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Renormalization Group Theory Scaling Laws And Deep Learning
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Book Synopsis Renormalization Group Theory, Scaling Laws and Deep Learning by : Parviz Haggi Mani
Download or read book Renormalization Group Theory, Scaling Laws and Deep Learning written by Parviz Haggi Mani and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The question of the possibility of intelligent machines is fundamentally intertwined with the machines' ability to reason. Or not. The developments of the recent years point in a completely different direction : What we need is simple, generic but scalable algorithms that can keep learning on their own. This thesis is an attempt to find theoretical explanations to the findings of recent years where empirical evidence has been presented in support of phase transitions in neural networks, power law behavior of various entities, and even evidence of algorithmic universality, all of which are beautifully explained in the context of statistical physics, quantum field theory and statistical field theory but not necessarily in the context of deep learning where no complete theoretical framework is available. Inspired by these developments, and as it turns out, with the overly ambitious goal of providing a solid theoretical explanation of the empirically observed power laws in neu- ral networks, we set out to substantiate the claims that renormalization group theory may be the sought-after theory of deep learning which may explain the above, as well as what we call algorithmic universality.
Book Synopsis The Principles of Deep Learning Theory by : Daniel A. Roberts
Download or read book The Principles of Deep Learning Theory written by Daniel A. Roberts and published by Cambridge University Press. This book was released on 2022-05-26 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics, Roberts and Yaida provide clear and pedagogical explanations of how realistic deep neural networks actually work. To make results from the theoretical forefront accessible, the authors eschew the subject's traditional emphasis on intimidating formality without sacrificing accuracy. Straightforward and approachable, this volume balances detailed first-principle derivations of novel results with insight and intuition for theorists and practitioners alike. This self-contained textbook is ideal for students and researchers interested in artificial intelligence with minimal prerequisites of linear algebra, calculus, and informal probability theory, and it can easily fill a semester-long course on deep learning theory. For the first time, the exciting practical advances in modern artificial intelligence capabilities can be matched with a set of effective principles, providing a timeless blueprint for theoretical research in deep learning.
Book Synopsis Machine Learning In Pure Mathematics And Theoretical Physics by : Yang-hui He
Download or read book Machine Learning In Pure Mathematics And Theoretical Physics written by Yang-hui He and published by World Scientific. This book was released on 2023-06-21 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: The juxtaposition of 'machine learning' and 'pure mathematics and theoretical physics' may first appear as contradictory in terms. The rigours of proofs and derivations in the latter seem to reside in a different world from the randomness of data and statistics in the former. Yet, an often under-appreciated component of mathematical discovery, typically not presented in a final draft, is experimentation: both with ideas and with mathematical data. Think of the teenage Gauss, who conjectured the Prime Number Theorem by plotting the prime-counting function, many decades before complex analysis was formalized to offer a proof.Can modern technology in part mimic Gauss's intuition? The past five years saw an explosion of activity in using AI to assist the human mind in uncovering new mathematics: finding patterns, accelerating computations, and raising conjectures via the machine learning of pure, noiseless data. The aim of this book, a first of its kind, is to collect research and survey articles from experts in this emerging dialogue between theoretical mathematics and machine learning. It does not dwell on the well-known multitude of mathematical techniques in deep learning, but focuses on the reverse relationship: how machine learning helps with mathematics. Taking a panoramic approach, the topics range from combinatorics to number theory, and from geometry to quantum field theory and string theory. Aimed at PhD students as well as seasoned researchers, each self-contained chapter offers a glimpse of an exciting future of this symbiosis.
Book Synopsis Scaling Laws for Deep Learning by : Jonathan Shmuel Rosenfeld
Download or read book Scaling Laws for Deep Learning written by Jonathan Shmuel Rosenfeld and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Running faster will only get you so far -- it is generally advisable to first understand where the roads lead, then get a car ...
Book Synopsis The Principles of Deep Learning Theory by : Daniel A. Roberts
Download or read book The Principles of Deep Learning Theory written by Daniel A. Roberts and published by Cambridge University Press. This book was released on 2022-05-26 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
Book Synopsis Scaling and Self-Similarity in Physics by : FRÖHLICH
Download or read book Scaling and Self-Similarity in Physics written by FRÖHLICH and published by Birkhäuser. This book was released on 2013-10-24 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Excluded Volume Effects in Polymer Solutions by : Lothar Schäfer
Download or read book Excluded Volume Effects in Polymer Solutions written by Lothar Schäfer and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: Schäfer gives a concise overview of the static equilibrium properties of polymer solutions. In the first part diagrammatic perturbation theory is derived from scratch. The second part illustrates the basic ideas of the renormalization group (RG). The crucial role of dilation invariance is stressed. The more efficient method of dimensional regularization and minimal subtractions is worked out in part three. The fourth part contains a unified evaluation of the theory to the one loop level. All the important experimental quantities are discussed in detail, and the results are compared extensively to experiment. Empirical methods of data analysis are critically discussed. The final (fifth) part is devoted to extensions of theory. The first three parts of this book may serve as the basis of a course. Parts four and five are hoped to be useful for detailed quantitative evaluations of experiments.
Book Synopsis Exact Renormalization Group, The - Proceedings Of The Workshop by : Alexander Krasnitz
Download or read book Exact Renormalization Group, The - Proceedings Of The Workshop written by Alexander Krasnitz and published by World Scientific. This book was released on 1999-08-13 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: The subject of the exact renormalization group started from pioneering work by Wegner and Houghton in the early seventies and, a decade later, by Polchinski, who formulated the Wilson renormalization group for field theory. In the past decade considerable progress has been made in this field, which includes the development of alternative formulations of the approach and of powerful techniques for solving the exact renormalization group equations, as well as widening of the scope of the exact renormalization group method to include fermions and gauge fields. In particular, two very recent results, namely the manifestly gauge-invariant formulation of the exact renormalization group equation and the proof of the c-theorem in four dimensions, are presented in this volume.
Book Synopsis Applied Computational Physics by : Joseph F. Boudreau
Download or read book Applied Computational Physics written by Joseph F. Boudreau and published by Oxford University Press. This book was released on 2018 with total page 936 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook that addresses a wide variety of problems in classical and quantum physics. Modern programming techniques are stressed throughout, along with the important topics of encapsulation, polymorphism, and object-oriented design. Scientific problems are physically motivated, solution strategies are developed, and explicit code is presented.
Book Synopsis Tensor Network Contractions by : Shi-Ju Ran
Download or read book Tensor Network Contractions written by Shi-Ju Ran and published by Springer Nature. This book was released on 2020-01-27 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tensor network is a fundamental mathematical tool with a huge range of applications in physics, such as condensed matter physics, statistic physics, high energy physics, and quantum information sciences. This open access book aims to explain the tensor network contraction approaches in a systematic way, from the basic definitions to the important applications. This book is also useful to those who apply tensor networks in areas beyond physics, such as machine learning and the big-data analysis. Tensor network originates from the numerical renormalization group approach proposed by K. G. Wilson in 1975. Through a rapid development in the last two decades, tensor network has become a powerful numerical tool that can efficiently simulate a wide range of scientific problems, with particular success in quantum many-body physics. Varieties of tensor network algorithms have been proposed for different problems. However, the connections among different algorithms are not well discussed or reviewed. To fill this gap, this book explains the fundamental concepts and basic ideas that connect and/or unify different strategies of the tensor network contraction algorithms. In addition, some of the recent progresses in dealing with tensor decomposition techniques and quantum simulations are also represented in this book to help the readers to better understand tensor network. This open access book is intended for graduated students, but can also be used as a professional book for researchers in the related fields. To understand most of the contents in the book, only basic knowledge of quantum mechanics and linear algebra is required. In order to fully understand some advanced parts, the reader will need to be familiar with notion of condensed matter physics and quantum information, that however are not necessary to understand the main parts of the book. This book is a good source for non-specialists on quantum physics to understand tensor network algorithms and the related mathematics.
Book Synopsis Lectures On Phase Transitions And The Renormalization Group by : Nigel Goldenfeld
Download or read book Lectures On Phase Transitions And The Renormalization Group written by Nigel Goldenfeld and published by CRC Press. This book was released on 2018-03-08 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covering the elementary aspects of the physics of phases transitions and the renormalization group, this popular book is widely used both for core graduate statistical mechanics courses as well as for more specialized courses. Emphasizing understanding and clarity rather than technical manipulation, these lectures de-mystify the subject and show precisely "how things work." Goldenfeld keeps in mind a reader who wants to understand why things are done, what the results are, and what in principle can go wrong. The book reaches both experimentalists and theorists, students and even active researchers, and assumes only a prior knowledge of statistical mechanics at the introductory graduate level.Advanced, never-before-printed topics on the applications of renormalization group far from equilibrium and to partial differential equations add to the uniqueness of this book.
Book Synopsis New Learning Paradigms in Soft Computing by : Lakhmi C. Jain
Download or read book New Learning Paradigms in Soft Computing written by Lakhmi C. Jain and published by Physica. This book was released on 2013-06-05 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning is a key issue in the analysis and design of all kinds of intelligent systems. In recent time many new paradigms of automated (machine) learning have been proposed in the literature. Soft computing, that has proved to be an effective and efficient tool in so many areas of science and technology, seems to offer new qualities in the realm of machine learning too. The purpose of this volume is to present some new learning paradigms that have been triggered, or at least strongly influenced by soft computing tools and techniques, mainly related to neural networks, fuzzy logic, rough sets, and evolutionary computations.
Book Synopsis Spin Glass Theory And Far Beyond: Replica Symmetry Breaking After 40 Years by : Patrick Charbonneau
Download or read book Spin Glass Theory And Far Beyond: Replica Symmetry Breaking After 40 Years written by Patrick Charbonneau and published by World Scientific. This book was released on 2023-07-26 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: About sixty years ago, the anomalous magnetic response of certain magnetic alloys drew the attention of theoretical physicists. It soon became clear that understanding these systems, now called spin glasses, would give rise to a new branch of statistical physics. As physical materials, spin glasses were found to be as useless as they were exotic. They have nevertheless been recognized as paradigmatic examples of complex systems with applications to problems as diverse as neural networks, amorphous solids, biological molecules, social and economic interactions, information theory and constraint satisfaction problems.This book presents an encyclopaedic overview of the broad range of these applications. More than 30 contributions are compiled, written by many of the leading researchers who have contributed to these developments over the last few decades. Some timely and cutting-edge applications are also discussed. This collection serves well as an introduction and summary of disordered and glassy systems for advanced undergraduates, graduate students and practitioners interested in the topic.
Book Synopsis Statistics and Dynamics of Urban Populations by : Marc Barthelemy
Download or read book Statistics and Dynamics of Urban Populations written by Marc Barthelemy and published by Oxford University Press. This book was released on 2024-03-21 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes all aspects of quantitative approaches to urban population growth, ranging from measures and empirical results such as the famous Zipf law, to the mathematical description of their evolution.
Book Synopsis Scaling, Self-similarity, and Intermediate Asymptotics by : G. I. Barenblatt
Download or read book Scaling, Self-similarity, and Intermediate Asymptotics written by G. I. Barenblatt and published by Cambridge University Press. This book was released on 1996-12-12 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scaling laws reveal the fundamental property of phenomena, namely self-similarity - repeating in time and/or space - which substantially simplifies the mathematical modelling of the phenomena themselves. This book begins from a non-traditional exposition of dimensional analysis, physical similarity theory, and general theory of scaling phenomena, using classical examples to demonstrate that the onset of scaling is not until the influence of initial and/or boundary conditions has disappeared but when the system is still far from equilibrium. Numerous examples from a diverse range of fields, including theoretical biology, fracture mechanics, atmospheric and oceanic phenomena, and flame propagation, are presented for which the ideas of scaling, intermediate asymptotics, self-similarity, and renormalisation were of decisive value in modelling.
Book Synopsis Renormalization Group and Effective Field Theory Approaches to Many-Body Systems by : Achim Schwenk
Download or read book Renormalization Group and Effective Field Theory Approaches to Many-Body Systems written by Achim Schwenk and published by Springer. This book was released on 2012-06-25 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: There have been many recent and important developments based on effective field theory and the renormalization group in atomic, condensed matter, nuclear and high-energy physics. These powerful and versatile methods provide novel approaches to study complex and strongly interacting many-body systems in a controlled manner. The six extensive lectures gathered in this volume combine selected introductory and interdisciplinary presentations focused on recent applications of effective field theory and the renormalization group to many-body problems in such diverse fields as BEC, DFT, extreme matter, Fermi-liquid theory and gauge theories. Primarily aimed at graduate students and junior researchers, they offer an opportunity to explore fundamental physics across subfield boundaries at an early stage in their careers.
Book Synopsis An Introduction To Quantum Field Theory by : Michael E. Peskin
Download or read book An Introduction To Quantum Field Theory written by Michael E. Peskin and published by CRC Press. This book was released on 2018-05-04 with total page 866 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Quantum Field Theory is a textbook intended for the graduate physics course covering relativistic quantum mechanics, quantum electrodynamics, and Feynman diagrams. The authors make these subjects accessible through carefully worked examples illustrating the technical aspects of the subject, and intuitive explanations of what is going on behind the mathematics. After presenting the basics of quantum electrodynamics, the authors discuss the theory of renormalization and its relation to statistical mechanics, and introduce the renormalization group. This discussion sets the stage for a discussion of the physical principles that underlie the fundamental interactions of elementary particle physics and their description by gauge field theories.
