Regularized Deep Learning with Data Geometry and Filter Structures

The central problem of deep learning is how to generalize well from training data to unseen data. One such solution is to regularize deep learning with priors encoded into models. In this talk we will discuss various techniques we
recently developed in regularizing deep learning with data geometry, such as lowrank subspace, or structures over convolutional filters.