Probabilistic Parametric Curves for Sequence Modeling

• Auteur principal: Hug, Ronny (auth)
• Format: Électronique Chapitre de livre
• Langue: anglais
• Publié: Karlsruhe KIT Scientific Publishing 2022
• Collection: Karlsruher Schriften zur Anthropomatik
• Sujets: Maths for computer scientists; Probabilistische Sequenzmodellierung; Stochastische Prozesse; Neuronale Netzwerke; Parametrische Kurven; Probabilistic Sequence Modeling; Stochastic Processes; Neural Networks; Parametric Curves
• Accès en ligne: DOAB: download the publication; DOAB: description of the publication

Résumé

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.

Description matérielle

1 electronic resource (226 p.)

ISBN

KSP/1000146434
9783731511984

Accès

Open Access