Log-Linear Models, Extensions, and Applications

Aleksandr Aravkin, Anna Choromanska, Li Deng, Georg Heigold, Tony Jebara, Dimitri Kanevsky, Stephen J. Wright, Michael I. Jordan, Thomas G. Dietterich

• Publisher: The MIT Press
• Year: 2018
• eBook ISBN: 9780262351614
• Edition: 0
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

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

Table of contents

• Cover
• Series Announcement Page
• Title Page
• Copyright
• Table of Contents
• 1. Some Insights into the Geometry and Training of Neural Networks
• 2. Variable Selection in Gaussian Markov Random Fields
• 3. Log-Nonlinear Formulations for Robust High-dimensional Modeling
• 4. Semistochastic Quadratic Bound Methods
• 5. Use of Deep Learning Features in Log-Linear Models
• 6. Informative Nonstationarity in Paleoclimatological Log-Linear Models
• 7. Log-Linear Quandloids and Information Fusion
• 8. Optimization Techniques for Large Speech Tasks
• Series Page