Statistical Field Theory For Neural Networks

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Statistical Field Theory for Neural Networks

Moritz Helias,David Dahmen
Statistical Field Theory for Neural Networks Book PDF

Author : Moritz Helias,David Dahmen
Publisher : Springer Nature
Page : 203 pages
File Size : 54,9 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.

Statistical Mechanics of Neural Networks

Haiping Huang
Statistical Mechanics of Neural Networks Book PDF

Author : Haiping Huang
Publisher : Unknown
Page : 0 pages
File Size : 49,5 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.

Neural Network Modeling

P. S. Neelakanta,Dolores DeGroff
Neural Network Modeling Book PDF

Author : P. S. Neelakanta,Dolores DeGroff
Publisher : CRC Press
Page : 259 pages
File Size : 55,5 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.

Statistical Mechanics of Neural Networks

Haiping Huang
Statistical Mechanics of Neural Networks Book PDF

Author : Haiping Huang
Publisher : Springer Nature
Page : 302 pages
File Size : 52,9 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.

Statistical Field Theory

Giorgio Parisi
Statistical Field Theory Book PDF

Author : Giorgio Parisi
Publisher : Westview Press
Page : 366 pages
File Size : 48,9 Mb
Release : 1998-11-26
Category : Science
ISBN : 0738200514

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Statistical Field Theory by Giorgio Parisi Pdf

Specifically written to introduce researchers and advanced students to the modern developments in statistical mechanics and field theory, this book's leitmotiv is functional integration and its application to different areas of physics. The book acts as both an introduction to and a lucid overview of the major problems in statistical field theory.

The Principles of Deep Learning Theory

Daniel A. Roberts,Sho Yaida,Boris Hanin
The Principles of Deep Learning Theory Book PDF

Author : Daniel A. Roberts,Sho Yaida,Boris Hanin
Publisher : Cambridge University Press
Page : 473 pages
File Size : 44,6 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.

Markov Chain Monte Carlo Methods in Quantum Field Theories

Anosh Joseph
Markov Chain Monte Carlo Methods in Quantum Field Theories Book PDF

Author : Anosh Joseph
Publisher : Springer Nature
Page : 134 pages
File Size : 49,6 Mb
Release : 2020-04-16
Category : Science
ISBN : 9783030460440

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Markov Chain Monte Carlo Methods in Quantum Field Theories by Anosh Joseph Pdf

This primer is a comprehensive collection of analytical and numerical techniques that can be used to extract the non-perturbative physics of quantum field theories. The intriguing connection between Euclidean Quantum Field Theories (QFTs) and statistical mechanics can be used to apply Markov Chain Monte Carlo (MCMC) methods to investigate strongly coupled QFTs. The overwhelming amount of reliable results coming from the field of lattice quantum chromodynamics stands out as an excellent example of MCMC methods in QFTs in action. MCMC methods have revealed the non-perturbative phase structures, symmetry breaking, and bound states of particles in QFTs. The applications also resulted in new outcomes due to cross-fertilization with research areas such as AdS/CFT correspondence in string theory and condensed matter physics. The book is aimed at advanced undergraduate students and graduate students in physics and applied mathematics, and researchers in MCMC simulations and QFTs. At the end of this book the reader will be able to apply the techniques learned to produce more independent and novel research in the field.

Statistics and Neural Networks

Jim W. Kay,D. M. Titterington
Statistics and Neural Networks Book PDF

Author : Jim W. Kay,D. M. Titterington
Publisher : Oxford University Press, USA
Page : 290 pages
File Size : 43,9 Mb
Release : 1999
Category : Computers
ISBN : 0198524226

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Statistics and Neural Networks by Jim W. Kay,D. M. Titterington Pdf

Providing a broad overview of important current developments in the area of neural networks, this book highlights likely future trends.

The Handbook of Brain Theory and Neural Networks

Michael A. Arbib
The Handbook of Brain Theory and Neural Networks Book PDF

Author : Michael A. Arbib
Publisher : MIT Press
Page : 1328 pages
File Size : 43,9 Mb
Release : 2003
Category : Neural circuitry
ISBN : 9780262011976

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The Handbook of Brain Theory and Neural Networks by Michael A. Arbib Pdf

This second edition presents the enormous progress made in recent years in the many subfields related to the two great questions : how does the brain work? and, How can we build intelligent machines? This second edition greatly increases the coverage of models of fundamental neurobiology, cognitive neuroscience, and neural network approaches to language. (Midwest).

Neural Networks

Berndt Müller,Joachim Reinhardt,Michael T. Strickland
Neural Networks Book PDF

Author : Berndt Müller,Joachim Reinhardt,Michael T. Strickland
Publisher : Springer Science & Business Media
Page : 340 pages
File Size : 46,7 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.

Graphical Models

Michael Irwin Jordan,Terrence Joseph Sejnowski
Graphical Models Book PDF

Author : Michael Irwin Jordan,Terrence Joseph Sejnowski
Publisher : MIT Press
Page : 450 pages
File Size : 45,7 Mb
Release : 2001
Category : Artificial intelligence
ISBN : 0262600420

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Graphical Models by Michael Irwin Jordan,Terrence Joseph Sejnowski Pdf

This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithm and architectures. The selections range from foundational papers of historical importance to results at the cutting edge of research. Graphical models use graphs to represent and manipulate joint probability distributions. They have their roots in artificial intelligence, statistics, and neural networks. The clean mathematical formalism of the graphical models framework makes it possible to understand a wide variety of network-based approaches to computation, and in particular to understand many neural network algorithms and architectures as instances of a broader probabilistic methodology. It also makes it possible to identify novel features of neural network algorithms and architectures and to extend them to more general graphical models.This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithms and architectures. The selections range from foundational papers of historical importance to results at the cutting edge of research. Contributors H. Attias, C. M. Bishop, B. J. Frey, Z. Ghahramani, D. Heckerman, G. E. Hinton, R. Hofmann, R. A. Jacobs, Michael I. Jordan, H. J. Kappen, A. Krogh, R. Neal, S. K. Riis, F. B. Rodríguez, L. K. Saul, Terrence J. Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss

Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning

Igor V. Tetko,Věra Kůrková,Pavel Karpov,Fabian Theis
Artificial Neural Networks and Machine Learning ICANN 2019 Deep Learning Book PDF

Author : Igor V. Tetko,Věra Kůrková,Pavel Karpov,Fabian Theis
Publisher : Springer Nature
Page : 807 pages
File Size : 41,9 Mb
Release : 2019-09-09
Category : Computers
ISBN : 9783030304843

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Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning by Igor V. Tetko,Věra Kůrková,Pavel Karpov,Fabian Theis Pdf

The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.

Quantum Computing: Physics, Blockchains, And Deep Learning Smart Networks

Melanie Swan,Renato P Dos Santos,Frank Witte
Quantum Computing Physics Blockchains And Deep Learning Smart Networks Book PDF

Author : Melanie Swan,Renato P Dos Santos,Frank Witte
Publisher : World Scientific
Page : 400 pages
File Size : 54,6 Mb
Release : 2020-03-20
Category : Science
ISBN : 9781786348227

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Quantum Computing: Physics, Blockchains, And Deep Learning Smart Networks by Melanie Swan,Renato P Dos Santos,Frank Witte Pdf

Quantum information and contemporary smart network domains are so large and complex as to be beyond the reach of current research approaches. Hence, new theories are needed for their understanding and control. Physics is implicated as smart networks are physical systems comprised of particle-many items interacting and reaching criticality and emergence across volumes of macroscopic and microscopic states. Methods are integrated from statistical physics, information theory, and computer science. Statistical neural field theory and the AdS/CFT correspondence are employed to derive a smart network field theory (SNFT) and a smart network quantum field theory (SNQFT) for the orchestration of smart network systems. Specifically, a smart network field theory (conventional or quantum) is a field theory for the organization of particle-many systems from a characterization, control, criticality, and novelty emergence perspective.This book provides insight as to how quantum information science as a paradigm shift in computing may influence other high-impact digital transformation technologies, such as blockchain and machine learning. Smart networks refer to the idea that the internet is no longer simply a communications network, but rather a computing platform. The trajectory is that of communications networks becoming computing networks (with self-executing code), and perhaps ultimately quantum computing networks. Smart network technologies are conceived as autonomous self-operating computing networks. This includes blockchain economies, deep learning neural networks, autonomous supply chains, self-piloting driving fleets, unmanned aerial vehicles, industrial robotics cloudminds, real-time bidding for advertising, high-frequency trading networks, smart city IoT sensors, and the quantum internet.

Computational Modelling of the Brain

Michele Giugliano,Mario Negrello,Daniele Linaro
Computational Modelling of the Brain Book PDF

Author : Michele Giugliano,Mario Negrello,Daniele Linaro
Publisher : Springer Nature
Page : 361 pages
File Size : 50,6 Mb
Release : 2022-04-26
Category : Medical
ISBN : 9783030894399

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Computational Modelling of the Brain by Michele Giugliano,Mario Negrello,Daniele Linaro Pdf

This volume offers an up-to-date overview of essential concepts and modern approaches to computational modelling, including the use of experimental techniques related to or directly inspired by them. The book introduces, at increasing levels of complexity and with the non-specialist in mind, state-of-the-art topics ranging from single-cell and molecular descriptions to circuits and networks. Four major themes are covered, including subcellular modelling of ion channels and signalling pathways at the molecular level, single-cell modelling at different levels of spatial complexity, network modelling from local microcircuits to large-scale simulations of entire brain areas and practical examples. Each chapter presents a systematic overview of a specific topic and provides the reader with the fundamental tools needed to understand the computational modelling of neural dynamics. This book is aimed at experimenters and graduate students with little or no prior knowledge of modelling who are interested in learning about computational models from the single molecule to the inter-areal communication of brain structures. The book will appeal to computational neuroscientists, engineers, physicists and mathematicians interested in contributing to the field of neuroscience. Chapters 6, 10 and 11 are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Deep Learning and Physics

Akinori Tanaka,Akio Tomiya,Koji Hashimoto
Deep Learning and Physics Book PDF

Author : Akinori Tanaka,Akio Tomiya,Koji Hashimoto
Publisher : Springer Nature
Page : 207 pages
File Size : 49,8 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.