Advances in Neural Information Processing Systems 17

Advances in Neural Information Processing Systems 17

Author: Lawrence K. Saul

Publisher: MIT Press

Published: 2005

Total Pages: 1710

ISBN-13: 9780262195348

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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 11

Advances in Neural Information Processing Systems 11

Author: Michael S. Kearns

Publisher: MIT Press

Published: 1999

Total Pages: 1122

ISBN-13: 9780262112451

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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 10

Advances in Neural Information Processing Systems 10

Author: Michael I. Jordan

Publisher: MIT Press

Published: 1998

Total Pages: 1114

ISBN-13: 9780262100762

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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 12

Advances in Neural Information Processing Systems 12

Author: Sara A. Solla

Publisher: MIT Press

Published: 2000

Total Pages: 1124

ISBN-13: 9780262194501

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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

Publisher: OUP Oxford

Published: 2005-07-21

Total Pages: 596

ISBN-13: 9780191583001

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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.


The Deep Learning Revolution

The Deep Learning Revolution

Author: Terrence J. Sejnowski

Publisher: MIT Press

Published: 2018-10-23

Total Pages: 354

ISBN-13: 026203803X

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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.


Handbook on Neural Information Processing

Handbook on Neural Information Processing

Author: Monica Bianchini

Publisher: Springer Science & Business Media

Published: 2013-04-12

Total Pages: 547

ISBN-13: 3642366570

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This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple classifier systems Self organisation and modal learning Applications to content-based image retrieval, text mining in large document collections, and bioinformatics This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.


Advances in Neural Information Processing Systems 19

Advances in Neural Information Processing Systems 19

Author: Bernhard Schölkopf

Publisher: MIT Press

Published: 2007

Total Pages: 1668

ISBN-13: 0262195682

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The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation and machine learning. This volume contains the papers presented at the December 2006 meeting, held in Vancouver.


Predicting Structured Data

Predicting Structured Data

Author: Neural Information Processing Systems Foundation

Publisher: MIT Press

Published: 2007

Total Pages: 361

ISBN-13: 0262026171

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