Log-Linear Models, Extensions, and Applications
- Paperback
214 pages
- Release Date
3 December 2024
Summary
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 applic…
Book Details
| ISBN-13: | 9780262553469 |
|---|---|
| ISBN-10: | 0262553465 |
| Author: | Aleksandr Aravkin, Anna Choromanska, Li Deng, Georg Heigold, Tony Jebara |
| Publisher: | MIT Press Ltd |
| Imprint: | MIT Press |
| Format: | Paperback |
| Number of Pages: | 214 |
| Release Date: | 3 December 2024 |
| Weight: | 369g |
| Dimensions: | 254mm x 203mm |
| Series: | Neural Information Processing series |
About The Author
Aleksandr Aravkin
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.
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