Hug R. Probabilistic Parametric Curves for Sequence Modeling

Karlsruhe: KIT Scientific Publishing, 2022. — 224 p.This work proposes a probabilistic extension to Bézier curves as a basis for effectively modeling stochastic processes with a bounded index set. The proposed stochastic process model is based on Mixture Density Networks and Bézier curves with Gaussian random variables as control points. A key advantage of this model is given by the ability to generate multi-mode predictions in a single inference step, thus avoiding the need for Monte Carlo simulation.Acknowledgements
Notation
Introduction
Sequence Modeling
Concept
Proposed Implementation
Evaluation
Summary
Thoughts on Future Research
Bibliography
Own publications
Supervised student theses
List of Figures
List of Tables