Edité par MIT Press, 2023
ISBN 10 : 0262549948 ISBN 13 : 9780262549943
Vendeur : booksXpress, Bayonne, NJ, Etats-Unis
Soft Cover. Etat : new.
Edité par MIT Press, 2023
ISBN 10 : 0262549948 ISBN 13 : 9780262549943
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Etat : New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book.
Edité par MIT Press, 2023
ISBN 10 : 0262549948 ISBN 13 : 9780262549943
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Etat : New.
Edité par Penguin Random House LLC, 2023
ISBN 10 : 0262549948 ISBN 13 : 9780262549943
Vendeur : PBShop.store US, Wood Dale, IL, Etats-Unis
PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Edité par Penguin Random House LLC, 2023
ISBN 10 : 0262549948 ISBN 13 : 9780262549943
Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Edité par MIT Press, 2023
ISBN 10 : 0262549948 ISBN 13 : 9780262549943
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Etat : As New. Unread book in perfect condition.
Edité par MIT Press, 2023
ISBN 10 : 0262549948 ISBN 13 : 9780262549943
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Etat : New. In.
Edité par MIT Press, 2023
ISBN 10 : 0262549948 ISBN 13 : 9780262549943
Vendeur : Books Unplugged, Amherst, NY, Etats-Unis
Etat : New. Buy with confidence! Book is in new, never-used condition 0.81.
Edité par MIT Press Ltd, 2023
ISBN 10 : 0262549948 ISBN 13 : 9780262549943
Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
Paperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Edité par MIT Press, 2023
ISBN 10 : 0262549948 ISBN 13 : 9780262549943
Vendeur : GreatBookPricesUK, Castle Donington, DERBY, Royaume-Uni
Etat : New.
Edité par Mit Pr, 2023
ISBN 10 : 0262549948 ISBN 13 : 9780262549943
Vendeur : Revaluation Books, Exeter, Royaume-Uni
Paperback. Etat : Brand New. 412 pages. 10.00x8.00x1.03 inches. In Stock.
Edité par MIT Press, 2023
ISBN 10 : 0262549948 ISBN 13 : 9780262549943
Vendeur : GreatBookPricesUK, Castle Donington, DERBY, Royaume-Uni
Etat : As New. Unread book in perfect condition.
Edité par MIT Press, 2023
ISBN 10 : 0262549948 ISBN 13 : 9780262549943
Vendeur : moluna, Greven, Allemagne
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Tamir Hazan is Assistant Professor at Technion, Israel Institute of Technology.George Papandreou is a Research Scientist for Google, Inc.Daniel Tarlow is a Researcher at Microsoft Research Cambridge, UK.A description of perturbation-b.
Edité par MIT Press, 2023
ISBN 10 : 0262549948 ISBN 13 : 9780262549943
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees.In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules with improved theoretical guarantees. This book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art overview.Chapters address recent modeling ideas that have arisen within the perturbations framework, including Perturb & MAP, herding, and the use of neural networks to map generic noise to distribution over highly structured data. They describe new learning procedures for perturbation models, including an improved EM algorithm and a learning algorithm that aims to match moments of model samples to moments of data. They discuss understanding the relation of perturbation models to their traditional counterparts, with one chapter showing that the perturbations viewpoint can lead to new algorithms in the traditional setting. And they consider perturbation-based regularization in neural networks, offering a more complete understanding of dropout and studying perturbations in the context of deep neural networks.