Research map / Probabilistic and scientific modeling / Variational and generative sampling
Identification of Gaussian Process State Space Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GPSSM instead of ARGP | Cart-pole trajectory prediction with 8 training episodes · Cart-pole | average Euclidean distance: 1.03 -> 0.67 | Helped |
| Use GPSSM instead of Kalman | Cart-pole trajectory prediction with 8 training episodes · Cart-pole | average Euclidean distance: 1.52 -> 0.67 | Helped |
| Use GPSSM instead of Kalman | Cart-pole trajectory prediction with 15 training episodes · Cart-pole | average Euclidean distance: 1.48 -> 0.59 | Helped |
| Use GPSSM instead of ARGP | Cart-pole trajectory prediction with 2 training episodes · Cart-pole | average Euclidean distance: 1.22 -> 1.21 | Mixed |
| Use a non-smooth kernel rather than RBF (ablation) | GPSSM on the synthetic non-smooth transition · Synthetic 1D system | transition fit | Helped |
| Use an RBF-Matern composition rather than a kernel without an RBF component (ablation) | GPSSM on the synthetic non-smooth transition · Synthetic 1D system | extrapolation | Helped |
Closest papers by meaning
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