Log-Linear Models, Extensions, and Applications (Neural Information Processing series)

ISBN 10: 0262553465 / ISBN 13: 9780262553469

Published by MIT Press, 2024

About this Item

2024. paperback. . . . . . Books ship from the US and Ireland.

Seller Inventory # V9780262553469

Bibliographic Details

• Title: Log-Linear Models, Extensions, and Applications (Neural Information Processing series)
• Publisher: MIT Press
• Publication Date: 2024
• Language: English
• ISBN 10: 0262553465
• ISBN 13: 9780262553469
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.

Contributors
Aleksandr Aravkin, Avishy Carmi, Guillermo A. Cecchi, Anna Choromanska, Li Deng, Xinwei Deng, Jean Honorio, Tony Jebara, Huijing Jiang, Dimitri Kanevsky, Brian Kingsbury, Fabrice Lambert, Aurélie 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

About the Author

Aleksandr Aravkin is Assistant Professor of Applied Mathematics at the University of Washington.

Anna Choromanska is Assistant Professor at New York University's 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.