Sirisha Pappoppula

Discrete Differential Geometry as a Regularizer for Deep Learning on Manifold-Valued Data

Deep learning models created today usually fail to handle non-Euclidean high-dimensional data due to manifold drift. Other common regularization methods like L2 weight decay and Dropout make the assumption of a flat Euclidean geometry and the lack of an inbuilt topological structure of the data manifold. This work intr...