Probabilistic Parametric Curves for Sequence Modeling
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Beschreibung
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.
Autor*in
Ronny Hug
Themen in »Probabilistic Parametric Curves for Sequence Modeling«
Probabilistische Sequenzmodellierung Stochastische Prozesse Neuronale Netzwerke Parametrische Kurven Probabilistic Sequence Modeling Stochastic Processes Neural Networks Parametric Curves