SILO



The weekly SILO Seminar Series is made possible through the generous support of the 3M Company and its Advanced Technology Group

3M

with additional support from the Analytics Group of the Northwestern Mutual Life Insurance Company

Northwestern Mutual

Multi-view representation learning for speech, language, and beyond

Karen Livescu, Assistant Professor, Toyota Technological Institute at Chicago

Date and Time: Oct 26, 2016 (12:30 PM)
Location: Orchard room (3280) at the Wisconsin Institute for Discovery Building

Abstract:

Video: https://vimeo.com/190728913

Many types of multi-dimensional data have a natural division into two "views", such as audio and video or images and text. Multi-view learning refers to techniques that use multiple views of data to learn improved models for each of the views. Theoretical and empirical results indicate that multi-view techniques can improve over single-view ones in certain settings. In many cases multiple views help by reducing noise in some sense. In this talk, I will focus on multi-view learning of representations (features) using canonical correlation analysis (CCA) and related techniques. I will present nonlinear extensions including deep CCA, where the learned representations are the outputs of deep neural networks, and other variants. Finally, I will give recent empirical results.