Probabilistic Parametric Curves for Sequence Modeling

Probabilistic Parametric Curves for Sequence Modeling

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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 adva...

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Bibliographic Details
Main Author: Hug, Ronny (auth)
Format: Electronic Book Chapter
Language:English
Published: Karlsruhe KIT Scientific Publishing 2022
Series:Karlsruher Schriften zur Anthropomatik
Subjects:
Maths for computer scientists
Probabilistische Sequenzmodellierung
Stochastische Prozesse
Neuronale Netzwerke
Parametrische Kurven
Probabilistic Sequence Modeling
Stochastic Processes
Neural Networks
Parametric Curves
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Summary: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.
Physical Description:1 electronic resource (226 p.)
ISBN:KSP/1000146434
9783731511984
Access:Open Access

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