2017
Max-Margin Invariant Features from Transformed Unlabelled Data
NeurIPS 2017poster
The study of representations invariant to common transformations of the data is important to learning. Most techniques have focused on local approximate invariance implemented within expensive optimization frameworks lacking explicit theoretical guarantees. In this paper, we study kernels that are i…