3 papers
math.OC2026
Natural Riemannian gradient for learning functional tensor networks
Nikolas Klug, Michael Ulbrich, André Uschmajew +1
We consider machine learning tasks with low-rank functional tree tensor networks (TTN) as the learning model. While in the case of least-squares regression, low-rank functional TTN…
cs.CV2020
Error Bounds of Projection Models in Weakly Supervised 3D Human Pose Estimation
Nikolas Klug, Moritz Einfalt, Stephan Brehm +1
The current state-of-the-art in monocular 3D human pose estimation is heavily influenced by weakly supervised methods. These allow 2D labels to be used to learn effective 3D human…
cs.AI2018
k-RNN: Extending NN-heuristics for the TSP
Nikolas Klug, Alok Chauhan, Ramesh Ragala +1
In this paper we present an extension of existing Nearest-Neighbor heuristics to an algorithm called k-Repetitive-Nearest-Neighbor. The idea is to start with a tour of k nodes and…