Probabilistic Parametric Curves for Sequence Modeling
(Sprache: Englisch)
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...
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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 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.
Bibliographische Angaben
- Autor: Ronny Hug
- 2022, 226 Seiten, mit Abbildungen, Maße: 14,8 x 21 cm, Kartoniert (TB), Englisch
- Verlag: KIT Scientific Publishing
- ISBN-10: 3731511983
- ISBN-13: 9783731511984
Sprache:
Englisch
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