Statistical Mechanics Of Learning

Author: A. Engel
Publisher: Cambridge University Press
ISBN: 9780521774796
Size: 39.85 MB
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Artificial neural networks, learning, statistical mechanics; background material in mathematics and physics; examples and exercises; textbook/reference.

Models Of Neural Networks Iii

Author: Eytan Domany
Publisher: Springer Science & Business Media
ISBN: 1461207231
Size: 40.97 MB
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One of the most challenging and fascinating problems of the theory of neural nets is that of asymptotic behavior, of how a system behaves as time proceeds. This is of particular relevance to many practical applications. Here we focus on association, generalization, and representation. We turn to the last topic first. The introductory chapter, "Global Analysis of Recurrent Neural Net works," by Andreas Herz presents an in-depth analysis of how to construct a Lyapunov function for various types of dynamics and neural coding. It includes a review of the recent work with John Hopfield on integrate-and fire neurons with local interactions. The chapter, "Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns" by Ken Miller, explains how the primary visual cortex may asymptotically gain its specific structure through a self-organization process based on Hebbian learning. His argu ment since has been shown to be rather susceptible to generalization.

Statistical Mechanics Of Phase Transitions

Author: J. M. Yeomans
Publisher: Clarendon Press
ISBN: 0191589705
Size: 48.85 MB
Format: PDF
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The book provides an introduction to the physics which underlies phase transitions and to the theoretical techniques currently at our disposal for understanding them. It will be useful for advanced undergraduates, for post-graduate students undertaking research in related fields, and for established researchers in experimental physics, chemistry, and metallurgy as an exposition of current theoretical understanding. - ;Recent developments have led to a good understanding of universality; why phase transitions in systems as diverse as magnets, fluids, liquid crystals, and superconductors can be brought under the same theoretical umbrella and well described by simple models. This book describes the physics underlying universality and then lays out the theoretical approaches now available for studying phase transitions. Traditional techniques, mean-field theory, series expansions, and the transfer matrix, are described; the Monte Carlo method is covered, and two chapters are devoted to the renormalization group, which led to a break-through in the field. The book will be useful as a textbook for a course in `Phase Transitions', as an introduction for graduate students undertaking research in related fields, and as an overview for scientists in other disciplines who work with phase transitions but who are not aware of the current tools in the armoury of the theoretical physicist. - ;Introduction; Statistical mechanics and thermodynamics; Models; Mean-field theories; The transfer matrix; Series expansions; Monte Carlo simulations; The renormalization group; Implementations of the renormalization group. -

Phase Transitions In Machine Learning

Author: Lorenza Saitta
Publisher: Cambridge University Press
ISBN: 1139496530
Size: 70.44 MB
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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.

Statistical Physics Of Particles

Author: Mehran Kardar
Publisher: Cambridge University Press
ISBN: 1139464876
Size: 39.39 MB
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Statistical physics has its origins in attempts to describe the thermal properties of matter in terms of its constituent particles, and has played a fundamental role in the development of quantum mechanics. Based on lectures taught by Professor Kardar at MIT, this textbook introduces the central concepts and tools of statistical physics. It contains a chapter on probability and related issues such as the central limit theorem and information theory, and covers interacting particles, with an extensive description of the van der Waals equation and its derivation by mean field approximation. It also contains an integrated set of problems, with solutions to selected problems at the end of the book and a complete set of solutions is available to lecturers on a password protected website at www.cambridge.org/9780521873420. A companion volume, Statistical Physics of Fields, discusses non-mean field aspects of scaling and critical phenomena, through the perspective of renormalization group.

Neural Network Modeling

Author: P. S. Neelakanta
Publisher: CRC Press
ISBN: 1351428950
Size: 63.56 MB
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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.

Advanced Statistical Mechanics

Author: Barry M McCoy
Publisher: Oxford University Press
ISBN: 0199556636
Size: 46.77 MB
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McCoy presents the advances made in statistical mechanics over the last 50 years, including mathematical theorems on order and phase transitions, numerical and series computations of phase diagrams and solutions for important solvable models such as Ising and 8 vortex.

Statistical Mechanics Of Nonequilibrium Liquids

Author: Denis J. Evans
Publisher: Elsevier
ISBN: 1483260453
Size: 70.49 MB
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Statistical Mechanics of Nonequilibrium Liquids deals with theoretical rheology. The book discusses nonlinear response of systems and outlines the statistical mechanical theory. In discussing the framework of nonequilibrium statistical mechanics, the book explains the derivation of a nonequilibrium analogue of the Gibbsian basis for equilibrium statistical mechanics. The book reviews the linear irreversible thermodynamics, the Liouville equation, and the Irving-Kirkwood procedure. The text then explains the Green-Kubo relations used in linear transport coefficients, the linear response theory, the isothermal linear response theory, as well as the equivalence of thermostatted linear responses. The book also describes how thermostatted linear mechanical response of many-body systems can be related to equilibrium fluctuations. The text explains the procedure for calculating the linear Navier-Stokes transport coefficients through computer simulation algorithms. The book also discusses the van Kampen objection to linear response theory, the steady-state fluctuations, and the thermodynamics of steady states. The text will prove valuable for researchers in molecular chemistry, scientists, and academicians involved in advanced physics.