When: October 26, 2016, 12:30 PM
Location: 3rd Floor Orchard View Room , Discovery Building
Contact: 608-316-4401, firstname.lastname@example.org
Multi-view representation learning for speech, language, and beyond
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.
The weekly SILO seminar series is made possible through the generous support of the 3M Company and its Advanced Technology Group
SILO is a lecture series with speakers from the UW faculty, graduate students or invited researchers that discuss mathematical related topics. The seminars are organized by WID’s Optimization research group.
SILO’s purpose is to provide a forum that helps connect and recruit mathematically-minded graduate students. SILO is a lunch-and-listen format, where speakers present interesting math topics while the audience eats lunch.