Neural Point Processes is a regression method that combines 2D Gaussian Processes with neural networks to leverage spatial correlations between sparse labels on images, addressing the limitation of traditional mean squared error methods that distort predictions in unlabeled areas.

Authors

• Person: Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche Organization: AISTATS
  Track: Poster Session 3 - aistats 2026

Sources

• Track: Poster Session 3 - aistats 2026 virtual.aistats.org Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche · AISTATS via serper

Referenced by nodes (1)

• artificial neural networks concept