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Bayesian Models of Perception and Action
An accessible introduction to constructing and interpreting Bayesian models of perceptual decision-making and action. Many forms of perception and action can be mathematically modeled as probabilistic—or Bayesian—inference, a method used to draw conclusions from uncertain evidence. According to these models, the human mind behaves like a capable data scientist or crime scene investigator when dealing with noisy and ambiguous data. This textbook provides an approachable introduction to constructing and reasoning with probabilistic models of perceptual decision-making and action. Featuring extensive examples and illustrations, Bayesian Models of Perception and Action is the first textbook to teach this widely used computational framework to beginners. Introduces Bayesian models of perception and action, which are central to cognitive science and neuroscience Beginner-friendly pedagogy includes intuitive examples, daily life illustrations, and gradual progression of complex concepts Broad appeal for students across psychology, neuroscience, cognitive science, linguistics, and mathematics Written by leaders in the field of computational approaches to mind and brain
Internet Law
Shortlisted for DSBA Law Book of the Year Award 2020 The law in Ireland regarding causes of action involving the internet is a rapidly growing area of law and litigation. This book examines issues such as privacy, data protection, defamation, data protection, crime, intellectual property and employment, all through the prism of online behaviour. This book examines key pieces of legislation such as the E-Commerce Directive, GDPR, and Defamation Act 2009; forthcoming legislation such as the Digital Content Directive and proposed Irish legislation to combat harmful online content. With Ireland being the European base of many international IT and tech firms such as Google, Facebook, LinkedIn, Am...
Index of Patents Issued from the United States Patent and Trademark Office
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Advances in Neural Information Processing Systems 11
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.
