Log-Linear Models, Extensions, and Applications

Aleksandr / Choromanska Aravkin

214 pages, parution le 26/11/2018

Ajouter à une liste

Indisponible

Résumé

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. 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, 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 BergAleksandr 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.

Caractéristiques techniques

	PAPIER
Éditeur(s)	Mit press
Auteur(s)	Aleksandr / Choromanska Aravkin
Parution	26/11/2018
Nb. de pages	214
EAN13	9780262039505

Avantages Eyrolles.com

Livraison à partir de 0,01 € en France métropolitaine

Paiement en ligne SÉCURISÉ

Livraison dans le monde

Retour sous 15 jours

+ d'un million et demi de livres disponibles

Log-Linear Models, Extensions, and Applications

Résumé

Caractéristiques techniques

Consultez aussi