Probabilistic Parametric Curves for Sequence Modeling
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.
KeywordsProbabilistische Sequenzmodellierung; Stochastische Prozesse; Neuronale Netzwerke; Parametrische Kurven; Probabilistic Sequence Modeling; Stochastic Processes; Neural Networks; Parametric Curves
PublisherKIT Scientific Publishing
Publication date and placeKarlsruhe, 2022
ImprintKIT Scientific Publishing
SeriesKarlsruher Schriften zur Anthropomatik, 54
Maths for computer scientists