SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement
, Google Brain, Cornell University
Date: Friday, May 26, 2017
Time: 3:00 PM to 4:00 PM Note: all times are in the Eastern Time Zone
Host: Nir Shavit and David Rolnick, MIT
Contact: Joanne Talbot Hanley, 617-253-6054, email@example.com
Speaker URL: None
TALK: Maithra Raghu: SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement
Abstract: With the continuing empirical successes of deep networks, it becomes increasingly important to develop better methods for understanding training of models and the representations learned within. In this talk we propose Singular Vector Canonical Correlation Analysis (SVCCA), a tool for quickly comparing two representations in a way that is both invariant to affine transform (allowing comparison between different layers and networks) and fast to compute (allowing more comparisons to be calculated than with previous methods). We deploy this tool to measure the intrinsic dimensionality of layers, showing in some cases needless over-parameterization; to probe learning dynamics throughout training, finding that networks converge to final representations from the bottom up; to show where class-specific information in networks is formed; and to suggest new training regimes that simultaneously save computation and overfit less.
Created by Joanne Talbot Hanley at Thursday, May 25, 2017 at 1:20 PM.