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Neural Information Processing Systems
Papers comprising this volume were presented at the first IEEE Conference on [title] held in Denver, Co., Nov. 1987. As the limits of the digital computer become apparent, interest in neural networks has intensified. Ninety contributions discuss what neural networks can do, addressing topics that in
Enforcer
A Full Length Novel Worse than bad. Hotter than hot. These are the bad boys of the Harrison Street Crew, and they answer to no one. They take what they want. And what they want is you. Ryan Gallagher is the one who does the dirty work. The brute force in the Harrison Street Crew, he lives to have the club all for his own one day. But the one thing he can’t have is Megan Mahoney—the one woman he’s loved since she was a teenager who needed saving. Megan never forgot the all-consuming passion she felt for Ryan, her larger-than-life, wild teenage love. But coming from a hard, shocking past has left her broken and scared—and untrusting of the Harrison Street Crew. Ten years later, she’s...
Learning on Silicon
Learning on Silicon combines models of adaptive information processing in the brain with advances in microelectronics technology and circuit design. The premise is to construct integrated systems not only loaded with sufficient computational power to handle demanding signal processing tasks in sensory perception and pattern recognition, but also capable of operating autonomously and robustly in unpredictable environments through mechanisms of adaptation and learning. This edited volume covers the spectrum of Learning on Silicon in five parts: adaptive sensory systems, neuromorphic learning, learning architectures, learning dynamics, and learning systems. The 18 chapters are documented with examples of fabricated systems, experimental results from silicon, and integrated applications ranging from adaptive optics to biomedical instrumentation. As the first comprehensive treatment on the subject, Learning on Silicon serves as a reference for beginners and experienced researchers alike. It provides excellent material for an advanced course, and a source of inspiration for continued research towards building intelligent adaptive machines.
Introduction to Computational Science
The essential introduction to computational science—now fully updated and expanded Computational science is an exciting new field at the intersection of the sciences, computer science, and mathematics because much scientific investigation now involves computing as well as theory and experiment. This textbook provides students with a versatile and accessible introduction to the subject. It assumes only a background in high school algebra, enables instructors to follow tailored pathways through the material, and is the only textbook of its kind designed specifically for an introductory course in the computational science and engineering curriculum. While the text itself is generic, an accomp...
Communication Skills That Heal
This book focuses on uncovering and challenging the many myths and fixed images about communication and healing. It hopes to raise awareness, and stimulate, provoke, and offer alternative perspectives that will lead healthcare practitioners to communicate differently with their patients.
Advances in Neural Information Processing Systems 7
November 28-December 1, 1994, Denver, Colorado NIPS is the longest running annual meeting devoted to Neural Information Processing Systems. Drawing on such disparate domains as neuroscience, cognitive science, computer science, statistics, mathematics, engineering, and theoretical physics, the papers collected in the proceedings of NIPS7 reflect the enduring scientific and practical merit of a broad-based, inclusive approach to neural information processing. The primary focus remains the study of a wide variety of learning algorithms and architectures, for both supervised and unsupervised learning. The 139 contributions are divided into eight parts: Cognitive Science, Neuroscience, Learning ...
The Balancing Act
Symbolic and statistical approaches to language have historically been at odds--the former viewed as difficult to test and therefore perhaps impossible to define, and the latter as descriptive but possibly inadequate. At the heart of the debate are fundamental questions concerning the nature of language, the role of data in building a model or theory, and the impact of the competence-performance distinction on the field of computational linguistics. Currently, there is an increasing realization in both camps that the two approaches have something to offer in achieving common goals. The eight contributions in this book explore the inevitable "balancing act" that must take place when symbolic ...
