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Phase Transitions In Machine Learning
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Book Synopsis Phase Transitions in Machine Learning by : Lorenza Saitta
Download or read book Phase Transitions in Machine Learning written by Lorenza Saitta and published by Cambridge University Press. This book was released on 2011-06-16 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Phase Transitions in Machine Learning by : Lorenza Saitta
Download or read book Phase Transitions in Machine Learning written by Lorenza Saitta and published by . This book was released on 2011 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: This state-of-the-art overview of the field describes how phase transitions occur and teaches appropriate methods for tackling the consequent problems.
Book Synopsis Phase Transitions in Machine Learning by : Lorenza Saitta
Download or read book Phase Transitions in Machine Learning written by Lorenza Saitta and published by . This book was released on 2011 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Phase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning and 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"--
Book Synopsis Phase Transitions by : Moshe Gitterman
Download or read book Phase Transitions written by Moshe Gitterman and published by World Scientific Publishing Company. This book was released on 2013-09-25 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive review of the theory of phase transitions and its modern applications, based on the five pillars of the modern theory of phase transitions: the Ising model, mean field, scaling, renormalization group and universality. This expanded second edition includes, along with a description of vortices and high temperature superconductivity, a discussion of phase transitions in chemical reactions and moving systems. The book covers the close connection between phase transitions and small world phenomena as well as scale-free systems such as the stock market and the Internet.
Book Synopsis Elements of Phase Transitions and Critical Phenomena by : Hidetoshi Nishimori
Download or read book Elements of Phase Transitions and Critical Phenomena written by Hidetoshi Nishimori and published by Oxford University Press. This book was released on 2011 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: As an introductory account of the theory of phase transitions and critical phenomena, this book reflects lectures given by the authors to graduate students at their departments and is thus classroom-tested to help beginners enter the field. Most parts are written as self-contained units and every new concept or calculation is explained in detail without assuming prior knowledge of the subject. The book significantly enhances and revises a Japanese version which is a bestseller in the Japanese market and is considered a standard textbook in the field. It contains new pedagogical presentations of field theory methods, including a chapter on conformal field theory, and various modern developments hard to find in a single textbook on phase transitions. Exercises are presented as the topics develop, with solutions found at the end of the book, making the text useful for self-teaching, as well as for classroom learning.
Book Synopsis Deep Learning and Physics by : Akinori Tanaka
Download or read book Deep Learning and Physics written by Akinori Tanaka and published by Springer Nature. This book was released on 2021-03-24 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Brain-Inspired Computing by : Katrin Amunts
Download or read book Brain-Inspired Computing written by Katrin Amunts and published by Springer Nature. This book was released on 2021-07-20 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures.
Book Synopsis Phase Transitions in Materials by : Brent Fultz
Download or read book Phase Transitions in Materials written by Brent Fultz and published by Cambridge University Press. This book was released on 2014-08-14 with total page 589 pages. Available in PDF, EPUB and Kindle. Book excerpt: A clear, concise and rigorous textbook covering phase transitions in the context of advances in electronic structure and statistical mechanics.
Book Synopsis Magnetic Phase Transitions by : M. Ausloos
Download or read book Magnetic Phase Transitions written by M. Ausloos and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present volume contains the courses given at a Summer School on "Magne tic Phase Transitions" held at the Ettore Majorana Centre for Scientific Culture, at Erice (Trapani), Italy in July 1983 under the auspices of the Condensed Matter Division of the European Physical Society in their series on Materials Science and Technology. The student participants came from West Germany, Great Britain, Brazil, Greece, Switzerland, Sweden, Italy, USA and The Netherlands. The lecturers came from various European countries, Israel, USA and Canada. The atmosphere at the meeting was excellent and a good spirit of companion ship developed during two weeks of working together. The spread of interests among the lecturers and students was divers;jfied but balanced. The main lec turing contributions are reported in this volume. They represent up-to-date reviews in a pedagogical style. In addition, informal presentations on cur rent research interests were made which have not been included. The school attempted to summarize the current position on the properties of magnetic phase transitions from several points of view. The range and scope of the oretical techniques, and of particular aspects of materials or phenomena as observed experimentally were very well put forward by the lecturers. The grouping of manuscripts in chapters does not represent, however, the sched ule followed during the school. Contributions on mean-field approximations and renormalization-group methods either for static or dynamic phenomena can be found at various places in the following sections.
Book Synopsis Kinetics of Phase Transitions by : Sanjay Puri
Download or read book Kinetics of Phase Transitions written by Sanjay Puri and published by CRC Press. This book was released on 2009-03-24 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing a comprehensive introduction with the necessary background material to make it accessible for a wide scientific audience, Kinetics of Phase Transitions discusses developments in domain-growth kinetics. This book combines pedagogical chapters from leading experts in this area and focuses on incorporating various experimentally relevant effects—such as disorder, strain fields, and wetting surfaces—into studies of phase ordering dynamics. In addition, it highlights topics garnering recent interest, such as the growth of nanostructures on surfaces. This book also provides a comprehensive overview of numerical techniques, which have proven useful in studying these complex nonlinear problems.
Book Synopsis Phase Transitions in Lattice Gauge Theories by : Manuel Scherzer
Download or read book Phase Transitions in Lattice Gauge Theories written by Manuel Scherzer and published by . This book was released on 2019* with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Machine Learning Meets Quantum Physics by : Kristof T. Schütt
Download or read book Machine Learning Meets Quantum Physics written by Kristof T. Schütt and published by Springer Nature. This book was released on 2020-06-03 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
Book Synopsis The Physics of Phase Transitions by : Pierre Papon
Download or read book The Physics of Phase Transitions written by Pierre Papon and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Physics of Phase Transitions occupies an important place at the crossroads of several fields central to materials sciences. This second edition incorporates new developments in the states of matter physics, in particular in the domain of nanomaterials and atomic Bose-Einstein condensates where progress is accelerating. New information and application examples are included. This work deals with all classes of phase transitions in fluids and solids, containing chapters on evaporation, melting, solidification, magnetic transitions, critical phenomena, superconductivity, and more. End-of-chapter problems and complete answers are included.
Book Synopsis Machine Learning and Knowledge Discovery in Databases by : Wray Buntine
Download or read book Machine Learning and Knowledge Discovery in Databases written by Wray Buntine and published by Springer. This book was released on 2009-08-28 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2009, held in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.
Book Synopsis Markov Chain Monte Carlo Methods in Quantum Field Theories by : Anosh Joseph
Download or read book Markov Chain Monte Carlo Methods in Quantum Field Theories written by Anosh Joseph and published by Springer Nature. This book was released on 2020-04-16 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Machine Learning: ECML 2006 by : Johannes Fürnkranz
Download or read book Machine Learning: ECML 2006 written by Johannes Fürnkranz and published by Springer. This book was released on 2006-09-21 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 17th European Conference on Machine Learning, ECML 2006, held, jointly with PKDD 2006. The book presents 46 revised full papers and 36 revised short papers together with abstracts of 5 invited talks, carefully reviewed and selected from 564 papers submitted. The papers present a wealth of new results in the area and address all current issues in machine learning.
Book Synopsis Machine Learning in Chemistry by : Hugh M Cartwright
Download or read book Machine Learning in Chemistry written by Hugh M Cartwright and published by Royal Society of Chemistry. This book was released on 2020-07-15 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Progress in the application of machine learning (ML) to the physical and life sciences has been rapid. A decade ago, the method was mainly of interest to those in computer science departments, but more recently ML tools have been developed that show significant potential across wide areas of science. There is a growing consensus that ML software, and related areas of artificial intelligence, may, in due course, become as fundamental to scientific research as computers themselves. Yet a perception remains that ML is obscure or esoteric, that only computer scientists can really understand it, and that few meaningful applications in scientific research exist. This book challenges that view. With contributions from leading research groups, it presents in-depth examples to illustrate how ML can be applied to real chemical problems. Through these examples, the reader can both gain a feel for what ML can and cannot (so far) achieve, and also identify characteristics that might make a problem in physical science amenable to a ML approach. This text is a valuable resource for scientists who are intrigued by the power of machine learning and want to learn more about how it can be applied in their own field.
