Probabilistic Parametric Curves for Sequence Modeling

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 advant...

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Detalles Bibliográficos
Otros Autores: Hug, Ronny (auth)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Karlsruhe KIT Scientific Publishing 2022
Colección:Karlsruher Schriften zur Anthropomatik
Materias:
Maths for computer scientists
Probabilistische Sequenzmodellierung
Stochastische Prozesse
Neuronale Netzwerke
Parametrische Kurven
Probabilistic Sequence Modeling
Stochastic Processes
Neural Networks
Parametric Curves
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009672507906719
Descripción
Sumario: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.
Descripción Física:1 electronic resource (226 p.)

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