Hug, Ronny <br/><br/>
Probabilistic Parametric Curves for Sequence Modeling 

 - Karlsruhe  KIT Scientific Publishing  2022 
 - 1 electronic resource (226 p.) 
 - Karlsruher Schriften zur Anthropomatik .
 - Karlsruher Schriften zur Anthropomatik .
<br/><br/> Open Access 
<br/><br/> 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. 
<br/><br/><br/>Creative Commons 
<br/><br/><br/>English 
<br/><br/><label>ISBN: </label>9783731511984 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Программирование
<br/><br/><label>Subjects--Index Terms: </label>Probabilistische Sequenzmodellierung Stochastische Prozesse Neuronale Netzwerke Parametrische Kurven Probabilistic Sequence Modeling Stochastic Processes Neural Networks Parametric Curves 
<br/><br/><label>Universal Decimal Class. No.: </label>004