Probabilistic Parametric Curves for Sequence Modeling - Ronny Hug
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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 ... Full description
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Description
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.
More Information
| Author | Ronny Hug |
|---|---|
| Publisher | Karlsruher Institut für Technologie |
| Release year | 2022 |
| Cover type | Softcover |
| EAN | 9783731511984 |