Neural information processing electronic resource

Neural information processing  electronic resource
Author: Indian Statistical Institute Nikil R. Pal,Nikil R. Pal,Nikola Kasabov,Rajani K. Mudi,Srimanta Pal,Swapan K. Parui
Publsiher: Springer Science & Business Media
Total Pages: 1368
Release: 2004-11-18
Genre: Computers
ISBN: 9783540239314

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Annotation This book constitutes the refereed proceedings of the 11th International Conference on Neural Information Processing, ICONIP 2004, held in Calcutta, India in November 2004. The 186 revised papers presented together with 24 invited contributions were carefully reviewed and selected from 470 submissions. The papers are organized in topical sections on computational neuroscience, complex-valued neural networks, self-organizing maps, evolutionary computation, control systems, cognitive science, adaptive intelligent systems, biometrics, brain-like computing, learning algorithms, novel neural architectures, image processing, pattern recognition, neuroinformatics, fuzzy systems, neuro-fuzzy systems, hybrid systems, feature analysis, independent component analysis, ant colony, neural network hardware, robotics, signal processing, support vector machine, time series prediction, and bioinformatics.

Advances in Neural Information Processing Systems 17

Advances in Neural Information Processing Systems 17
Author: Lawrence K. Saul,Yair Weiss,Léon Bottou
Publsiher: MIT Press
Total Pages: 1710
Release: 2005
Genre: Medical
ISBN: 0262195348

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Papers presented at NIPS, the flagship meeting on neural computation, held in December 2004 in Vancouver.The annual Neural Information Processing Systems (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 twenty-five percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. This volume contains the papers presented at the December, 2004 conference, held in Vancouver.

Advances in Neural Information Processing Systems 10

Advances in Neural Information Processing Systems 10
Author: Michael I. Jordan,Michael J. Kearns,Sara A. Solla
Publsiher: MIT Press
Total Pages: 1089
Release: 1998
Genre: Computers
ISBN: 0262100762

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The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. These proceedings contain all of the papers that were presented.

Advances in Neural Information Processing Systems 15

Advances in Neural Information Processing Systems 15
Author: Suzanna Becker,Sebastian Thrun,Klaus Obermayer
Publsiher: MIT Press
Total Pages: 1738
Release: 2003
Genre: Computers
ISBN: 0262025507

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Proceedings of the 2002 Neural Information Processing Systems Conference. The annual Neural Information Processing (NIPS) meeting is the flagship conference on neural computation. The conference draws a diverse group of attendees--physicists, neuroscientists, mathematicians, statisticians, and computer scientists--and the presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and applications. Only about 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 2002 conference.

Complex valued Neural Networks

Complex valued Neural Networks
Author: Akira Hirose
Publsiher: World Scientific
Total Pages: 363
Release: 2003
Genre: Computers
ISBN: 9789812384645

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In recent years, complex-valued neural networks have widened the scope of application in optoelectronics, imaging, remote sensing, quantum neural devices and systems, spatiotemporal analysis of physiological neural systems, and artificial neural information processing. In this first-ever book on complex-valued neural networks, the most active scientists at the forefront of the field describe theories and applications from various points of view to provide academic and industrial researchers with a comprehensive understanding of the fundamentals, features and prospects of the powerful complex-valued networks.

Advances in Neural Information Processing Systems 11

Advances in Neural Information Processing Systems 11
Author: Michael S. Kearns,Sara A. Solla,David A. Cohn
Publsiher: MIT Press
Total Pages: 1090
Release: 1999
Genre: Computers
ISBN: 0262112450

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The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. It draws preeminent academic researchers from around the world and is widely considered to be a showcase conference for new developments in network algorithms and architectures. The broad range of interdisciplinary research areas represented includes computer science, neuroscience, statistics, physics, cognitive science, and many branches of engineering, including signal processing and control theory. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented.

Advances in Neural Information Processing Systems 12

Advances in Neural Information Processing Systems 12
Author: Sara A. Solla,Klaus-Robert Müller,Todd K. Leen
Publsiher: MIT Press
Total Pages: 1080
Release: 2000
Genre: Computers
ISBN: 0262194503

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The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. It draws preeminent academic researchers from around the world and is widely considered to be a showcase conference for new developments in network algorithms and architectures. The broad range of interdisciplinary research areas represented includes computer science, neuroscience, statistics, physics, cognitive science, and many branches of engineering, including signal processing and control theory. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented.

Theory of Neural Information Processing Systems

Theory of Neural Information Processing Systems
Author: A.C.C. Coolen,R. Kuehn,P. Sollich
Publsiher: OUP Oxford
Total Pages: 592
Release: 2005-07-21
Genre: Neural networks (Computer science)
ISBN: 0191583006

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Theory of Neural Information Processing Systems provides an explicit, coherent, and up-to-date account of the modern theory of neural information processing systems. It has been carefully developed for graduate students from any quantitative discipline, including mathematics, computer science, physics, engineering or biology, and has been thoroughly class-tested by the authors over a period of some 8 years. Exercises are presented throughout the text and notes on historical background and further reading guide the student into the literature. All mathematical details are included and appendices provide further background material, including probability theory, linear algebra and stochastic processes, making this textbook accessible to a wide audience.

Toward Brain computer Interfacing

Toward Brain computer Interfacing
Author: Guido Dornhege
Publsiher: MIT Press
Total Pages: 507
Release: 2007
Genre: Computers
ISBN: 9780262042444

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The latest research in the development of technologies that will allow humans tocommunicate, using brain signals only, with computers, wheelchairs, prostheses, and otherdevices.

Advances in Neural Information Processing Systems 19

Advances in Neural Information Processing Systems 19
Author: Bernhard Schölkopf,John Platt,Thomas Hofmann
Publsiher: MIT Press
Total Pages: 1643
Release: 2007
Genre: Computers
ISBN: 9780262195683

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The annual conference on NIPS is the flagship conference on neural computation. It draws top academic researchers from around the world & is considered to be a showcase conference for new developments in network algorithms & architectures. This volume contains all of the papers presented at NIPS 2006.

Neural Information Processing and VLSI

Neural Information Processing and VLSI
Author: Bing J. Sheu,Joongho Choi
Publsiher: Springer
Total Pages: 559
Release: 1995-02-28
Genre: Computers
ISBN: UOM:39015034435522

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Neural Information Processing and VLSI provides a unified treatment of this important subject for use in classrooms, industry, and research laboratories, in order to develop advanced artificial and biologically-inspired neural networks using compact analog and digital VLSI parallel processing techniques. Neural Information Processing and VLSI systematically presents various neural network paradigms, computing architectures, and the associated electronic/optical implementations using efficient VLSI design methodologies. Conventional digital machines cannot perform computationally-intensive tasks with satisfactory performance in such areas as intelligent perception, including visual and auditory signal processing, recognition, understanding, and logical reasoning (where the human being and even a small living animal can do a superb job). Recent research advances in artificial and biological neural networks have established an important foundation for high-performance information processing with more efficient use of computing resources. The secret lies in the design optimization at various levels of computing and communication of intelligent machines. Each neural network system consists of massively paralleled and distributed signal processors with every processor performing very simple operations, thus consuming little power. Large computational capabilities of these systems in the range of some hundred giga to several tera operations per second are derived from collectively parallel processing and efficient data routing, through well-structured interconnection networks. Deep-submicron very large-scale integration (VLSI) technologies can integrate tens of millions of transistors in a single silicon chip for complex signal processing and information manipulation. The book is suitable for those interested in efficient neurocomputing as well as those curious about neural network system applications. It has been especially prepared for use as a text for advanced undergraduate and first year graduate students, and is an excellent reference book for researchers and scientists working in the fields covered.

Predicting Structured Data

Predicting Structured Data
Author: Gökhan BakIr,Neural Information Processing Systems Foundation
Publsiher: MIT Press
Total Pages: 348
Release: 2007
Genre: Computers
ISBN: 9780262026178

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State-of-the-art algorithms and theory in a novel domain of machine learning,prediction when the output has structure.

Neural Information Processing

Neural Information Processing
Author: Tom Gedeon,Kok Wai Wong,Minho Lee
Publsiher: Springer Nature
Total Pages: 782
Release: 2019-12-06
Genre: Computers
ISBN: 9783030368081

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The two-volume set CCIS 1142 and 1143 constitutes thoroughly refereed contributions presented at the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019. For ICONIP 2019 a total of 345 papers was carefully reviewed and selected for publication out of 645 submissions. The 168 papers included in this volume set were organized in topical sections as follows: adversarial networks and learning; convolutional neural networks; deep neural networks; embeddings and feature fusion; human centred computing; human centred computing and medicine; human centred computing for emotion; hybrid models; image processing by neural techniques; learning from incomplete data; model compression and optimization; neural network applications; neural network models; semantic and graph based approaches; social network computing; spiking neuron and related models; text computing using neural techniques; time-series and related models; and unsupervised neural models.

An Introduction to Lifted Probabilistic Inference

An Introduction to Lifted Probabilistic Inference
Author: David Poole
Publsiher: MIT Press
Total Pages: 454
Release: 2021-08-17
Genre: Computers
ISBN: 9780262366182

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Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models. Statistical relational AI (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field. After providing necessary background on probabilistic graphical models, relational probabilistic models, and learning inside these models, the book turns to lifted inference, first covering exact inference and then approximate inference. In addition, the book considers the theory of liftability and acting in relational domains, which allows the connection of learning and reasoning in relational domains.

The Deep Learning Revolution

The Deep Learning Revolution
Author: Terrence J. Sejnowski
Publsiher: MIT Press
Total Pages: 352
Release: 2018-10-23
Genre: Computers
ISBN: 9780262038034

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How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.