Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Kernel Bayesian Inference with Posterior Regularization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace squared regularization with thresholding regularization (drop negative beta_i) | kernel Bayesian inference (pKBR) · Toy dynamics | running time: 601.3s vs 3667.4s for KBR | Helped |
| replace squared regularization with thresholding regularization | kernel Bayesian inference (pKBR) · Toy dynamics | MSE | No clear effect |
| add posterior regularization pulling posterior toward dynamics-derived points (kRegBayes) | pKBR · Toy dynamics | MSE | Helped |
| add posterior regularization (kRegBayes) on top of pKBR | kernel Bayesian inference with thresholding · Camera position recovery | MSE | Helped |
| replace squared regularization with thresholding regularization | kernel Bayesian inference (pKBR) · Camera position recovery | MSE | Hurt |
| use kernel filters instead of linear Kalman filter | state-space filtering · Camera position recovery | MSE | Helped |
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