Chandra Sekhar Seelamantula

This work derives the generalized score matching objective on a convex subset of $\mathbb{R}^{d}$ constructively starting from Minimum Probability Flow (MPF) learning, and shows how classical score matching as well as domain-adapted variants for non-negative data arise naturally within the proposed framework.

Dual-pixel sensors unlock surprisingly effective single-image fence removal, outperforming existing methods by fusing geometric and frequency-based priors.