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Information Theoretic Perspectives On Generalization And Robustness Of Neural Networks
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Book Synopsis Information-theoretic Perspectives on Generalization and Robustness of Neural Networks by : Adrian Tovar Lopez
Download or read book Information-theoretic Perspectives on Generalization and Robustness of Neural Networks written by Adrian Tovar Lopez and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Information Bottleneck by : Bernhard C. Geiger
Download or read book Information Bottleneck written by Bernhard C. Geiger and published by MDPI. This book was released on 2021-06-15 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Generalization and Robustness in Deep Neural Networks by : Yifei Huang
Download or read book Generalization and Robustness in Deep Neural Networks written by Yifei Huang and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Information Theoretic Neural Computation by : Ryotaro Kamimura
Download or read book Information Theoretic Neural Computation written by Ryotaro Kamimura and published by World Scientific. This book was released on 2002 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: In order to develope 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. a-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.
Author :Mathukumalli Vidyasagar Publisher :Springer Science & Business Media ISBN 13 :9781852333737 Total Pages :520 pages Book Rating :4.3/5 (337 download)
Book Synopsis Learning and Generalisation by : Mathukumalli Vidyasagar
Download or read book Learning and Generalisation written by Mathukumalli Vidyasagar and published by Springer Science & Business Media. This book was released on 2002-09-27 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: How does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.
Book Synopsis Robustness and Generalization in Neural Networks by : Weizhi Zhu
Download or read book Robustness and Generalization in Neural Networks written by Weizhi Zhu and published by . This book was released on 2020 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Informational Complexity of Learning by : Partha Niyogi
Download or read book The Informational Complexity of Learning written by Partha Niyogi and published by Springer. This book was released on 2011-09-26 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Among other topics, The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar brings together two important but very different learning problems within the same analytical framework. The first concerns the problem of learning functional mappings using neural networks, followed by learning natural language grammars in the principles and parameters tradition of Chomsky. These two learning problems are seemingly very different. Neural networks are real-valued, infinite-dimensional, continuous mappings. On the other hand, grammars are boolean-valued, finite-dimensional, discrete (symbolic) mappings. Furthermore the research communities that work in the two areas almost never overlap. The book's objective is to bridge this gap. It uses the formal techniques developed in statistical learning theory and theoretical computer science over the last decade to analyze both kinds of learning problems. By asking the same question - how much information does it take to learn? - of both problems, it highlights their similarities and differences. Specific results include model selection in neural networks, active learning, language learning and evolutionary models of language change. The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar is a very interdisciplinary work. Anyone interested in the interaction of computer science and cognitive science should enjoy the book. Researchers in artificial intelligence, neural networks, linguistics, theoretical computer science, and statistics will find it particularly relevant.
Book Synopsis Deep Learning: Algorithms and Applications by : Witold Pedrycz
Download or read book Deep Learning: Algorithms and Applications written by Witold Pedrycz and published by Springer Nature. This book was released on 2019-10-23 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.
Book Synopsis Information Theory, Probability and Neural Networks by : MacKay
Download or read book Information Theory, Probability and Neural Networks written by MacKay and published by . This book was released on 1998-01 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robustness and Invariance in the Generalization Error of Deep Neural Networks by : Jure Sokolić
Download or read book Robustness and Invariance in the Generalization Error of Deep Neural Networks written by Jure Sokolić and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Game Theory and Machine Learning for Cyber Security by : Charles A. Kamhoua
Download or read book Game Theory and Machine Learning for Cyber Security written by Charles A. Kamhoua and published by John Wiley & Sons. This book was released on 2021-09-08 with total page 546 pages. Available in PDF, EPUB and Kindle. Book excerpt: GAME THEORY AND MACHINE LEARNING FOR CYBER SECURITY Move beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field In Game Theory and Machine Learning for Cyber Security, a team of expert security researchers delivers a collection of central research contributions from both machine learning and game theory applicable to cybersecurity. The distinguished editors have included resources that address open research questions in game theory and machine learning applied to cyber security systems and examine the strengths and limitations of current game theoretic models for cyber security. Readers will explore the vulnerabilities of traditional machine learning algorithms and how they can be mitigated in an adversarial machine learning approach. The book offers a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges. Beginning with an introduction to foundational concepts in game theory, machine learning, cyber security, and cyber deception, the editors provide readers with resources that discuss the latest in hypergames, behavioral game theory, adversarial machine learning, generative adversarial networks, and multi-agent reinforcement learning. Readers will also enjoy: A thorough introduction to game theory for cyber deception, including scalable algorithms for identifying stealthy attackers in a game theoretic framework, honeypot allocation over attack graphs, and behavioral games for cyber deception An exploration of game theory for cyber security, including actionable game-theoretic adversarial intervention detection against advanced persistent threats Practical discussions of adversarial machine learning for cyber security, including adversarial machine learning in 5G security and machine learning-driven fault injection in cyber-physical systems In-depth examinations of generative models for cyber security Perfect for researchers, students, and experts in the fields of computer science and engineering, Game Theory and Machine Learning for Cyber Security is also an indispensable resource for industry professionals, military personnel, researchers, faculty, and students with an interest in cyber security.
Book Synopsis An Information Theoretic Approach to Neural Network Design by : Fernando B. L. Cunha
Download or read book An Information Theoretic Approach to Neural Network Design written by Fernando B. L. Cunha and published by . This book was released on 1996 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Information Theory Applied to Neural Networks by : Qi Yu
Download or read book Information Theory Applied to Neural Networks written by Qi Yu and published by . This book was released on 1993 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis An Information-theoretic Unsupervised Learning Algorithm for Neural Networks by : Samuel Becker
Download or read book An Information-theoretic Unsupervised Learning Algorithm for Neural Networks written by Samuel Becker and published by . This book was released on 1992 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Towards Model Robustness and Generalization Against Adversarial Examples for Deep Neural Networks by : Shufei Zhang
Download or read book Towards Model Robustness and Generalization Against Adversarial Examples for Deep Neural Networks written by Shufei Zhang and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis An Information-theoretic Unsupervised Learning Algorithm for Neural Networks by : Helen Suzanna Becker
Download or read book An Information-theoretic Unsupervised Learning Algorithm for Neural Networks written by Helen Suzanna Becker and published by . This book was released on 1992 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Neuromorphic Intelligence by : Shuangming Yang
Download or read book Neuromorphic Intelligence written by Shuangming Yang and published by Springer Nature. This book was released on with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt:
