Release : 2018-02-06
Category : Technology & Engineering
ISBN : 9781351428958

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Neural Network Modeling by P. S. Neelakanta,Dolores DeGroff Pdf

Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.

Author : Luis Garrido
Publisher : Unknown
Page : 484 pages
File Size : 44,5 Mb
Release : 2014-01-15
Category : Electronic
ISBN : 3662137844

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Statistical Mechanics of Neural Networks by Luis Garrido Pdf

Author : Moritz Helias,David Dahmen
Publisher : Springer Nature
Page : 203 pages
File Size : 42,7 Mb
Release : 2020-08-20
Category : Science
ISBN : 9783030464448

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Statistical Field Theory for Neural Networks by Moritz Helias,David Dahmen Pdf

This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in theoretical neuroscience and also machine learning. They enable a systematic and quantitative understanding of the dynamics in recurrent and stochastic neuronal networks. This book is intended for physicists, mathematicians, and computer scientists and it is designed for self-study by researchers who want to enter the field or as the main text for a one semester course at advanced undergraduate or graduate level. The theoretical concepts presented in this book are systematically developed from the very beginning, which only requires basic knowledge of analysis and linear algebra.

Author : Haiping Huang
Publisher : Unknown
Page : 0 pages
File Size : 47,9 Mb
Release : 2021
Category : Electronic
ISBN : 9811675716

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Statistical Mechanics of Neural Networks by Haiping Huang Pdf

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, variational methods, the dynamical mean-field theory, unsupervised learning, associative memory models, perceptron models, the chaos theory of recurrent neural networks, and eigen-spectrums of neural networks, walking new learners through the theories and must-have skillsets to understand and use neural networks. The book focuses on quantitative frameworks of neural network models where the underlying mechanisms can be precisely isolated by physics of mathematical beauty and theoretical predictions. It is a good reference for students, researchers, and practitioners in the area of neural networks.

Author : P. S. Neelakanta,Dolores DeGroff
Publisher : CRC Press
Page : 259 pages
File Size : 51,5 Mb
Release : 2018-02-06
Category : Technology & Engineering
ISBN : 9781351428965

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Neural Network Modeling by P. S. Neelakanta,Dolores DeGroff Pdf

Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.

Author : A. Engel
Publisher : Cambridge University Press
Page : 346 pages
File Size : 45,8 Mb
Release : 2001-03-29
Category : Computers
ISBN : 0521774799

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Statistical Mechanics of Learning by A. Engel Pdf

Learning is one of the things that humans do naturally, and it has always been a challenge for us to understand the process. Nowadays this challenge has another dimension as we try to build machines that are able to learn and to undertake tasks such as datamining, image processing and pattern recognition. We can formulate a simple framework, artificial neural networks, in which learning from examples may be described and understood. The contribution to this subject made over the last decade by researchers applying the techniques of statistical mechanics is the subject of this book. The authors provide a coherent account of various important concepts and techniques that are currently only found scattered in papers, supplement this with background material in mathematics and physics and include many examples and exercises to make a book that can be used with courses, or for self-teaching, or as a handy reference.

Author : Eytan Domany
Publisher : Springer Science & Business Media
Page : 336 pages
File Size : 46,8 Mb
Release : 1996
Category : Computers
ISBN : 0387943684

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Models of Neural Networks III by Eytan Domany Pdf

Presents a collection of articles by leading researchers in neural networks. This work focuses on data storage and retrieval, and the recognition of handwriting.

Author : Berndt Müller,Joachim Reinhardt,Michael T. Strickland
Publisher : Springer Science & Business Media
Page : 340 pages
File Size : 44,6 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9783642577604

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Neural Networks by Berndt Müller,Joachim Reinhardt,Michael T. Strickland Pdf

Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the "space of interactions" approach to the storage capacity of neural networks. - The final part discusses nine programs with practical demonstrations of neural-network models. The software and source code in C are on a 3 1/2" MS-DOS diskette can be run with Microsoft, Borland, Turbo-C, or compatible compilers.

Author : Akinori Tanaka,Akio Tomiya,Koji Hashimoto
Publisher : Springer Nature
Page : 207 pages
File Size : 53,6 Mb
Release : 2021-03-24
Category : Science
ISBN : 9789813361089

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Deep Learning and Physics by Akinori Tanaka,Akio Tomiya,Koji Hashimoto Pdf

What is deep learning for those who study physics? Is it completely different from physics? Or is it similar? In recent years, machine learning, including deep learning, has begun to be used in various physics studies. Why is that? Is knowing physics useful in machine learning? Conversely, is knowing machine learning useful in physics? This book is devoted to answers of these questions. Starting with basic ideas of physics, neural networks are derived naturally. And you can learn the concepts of deep learning through the words of physics. In fact, the foundation of machine learning can be attributed to physical concepts. Hamiltonians that determine physical systems characterize various machine learning structures. Statistical physics given by Hamiltonians defines machine learning by neural networks. Furthermore, solving inverse problems in physics through machine learning and generalization essentially provides progress and even revolutions in physics. For these reasons, in recent years interdisciplinary research in machine learning and physics has been expanding dramatically. This book is written for anyone who wants to learn, understand, and apply the relationship between deep learning/machine learning and physics. All that is needed to read this book are the basic concepts in physics: energy and Hamiltonians. The concepts of statistical mechanics and the bracket notation of quantum mechanics, which are explained in columns, are used to explain deep learning frameworks. We encourage you to explore this new active field of machine learning and physics, with this book as a map of the continent to be explored.

Author : Lorenza Saitta,Attilio Giordana,Antoine Cornuéjols
Publisher : Cambridge University Press
Page : 401 pages
File Size : 51,8 Mb
Release : 2011-06-16
Category : Computers
ISBN : 9781139496537

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Phase Transitions in Machine Learning by Lorenza Saitta,Attilio Giordana,Antoine Cornuéjols Pdf

Phase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning as well as sequence learning methodologies. In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon, and the extensive experimental investigation that supports its presence. They then turn their attention to the possible implications and explore appropriate methods for tackling them. Weaving together fundamental aspects of computer science, statistical physics and machine learning, the book provides sufficient mathematics and physics background to make the subject intelligible to researchers in AI and other computer science communities. Open research issues are also discussed, suggesting promising directions for future research.

Author : Luis Garrido
Publisher : Springer
Page : 460 pages
File Size : 45,9 Mb
Release : 1990
Category : Neural circuitry
ISBN : UOM:39015019640880

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Statistical Mechanics of Neural Networks by Luis Garrido Pdf

Combined for researchers and graduate students the articles from the Sitges Summer School together form an excellent survey of the applications of neural-network theory to statistical mechanics and computer-science biophysics. Various mathematical models are presented together with their interpretation, especially those to do with collective behaviour, learning and storage capacity, and dynamical stability.

Author : Daniel A. Roberts,Sho Yaida,Boris Hanin
Publisher : Cambridge University Press
Page : 473 pages
File Size : 53,5 Mb
Release : 2022-05-26
Category : Computers
ISBN : 9781316519332

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The Principles of Deep Learning Theory by Daniel A. Roberts,Sho Yaida,Boris Hanin Pdf

This volume develops an effective theory approach to understanding deep neural networks of practical relevance.

Author : Rainer Dietrich
Publisher : Unknown
Page : 150 pages
File Size : 49,8 Mb
Release : 2001
Category : Electronic
ISBN : 3826592794

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Statistical Mechanics of Neural Networks by Rainer Dietrich Pdf

Author : Jong-Hoon Oh,Chulan Kwon,Sungzoon Cho,Sŏul Taehakkyo. Chayŏn Kwahak Taehak. Pusŏl Iron Mullihak Yŏnʼguso,Pʻohang Kongkwa Taehak (Korea). Kichʻo Kwahak Yŏnʼguso
Publisher : World Scientific Publishing Company Incorporated
Page : 330 pages
File Size : 42,9 Mb
Release : 1995
Category : Science
ISBN : 9810223242

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Neural Networks by Jong-Hoon Oh,Chulan Kwon,Sungzoon Cho,Sŏul Taehakkyo. Chayŏn Kwahak Taehak. Pusŏl Iron Mullihak Yŏnʼguso,Pʻohang Kongkwa Taehak (Korea). Kichʻo Kwahak Yŏnʼguso Pdf