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...
Saved in:
| Superior document: | Karlsruher Schriften zur Anthropomatik |
|---|---|
| : | |
| Year of Publication: | 2022 |
| Language: | English |
| Series: | Karlsruher Schriften zur Anthropomatik
|
| Physical Description: | 1 electronic resource (226 p.) |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISBN: | 1000146434 |
| Hierarchical level: | Monograph |