Title:	Uncertainty estimation and low-dimensional representation learning
DNr:	Berzelius-2023-198
Project Type:	LiU Berzelius
Principal Investigator:	Daniel Gedon <daniel.gedon@it.uu.se>
Affiliation:	Uppsala universitet
Duration:	2023-09-01 – 2024-03-01
Classification:	10201
Homepage:	https://mp.uu.se/web/profilsidor/start/-/emp/N19-1795
Keywords:

Abstract

This project will support the experiments for projects in three main directions: (1) theory of deep learning. Here, we develop new theories and try to verify this on experimental benchmark data. This includes low-dimensional representation learning. (2) Uncertainty quantification with kernel-based methods. This is based on recent advances in feature learning (possibly low-dimensional) in kernels and exploiting that information efficiently. (3) identification of dynamic systems with various deep learning-based methods such as deep state-space systems and graph neural networks.