Advanced Mean Field Methods

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Advanced Mean Field Methods

Author : Manfred Opper,David Saad
Publisher : MIT Press
Page : 300 pages
File Size : 43,6 Mb
Release : 2001
Category : Computers
ISBN : 0262150549

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Advanced Mean Field Methods by Manfred Opper,David Saad Pdf

This book covers the theoretical foundations of advanced mean field methods, explores the relation between the different approaches, examines the quality of the approximation obtained, and demonstrates their application to various areas of probabilistic modeling. A major problem in modern probabilistic modeling is the huge computational complexity involved in typical calculations with multivariate probability distributions when the number of random variables is large. Because exact computations are infeasible in such cases and Monte Carlo sampling techniques may reach their limits, there is a need for methods that allow for efficient approximate computations. One of the simplest approximations is based on the mean field method, which has a long history in statistical physics. The method is widely used, particularly in the growing field of graphical models. Researchers from disciplines such as statistical physics, computer science, and mathematical statistics are studying ways to improve this and related methods and are exploring novel application areas. Leading approaches include the variational approach, which goes beyond factorizable distributions to achieve systematic improvements; the TAP (Thouless-Anderson-Palmer) approach, which incorporates correlations by including effective reaction terms in the mean field theory; and the more general methods of graphical models. Bringing together ideas and techniques from these diverse disciplines, this book covers the theoretical foundations of advanced mean field methods, explores the relation between the different approaches, examines the quality of the approximation obtained, and demonstrates their application to various areas of probabilistic modeling.

Sublinear Computation Paradigm

Author : Naoki Katoh,Yuya Higashikawa,Hiro Ito,Atsuki Nagao,Tetsuo Shibuya,Adnan Sljoka,Kazuyuki Tanaka,Yushi Uno
Publisher : Springer Nature
Page : 403 pages
File Size : 54,8 Mb
Release : 2021-10-19
Category : Computers
ISBN : 9789811640957

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Sublinear Computation Paradigm by Naoki Katoh,Yuya Higashikawa,Hiro Ito,Atsuki Nagao,Tetsuo Shibuya,Adnan Sljoka,Kazuyuki Tanaka,Yushi Uno Pdf

This open access book gives an overview of cutting-edge work on a new paradigm called the “sublinear computation paradigm,” which was proposed in the large multiyear academic research project “Foundations of Innovative Algorithms for Big Data.” That project ran from October 2014 to March 2020, in Japan. To handle the unprecedented explosion of big data sets in research, industry, and other areas of society, there is an urgent need to develop novel methods and approaches for big data analysis. To meet this need, innovative changes in algorithm theory for big data are being pursued. For example, polynomial-time algorithms have thus far been regarded as “fast,” but if a quadratic-time algorithm is applied to a petabyte-scale or larger big data set, problems are encountered in terms of computational resources or running time. To deal with this critical computational and algorithmic bottleneck, linear, sublinear, and constant time algorithms are required. The sublinear computation paradigm is proposed here in order to support innovation in the big data era. A foundation of innovative algorithms has been created by developing computational procedures, data structures, and modelling techniques for big data. The project is organized into three teams that focus on sublinear algorithms, sublinear data structures, and sublinear modelling. The work has provided high-level academic research results of strong computational and algorithmic interest, which are presented in this book. The book consists of five parts: Part I, which consists of a single chapter on the concept of the sublinear computation paradigm; Parts II, III, and IV review results on sublinear algorithms, sublinear data structures, and sublinear modelling, respectively; Part V presents application results. The information presented here will inspire the researchers who work in the field of modern algorithms.

The Variational Bayes Method in Signal Processing

Author : Václav Šmídl,Anthony Quinn
Publisher : Springer Science & Business Media
Page : 241 pages
File Size : 52,8 Mb
Release : 2006-03-30
Category : Technology & Engineering
ISBN : 9783540288206

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The Variational Bayes Method in Signal Processing by Václav Šmídl,Anthony Quinn Pdf

Treating VB approximation in signal processing, this monograph is for academic and industrial research groups in signal processing, data analysis, machine learning and identification. It reviews distributional approximation, showing that tractable algorithms for parametric model identification can be generated in off-line and on-line contexts.

Proceedings of the Third SIAM International Conference on Data Mining

Author : Daniel Barbara,Chandrika Kamath
Publisher : SIAM
Page : 368 pages
File Size : 44,7 Mb
Release : 2003-01-01
Category : Mathematics
ISBN : 0898715458

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Proceedings of the Third SIAM International Conference on Data Mining by Daniel Barbara,Chandrika Kamath Pdf

The third SIAM International Conference on Data Mining provided an open forum for the presentation, discussion and development of innovative algorithms, software and theories for data mining applications and data intensive computation. This volume includes 21 research papers.

Text Mining

Author : Ashok N. Srivastava,Mehran Sahami
Publisher : CRC Press
Page : 330 pages
File Size : 48,8 Mb
Release : 2009-06-15
Category : Business & Economics
ISBN : 9781420059458

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Text Mining by Ashok N. Srivastava,Mehran Sahami Pdf

The Definitive Resource on Text Mining Theory and Applications from Foremost Researchers in the FieldGiving a broad perspective of the field from numerous vantage points, Text Mining: Classification, Clustering, and Applications focuses on statistical methods for text mining and analysis. It examines methods to automatically cluster and classify te

Probabilistic Models of the Brain

Author : Rajesh P.N. Rao,Bruno A. Olshausen,Michael S. Lewicki
Publisher : MIT Press
Page : 348 pages
File Size : 41,5 Mb
Release : 2002-03-29
Category : Medical
ISBN : 0262264323

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Probabilistic Models of the Brain by Rajesh P.N. Rao,Bruno A. Olshausen,Michael S. Lewicki Pdf

A survey of probabilistic approaches to modeling and understanding brain function. Neurophysiological, neuroanatomical, and brain imaging studies have helped to shed light on how the brain transforms raw sensory information into a form that is useful for goal-directed behavior. A fundamental question that is seldom addressed by these studies, however, is why the brain uses the types of representations it does and what evolutionary advantage, if any, these representations confer. It is difficult to address such questions directly via animal experiments. A promising alternative is to use probabilistic principles such as maximum likelihood and Bayesian inference to derive models of brain function. This book surveys some of the current probabilistic approaches to modeling and understanding brain function. Although most of the examples focus on vision, many of the models and techniques are applicable to other modalities as well. The book presents top-down computational models as well as bottom-up neurally motivated models of brain function. The topics covered include Bayesian and information-theoretic models of perception, probabilistic theories of neural coding and spike timing, computational models of lateral and cortico-cortical feedback connections, and the development of receptive field properties from natural signals.

Energy Minimization Methods in Computer Vision and Pattern Recognition

Author : Mario Figueiredo,Josiane Zerubia,Anil K. Jain
Publisher : Springer
Page : 652 pages
File Size : 54,6 Mb
Release : 2003-06-30
Category : Computers
ISBN : 9783540447450

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Energy Minimization Methods in Computer Vision and Pattern Recognition by Mario Figueiredo,Josiane Zerubia,Anil K. Jain Pdf

This volume consists of the 42 papers presented at the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR2001),whichwasheldatINRIA(InstitutNationaldeRechercheen Informatique et en Automatique) in Sophia Antipolis, France, from September 3 through September 5, 2001. This workshop is the third of a series, which was started with EMMCVPR’97, held in Venice in May 1997, and continued with EMMCVR’99, which took place in York, in July 1999. Minimization problems and optimization methods permeate computer vision (CV), pattern recognition (PR), and many other ?elds of machine intelligence. The aim of the EMMCVPR workshops is to bring together people with research interests in this interdisciplinary topic. Although the subject is traditionally well represented at major international conferences on CV and PR, the EMMCVPR workshops provide a forum where researchers can report their recent work and engage in more informal discussions. We received 70 submissions from 23 countries, which were reviewed by the members of the program committee. Based on the reviews, 24 papers were - cepted for oral presentation and 18 for poster presentation. In this volume, no distinction is made between papers that were presented orally or as posters. The book is organized into ?ve sections, whose topics coincide with the ?ve s- sionsoftheworkshop:“ProbabilisticModelsandEstimation”,“ImageModelling and Synthesis”, “Clustering, Grouping, and Segmentation”, “Optimization and Graphs”, and “Shapes, Curves, Surfaces, and Templates”.

Advances in Neural Information Processing Systems 16

Author : Sebastian Thrun,Lawrence K. Saul,Bernhard Schölkopf
Publisher : MIT Press
Page : 1694 pages
File Size : 42,5 Mb
Release : 2004
Category : Models, Neurological
ISBN : 0262201526

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Advances in Neural Information Processing Systems 16 by Sebastian Thrun,Lawrence K. Saul,Bernhard Schölkopf Pdf

Papers presented at the 2003 Neural Information Processing Conference by leading physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The annual Neural Information Processing (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees -- physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications. Only thirty percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains all the papers presented at the 2003 conference.

The Probability Companion for Engineering and Computer Science

Author : Adam Prügel-Bennett
Publisher : Cambridge University Press
Page : 475 pages
File Size : 44,9 Mb
Release : 2020-01-23
Category : Business & Economics
ISBN : 9781108480536

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The Probability Companion for Engineering and Computer Science by Adam Prügel-Bennett Pdf

Using examples and building intuition, this friendly guide helps readers understand and use probabilistic tools from basic to sophisticated.

Algorithmic Learning Theory

Author : Shai Ben David,John Case,Akira Maruoka
Publisher : Springer
Page : 519 pages
File Size : 55,6 Mb
Release : 2004-09-24
Category : Computers
ISBN : 9783540302155

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Algorithmic Learning Theory by Shai Ben David,John Case,Akira Maruoka Pdf

Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also considerable interaction with the practical, empirical ?elds of machine and statistical learning in which a principal aim is to predict, from past data about phenomena, useful features of future data from the same phenomena. The papers in this volume cover a broad range of topics of current research in the ?eld of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) re?ecting this broad range. The categories featured are Inductive Inf- ence, Approximate Optimization Algorithms, Online Sequence Prediction, S- tistical Analysis of Unlabeled Data, PAC Learning & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. Below we give a brief overview of the ?eld, placing each of these topics in the general context of the ?eld. Formal models of automated learning re?ect various facets of the wide range of activities that can be viewed as learning. A ?rst dichotomy is between viewing learning as an inde?nite process and viewing it as a ?nite activity with a de?ned termination. Inductive Inference models focus on inde?nite learning processes, requiring only eventual success of the learner to converge to a satisfactory conclusion.

Machine Learning and Knowledge Discovery in Databases

Author : Hendrik Blockeel,Kristian Kersting,Siegfried Nijssen,Filip Železný
Publisher : Springer
Page : 732 pages
File Size : 47,7 Mb
Release : 2013-08-28
Category : Computers
ISBN : 9783642409912

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Machine Learning and Knowledge Discovery in Databases by Hendrik Blockeel,Kristian Kersting,Siegfried Nijssen,Filip Železný Pdf

This three-volume set LNAI 8188, 8189 and 8190 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2013, held in Prague, Czech Republic, in September 2013. The 111 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 447 submissions. The papers are organized in topical sections on reinforcement learning; Markov decision processes; active learning and optimization; learning from sequences; time series and spatio-temporal data; data streams; graphs and networks; social network analysis; natural language processing and information extraction; ranking and recommender systems; matrix and tensor analysis; structured output prediction, multi-label and multi-task learning; transfer learning; bayesian learning; graphical models; nearest-neighbor methods; ensembles; statistical learning; semi-supervised learning; unsupervised learning; subgroup discovery, outlier detection and anomaly detection; privacy and security; evaluation; applications; and medical applications.

Statistical Field Theory for Neural Networks

Author : Moritz Helias,David Dahmen
Publisher : Springer Nature
Page : 203 pages
File Size : 45,6 Mb
Release : 2020-08-20
Category : Science
ISBN : 9783030464448

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Statistical Field Theory for Neural Networks by Moritz Helias,David Dahmen Pdf

This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in theoretical neuroscience and also machine learning. They enable a systematic and quantitative understanding of the dynamics in recurrent and stochastic neuronal networks. This book is intended for physicists, mathematicians, and computer scientists and it is designed for self-study by researchers who want to enter the field or as the main text for a one semester course at advanced undergraduate or graduate level. The theoretical concepts presented in this book are systematically developed from the very beginning, which only requires basic knowledge of analysis and linear algebra.

Advanced Wireless Networks

Author : Savo G. Glisic
Publisher : John Wiley & Sons
Page : 864 pages
File Size : 43,7 Mb
Release : 2016-07-22
Category : Technology & Engineering
ISBN : 9781119096870

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Advanced Wireless Networks by Savo G. Glisic Pdf

The third edition of this popular reference covers enabling technologies for building up 5G wireless networks. Due to extensive research and complexity of the incoming solutions for the next generation of wireless networks it is anticipated that the industry will select a subset of these results and leave some advanced technologies to be implemented later,. This new edition presents a carefully chosen combination of the candidate network architectures and the required tools for their analysis. Due to the complexity of the technology, the discussion on 5G will be extensive and it will be difficult to reach consensus on the new global standard. The discussion will have to include the vendors, operators, regulators as well as the research and academic community in the field. Having a comprehensive book will help many participants to join actively the discussion and make meaningful contribution to shaping the new standard.

Advanced Structured Prediction

Author : Sebastian Nowozin,Peter V. Gehler,Jeremy Jancsary,Christoph H. Lampert
Publisher : MIT Press
Page : 430 pages
File Size : 53,5 Mb
Release : 2014-12-05
Category : Computers
ISBN : 9780262028370

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Advanced Structured Prediction by Sebastian Nowozin,Peter V. Gehler,Jeremy Jancsary,Christoph H. Lampert Pdf

An overview of recent work in the field of structured prediction, the building of predictive machine learning models for interrelated and dependent outputs. The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning. Contributors Jonas Behr, Yutian Chen, Fernando De La Torre, Justin Domke, Peter V. Gehler, Andrew E. Gelfand, Sébastien Giguère, Amir Globerson, Fred A. Hamprecht, Minh Hoai, Tommi Jaakkola, Jeremy Jancsary, Joseph Keshet, Marius Kloft, Vladimir Kolmogorov, Christoph H. Lampert, François Laviolette, Xinghua Lou, Mario Marchand, André F. T. Martins, Ofer Meshi, Sebastian Nowozin, George Papandreou, Daniel Průša, Gunnar Rätsch, Amélie Rolland, Bogdan Savchynskyy, Stefan Schmidt, Thomas Schoenemann, Gabriele Schweikert, Ben Taskar, Sinisa Todorovic, Max Welling, David Weiss, Thomáš Werner, Alan Yuille, Stanislav Živný

Database Systems for Advanced Applications

Author : Matthias Renz,Cyrus Shahabi,Xiaofang Zhou,Muhammad Aamir Cheema
Publisher : Springer
Page : 549 pages
File Size : 47,6 Mb
Release : 2015-04-08
Category : Computers
ISBN : 9783319181233

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Database Systems for Advanced Applications by Matthias Renz,Cyrus Shahabi,Xiaofang Zhou,Muhammad Aamir Cheema Pdf

This two volume set LNCS 9049 and LNCS 9050 constitutes the refereed proceedings of the 20th International Conference on Database Systems for Advanced Applications, DASFAA 2015, held in Hanoi, Vietnam, in April 2015. The 63 full papers presented were carefully reviewed and selected from a total of 287 submissions. The papers cover the following topics: data mining; data streams and time series; database storage and index; spatio-temporal data; modern computing platform; social networks; information integration and data quality; information retrieval and summarization; security and privacy; outlier and imbalanced data analysis; probabilistic and uncertain data; query processing.