Log-Linear Models, Extensions, and Applications by Aleksandr Aravkin Paperback B

Description: Log-Linear Models, Extensions, and Applications by Aleksandr Aravkin, Anna Choromanska, Li Deng, Georg Heigold, Tony Jebara Advances in training models with log-linear structures, with topics including variable selection, the geometry of neural nets, and applications.Advances in training models with log-linear structures, with topics including variable selection, the geometry of neural nets, and applications.Log-linear models play a key role in modern big data and machine learning applications. From simple binary classification models through partition functions, conditional random fields, and neural nets, log-linear structure is closely related to performance in certain applications and influences fitting techniques used to train models. This volume covers recent advances in training models with log-linear structures, covering the underlying geometry, optimization techniques, and multiple applications. The first chapter shows readers the inner workings of machine learning, providing insights into the geometry of log-linear and neural net models. The other chapters range from introductory material to optimization techniques to involved use cases. The book, which grew out of a NIPS workshop, is suitable for graduate students doing research in machine learning, in particular deep learning, variable selection, and applications to speech recognition. The contributors come from academia and industry, allowing readers to view the field from both perspectives.ContributorsAleksandr Aravkin, Avishy Carmi, Guillermo A. Cecchi, Anna Choromanska, Li Deng, Xinwei Deng, Jean Honorio, Tony Jebara, Huijing Jiang, Dimitri Kanevsky, Brian Kingsbury, Fabrice Lambert, Aurelie C. Lozano, Daniel Moskovich, Yuriy S. Polyakov, Bhuvana Ramabhadran, Irina Rish, Dimitris Samaras, Tara N. Sainath, Hagen Soltau, Serge F. Timashev, Ewout van den Berg FORMAT Paperback CONDITION Brand New Author Biography Aleksandr Aravkin is Assistant Professor of Applied Mathematics at the University of Washington.Anna Choromanska is Assistant Professor at New York Universitys Tandon School of Engineering.Li Deng is Chief Artificial Intelligence Officer of Citadel.Georg Heigold is Research Scientist at Google.Tony Jebara is Associate Professor of Computer Science at Columbia University.Dimitri Kanevsky is Research Scientist at Google.Stephen J. Wright is Professor of Computer Science at the University of Wisconsin-Madison. Details ISBN0262553465 Author Tony Jebara Pages 214 Publisher MIT Press Ltd Series Neural Information Processing series Year 2024 ISBN-13 9780262553469 Format Paperback Imprint MIT Press Country of Publication United States Edited by Tony Jebara Illustrations 51 COLOR ILLUS. Audience General Publication Date 2024-12-03 US Release Date 2024-12-03 ISBN-10 0262553465 UK Release Date 2024-12-03 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:168407359;

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