Information Theoretic Aspects Of Neural Networks
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Information Theoretic Aspects Of Neural Networks
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Author : P. S. Neelakanta
language : en
Publisher: CRC Press
Release Date : 2020-09-23
Information Theoretic Aspects Of Neural Networks written by P. S. Neelakanta and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-23 with Technology & Engineering categories.
Information theoretics vis-a-vis neural networks generally embodies parametric entities and conceptual bases pertinent to memory considerations and information storage, information-theoretic based cost-functions, and neurocybernetics and self-organization. Existing studies only sparsely cover the entropy and/or cybernetic aspects of neural information. Information-Theoretic Aspects of Neural Networks cohesively explores this burgeoning discipline, covering topics such as: Shannon information and information dynamics neural complexity as an information processing system memory and information storage in the interconnected neural web extremum (maximum and minimum) information entropy neural network training non-conventional, statistical distance-measures for neural network optimizations symmetric and asymmetric characteristics of information-theoretic error-metrics algorithmic complexity based representation of neural information-theoretic parameters genetic algorithms versus neural information dynamics of neurocybernetics viewed in the information-theoretic plane nonlinear, information-theoretic transfer function of the neural cellular units statistical mechanics, neural networks, and information theory semiotic framework of neural information processing and neural information flow fuzzy information and neural networks neural dynamics conceived through fuzzy information parameters neural information flow dynamics informatics of neural stochastic resonance Information-Theoretic Aspects of Neural Networks acts as an exceptional resource for engineers, scientists, and computer scientists working in the field of artificial neural networks as well as biologists applying the concepts of communication theory and protocols to the functioning of the brain. The information in this book explores new avenues in the field and creates a common platform for analyzing the neural complex as well as artificial neural networks.
Information Theoretic Aspects Of Neural Networks
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Author : P. S. Neelakanta
language : en
Publisher: CRC Press
Release Date : 1999-03-30
Information Theoretic Aspects Of Neural Networks written by P. S. Neelakanta and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-03-30 with Computers categories.
Information theoretics vis-a-vis neural networks generally embodies parametric entities and conceptual bases pertinent to memory considerations and information storage, information-theoretic based cost-functions, and neurocybernetics and self-organization. Existing studies only sparsely cover the entropy and/or cybernetic aspects of neural information. Information-Theoretic Aspects of Neural Networks cohesively explores this burgeoning discipline, covering topics such as: Shannon information and information dynamics neural complexity as an information processing system memory and information storage in the interconnected neural web extremum (maximum and minimum) information entropy neural network training non-conventional, statistical distance-measures for neural network optimizations symmetric and asymmetric characteristics of information-theoretic error-metrics algorithmic complexity based representation of neural information-theoretic parameters genetic algorithms versus neural information dynamics of neurocybernetics viewed in the information-theoretic plane nonlinear, information-theoretic transfer function of the neural cellular units statistical mechanics, neural networks, and information theory semiotic framework of neural information processing and neural information flow fuzzy information and neural networks neural dynamics conceived through fuzzy information parameters neural information flow dynamics informatics of neural stochastic resonance Information-Theoretic Aspects of Neural Networks acts as an exceptional resource for engineers, scientists, and computer scientists working in the field of artificial neural networks as well as biologists applying the concepts of communication theory and protocols to the functioning of the brain. The information in this book explores new avenues in the field and creates a common platform for analyzing the neural complex as well as artificial neural networks.
An Information Theoretic Approach To Neural Computing
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Author : Gustavo Deco
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
An Information Theoretic Approach To Neural Computing written by Gustavo Deco and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
A detailed formulation of neural networks from the information-theoretic viewpoint. The authors show how this perspective provides new insights into the design theory of neural networks. In particular they demonstrate how these methods may be applied to the topics of supervised and unsupervised learning, including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from varied scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this an extremely valuable introduction to this topic.
Information Theoretic Learning
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Author : Jose C. Principe
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-04-06
Information Theoretic Learning written by Jose C. Principe and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-04-06 with Computers categories.
This book is the first cohesive treatment of ITL algorithms to adapt linear or nonlinear learning machines both in supervised and unsupervised paradigms. It compares the performance of ITL algorithms with the second order counterparts in many applications.
Information Theoretic Neural Computation
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Author : Ryotaro Kamimura
language : en
Publisher: World Scientific
Release Date : 2002-12-19
Information Theoretic Neural Computation written by Ryotaro Kamimura and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-12-19 with Computers categories.
In order to develop new types of information media and technology, it is essential to model complex and flexible information processing in living systems. This book presents a new approach to modeling complex information processing in living systems. Traditional information-theoretic methods in neural networks are unified in one framework, i.e. α-entropy. This new approach will enable information systems such as computers to imitate and simulate human complex behavior and to uncover the deepest secrets of the human mind. Contents: Information in Neural NetworksInformation MinimizationInformation MaximizationConstrained Information MaximizationNeural Feature DetectorsInformation Maximization and MinimizationInformation ControllerInformation Control by α-EntropyIntegrated Information Processing Systems Readership: Students and researchers in artificial intelligence and neural networks. Keywords:
Textbook Of Bioinformatics A Information Theoretic Perspectives Of Bioengineering And Biological Complexes
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Author : Perambur S Neelakanta
language : en
Publisher: World Scientific
Release Date : 2020-08-24
Textbook Of Bioinformatics A Information Theoretic Perspectives Of Bioengineering And Biological Complexes written by Perambur S Neelakanta and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-24 with Science categories.
This book on bioinformatics is designed as an introduction to the conventional details of genomics and proteomics as well as a practical comprehension text with an extended scope on the state-of-the-art bioinformatic details pertinent to next-generation sequencing, translational/clinical bioinformatics and vaccine-design related viral informatics.It includes four major sections: (i) An introduction to bioinformatics with a focus on the fundamentals of information-theory applied to biology/microbiology, with notes on bioinformatic resources, data bases, information networking and tools; (ii) a collection of annotations on the analytics of biomolecular sequences, with pertinent details presented on biomolecular informatics, pairwise and multiple sequences, viral sequence informatics, next-generation sequencing and translational/clinical bioinformatics; (iii) a novel section on cytogenetic and organelle bioinformatics explaining the entropy-theoretics of cellular structures and the underlying informatics of synteny correlations; and (iv) a comprehensive presentation on phylogeny and species informatics.The book is aimed at students, faculty and researchers in biology, health/medical sciences, veterinary/agricultural sciences, bioengineering, biotechnology and genetic engineering. It will be a useful companion for managerial personnel in the biotechnology and bioengineering industries as well as in health/medical science.
Information Theoretic Perspectives On Generalization And Robustness Of Neural Networks
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Author : Adrian Tovar Lopez
language : en
Publisher:
Release Date : 2022
Information Theoretic Perspectives On Generalization And Robustness Of Neural Networks written by Adrian Tovar Lopez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Neural networks as efficient as they are in practice, remain in several aspects still a mystery. Some of the most studied questions are: where does their generalization capabilities come from? What are the reason behind the existence of adversarial examples? In this thesis I use a formal mathematical representation of neural networks to investigate this questions. I also develop new algorithms based on the theory developed. The first par of the thesis is concerned with the generalization error which characterizes the gap between an algorithm's performance on test data versus performance on training data. I derive upper bounds on the generalization error in terms of a certain Wasserstein distance involving the distributions of input and the output under the assumption of a Lipschitz continuous loss function. Unlike mutual information-based bounds, these new bounds are useful for algorithms such as stochastic gradient descent. Moreover, I show that in some natural cases these bounds are tighter than mutual information-based bounds. In the second part of the thesis I study manifold learning. The goal is to learn a manifold that captures the inherent low-dimensionality of high-dimensional data. I present a novel training procedure to learn manifolds using neural networks. Parametrizing the manifold via a neural network with a low-dimensional input and a high-dimensional output. During training, I calculate the distance between the training data points and the manifold via a geometric projection and update the network weights so that this distance diminishes. The learned manifold is seen to interpolate the training data, analogous to autoencoders. Experiments show that the procedure leads to lower reconstruction errors for noisy inputs, and higher adversarial accuracy when used in manifold defense methods than those of autoencoders. In the final part of the thesis I propose an information bottleneck principle for causal time-series prediction. I develop variational bounds on the information bottleneck objective function that can be efficiently optimized using recurrent neural networks. Then implement an algorithm on simulated data as well as real-world weather-prediction and stock market-prediction datasets and show that these problems can be successfully solved using the new information bottleneck principle.
The Principles Of Deep Learning Theory
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Author : Daniel A. Roberts
language : en
Publisher: Cambridge University Press
Release Date : 2022-05-26
The Principles Of Deep Learning Theory written by Daniel A. Roberts and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-26 with Computers categories.
This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
Information Bottleneck
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Author : Bernhard C. Geiger
language : en
Publisher: MDPI
Release Date : 2021-06-15
Information Bottleneck written by Bernhard C. Geiger and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-15 with Technology & Engineering categories.
The celebrated information bottleneck (IB) principle of Tishby et al. has recently enjoyed renewed attention due to its application in the area of deep learning. This collection investigates the IB principle in this new context. The individual chapters in this collection: • provide novel insights into the functional properties of the IB; • discuss the IB principle (and its derivates) as an objective for training multi-layer machine learning structures such as neural networks and decision trees; and • offer a new perspective on neural network learning via the lens of the IB framework. Our collection thus contributes to a better understanding of the IB principle specifically for deep learning and, more generally, of information–theoretic cost functions in machine learning. This paves the way toward explainable artificial intelligence.
Information Theoretic Learning
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Author : Jose C. Principe
language : en
Publisher: Springer
Release Date : 2010-04-15
Information Theoretic Learning written by Jose C. Principe and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-04-15 with Computers categories.
This book is the first cohesive treatment of ITL algorithms to adapt linear or nonlinear learning machines both in supervised and unsupervised paradigms. It compares the performance of ITL algorithms with the second order counterparts in many applications.