Stochastic Optimization Methods For Modern Machine Learning Problems
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Stochastic Optimization Methods For Modern Machine Learning Problems
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Author : Yuejiao Sun
language : en
Publisher:
Release Date : 2021
Stochastic Optimization Methods For Modern Machine Learning Problems written by Yuejiao Sun and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Optimization has been the workhorse of solving machine learning problems. However, the efficiency of these methods remains far from satisfaction to meet the ever-growing demand that arises in modern applications. In this context, the present dissertation will focus on two fundamental classes of machine learning problems: 1) stochastic nested problems, where one subproblem builds upon the solution of others; and, 2) stochastic distributed problems, where the subproblems are coupled through sharing the common variables. One key difficulty of solving stochastic nested problems is that the hierarchically coupled structure makes the computation of (stochastic) gradients, the basic element in first-order optimization machinery, prohibitively expensive or even impossible.We will develop the first stochastic optimization method, which runs in a single-loop manner and achieves the same sample complexity as the stochastic gradient descent method for non-nested problems. One key difficulty of solving stochastic distributed problems is the resource intensity, especially when algorithms are running atresource-limited devices. In this context, we will introduce a class of communication-adaptive stochastic gradient descent (SGD) methods, which adaptively reuse the stale gradients, thus saving communication. We will show that the new algorithms have convergence rates comparable to original SGD and Adam algorithms, but enjoy impressive empirical performance in terms of total communication round reduction.
Stochastic Modeling And Optimization Methods For Critical Infrastructure Protection Volume 1
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Author : Alexei A. Gaivoronski
language : en
Publisher: John Wiley & Sons
Release Date : 2025-05-13
Stochastic Modeling And Optimization Methods For Critical Infrastructure Protection Volume 1 written by Alexei A. Gaivoronski and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-13 with Business & Economics categories.
Stochastic Modeling and Optimization Methods for Critical Infrastructure Protection is a thorough exploration of mathematical models and tools that are designed to strengthen critical infrastructures against threats – both natural and adversarial. Divided into two volumes, this first volume examines stochastic modeling across key economic sectors and their interconnections, while the second volume focuses on advanced mathematical methods for enhancing infrastructure protection. The book covers a range of themes, including risk assessment techniques that account for systemic interdependencies within modern technospheres, the dynamics of uncertainty, instability and system vulnerabilities. The book also presents other topics such as cryptographic information protection and Shannon’s theory of secret systems, alongside solutions arising from optimization, game theory and machine learning approaches. Featuring research from international collaborations, this book covers both theory and applications, offering vital insights for advanced risk management curricula. It is intended not only for researchers, but also educators and professionals in infrastructure protection and stochastic optimization.
First Order And Stochastic Optimization Methods For Machine Learning
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Author : Guanghui Lan
language : en
Publisher: Springer Nature
Release Date : 2020-05-15
First Order And Stochastic Optimization Methods For Machine Learning written by Guanghui Lan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-15 with Mathematics categories.
This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.
Stochastic Modeling And Optimization Methods For Critical Infrastructure Protection Volume 2
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Author : Alexei A. Gaivoronski
language : en
Publisher: John Wiley & Sons
Release Date : 2025-05-13
Stochastic Modeling And Optimization Methods For Critical Infrastructure Protection Volume 2 written by Alexei A. Gaivoronski and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-13 with Business & Economics categories.
Stochastic Modeling and Optimization Methods for Critical Infrastructure Protection is a thorough exploration of mathematical models and tools that are designed to strengthen critical infrastructures against threats – both natural and adversarial. Divided into two volumes, this first volume examines stochastic modeling across key economic sectors and their interconnections, while the second volume focuses on advanced mathematical methods for enhancing infrastructure protection. The book covers a range of themes, including risk assessment techniques that account for systemic interdependencies within modern technospheres, the dynamics of uncertainty, instability and system vulnerabilities. The book also presents other topics such as cryptographic information protection and Shannon’s theory of secret systems, alongside solutions arising from optimization, game theory and machine learning approaches. Featuring research from international collaborations, this book covers both theory and applications, offering vital insights for advanced risk management curricula. It is intended not only for researchers, but also educators and professionals in infrastructure protection and stochastic optimization.
Ecai 2023
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Author : K. Gal
language : en
Publisher: IOS Press
Release Date : 2023-10-18
Ecai 2023 written by K. Gal and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-18 with Computers categories.
Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrate innovative applications and uses of advanced AI technology. ECAI 2023 received 1896 submissions – a record number – of which 1691 were retained for review, ultimately resulting in an acceptance rate of 23%. The 390 papers included here, cover topics including machine learning, natural language processing, multi agent systems, and vision and knowledge representation and reasoning. PAIS 2023 received 17 submissions, of which 10 were accepted after a rigorous review process. Those 10 papers cover topics ranging from fostering better working environments, behavior modeling and citizen science to large language models and neuro-symbolic applications, and are also included here. Presenting a comprehensive overview of current research and developments in AI, the book will be of interest to all those working in the field.
Optimization And Applications
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Author : Nicholas Olenev
language : en
Publisher: Springer Nature
Release Date : 2023-11-09
Optimization And Applications written by Nicholas Olenev and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-09 with Mathematics categories.
This book constitutes the refereed proceedings of the 14th International Conference on Optimization and Applications, OPTIMA 2023, held in Petrovac, Montenegro, during September 18–22, 2023. The 27 full papers included in this book were carefully reviewed and selected from 68 submissions. They were organized in topical sections as follows: mathematical programming; global optimization; discrete and combinatorial optimization; game theory and mathematical economics; optimization in economics and finance; and applications.
Artificial Neural Networks And Machine Learning Icann 2018
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Author : Věra Kůrková
language : en
Publisher: Springer
Release Date : 2018-10-02
Artificial Neural Networks And Machine Learning Icann 2018 written by Věra Kůrková and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-02 with Computers categories.
This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning.
Hidden Dynamics Of Stochastic Processes
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Author : Pasquale De Marco
language : en
Publisher: Pasquale De Marco
Release Date : 2025-03-09
Hidden Dynamics Of Stochastic Processes written by Pasquale De Marco and has been published by Pasquale De Marco this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-09 with Science categories.
In the realm of uncertainty and change, where randomness and unpredictability reign supreme, lies the captivating world of stochastic processes. This book embarks on an enthralling journey into the hidden dynamics that govern seemingly chaotic systems, unveiling the secrets of randomness and unlocking the mysteries of uncertainty. Unveiling the fundamental concepts of probability, we delve into the language of chance, exploring the mathematical tools that allow us to quantify uncertainty and make sense of seemingly unpredictable events. We unravel the rich tapestry of stochastic processes, from the familiar world of coin flips and dice rolls to the complex dynamics of financial markets and biological systems. With each chapter, we uncover the profound implications of stochastic processes in various fields, from engineering and finance to biology and social sciences. We witness the power of stochastic models in predicting the behavior of complex systems, optimizing decision-making under uncertainty, and simulating the intricate dynamics of real-world phenomena. Throughout our exploration, we encounter a symphony of mathematical melodies, from the elegant simplicity of Poisson processes to the intricate harmonies of stochastic differential equations. We unlock the secrets of stationarity, unravel the mysteries of ergodicity, and traverse the fascinating world of stochastic control and optimization. Join us on this intellectual adventure as we delve into the hidden dynamics of stochastic processes, unveiling the secrets of randomness and harnessing the power of uncertainty to gain a deeper understanding of the world around us. This book is an essential guide for anyone seeking to understand the intricate workings of stochastic processes. With its comprehensive coverage of fundamental concepts, diverse applications, and captivating explanations, it is a must-read for students, researchers, and practitioners across a wide range of disciplines. If you like this book, write a review!
Designing Engineering Structures Using Stochastic Optimization Methods
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Author : Levent Aydin
language : en
Publisher: CRC Press
Release Date : 2020-04-27
Designing Engineering Structures Using Stochastic Optimization Methods written by Levent Aydin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-27 with Science categories.
Among all aspects of engineering, design is the most important step in developing a new product. A systematic approach to managing design issues can only be accomplished by applying mathematical optimization methods. Furthermore, due to the practical issues in engineering problems, there are limitations in using traditional methods. As such, stochastic optimization methods such as differential evolution, simulated annealing, and genetic algorithms are preferable in finding solutions in design optimization problems. This book reviews mechanical engineering design optimization using stochastic methods. It introduces students and design engineers to practical aspects of complicated mathematical optimization procedures, and outlines steps for wide range of selected engineering design problems. It shows how engineering structures are systematically designed. Many new engineering design applications based on stochastic optimization techniques in automotive, energy, military, naval, manufacturing process and fluids-heat transfer, are described in the book. For each design optimization problem described, background is provided for understanding the solutions. There are very few books on optimization that include engineering applications. They cover limited applications, and that too of well-known design problems of advanced and niche nature. Common problems are hardly addressed. Thus, the subject has remained fairly theoretical. To overcome this, each chapter in this book is contributed by at least one academic and one industrial expert researcher.
Optimization Machine Learning And Fuzzy Logic Theory Algorithms And Applications
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Author : Mzili, Toufik
language : en
Publisher: IGI Global
Release Date : 2025-02-20
Optimization Machine Learning And Fuzzy Logic Theory Algorithms And Applications written by Mzili, Toufik and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Computers categories.
Optimization, machine learning, and fuzzy logic are fundamental in the field of computational intelligence, each contributing to solving complex problems across various domains. Optimization techniques focus on finding the best solutions to problems by improving efficiency and minimizing resources. Machine learning enables systems to learn from data, making predictions or decisions without being programmed. Fuzzy logic deals with uncertainty and imprecision, allowing for flexible decision-making processes. Together, these theories, algorithms, and applications solve challenges in fields such as engineering, finance, and healthcare, where traditional methods often fall short. The continued application and exploration of these disciplines may unveil new possibilities for advanced problem-solving and intelligent systems. Optimization, Machine Learning, and Fuzzy Logic: Theory, Algorithms, and Applications explores optimization techniques, fuzzy logic, and their integration with machine learning. It covers fundamental concepts, mathematical foundations, algorithms, and applications, providing a holistic understanding of these domains. This book covers topics such as disease detection, deep learning, and text analysis, and is a useful resource for engineers, data scientists, medical professionals, academicians, and researchers.