Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use orthogonal signature features as inducing variables | SigGPDE · PenDigits | Mean Acc.: 97.42\pm 0.17 -> 97.73\pm 0.13; 96.66\pm 0.59 -> 97.73\pm 0.13 | Helped |
| use orthogonal signature features as inducing variables | SigGPDE · PenDigits | NLPP: 0.096\pm0.005 -> 0.085\pm0.001; 0.115\pm0.018 -> 0.085\pm0.001 | Helped |
| use orthogonal signature features and PDE-based kernel computation | SigGPDE · PenDigits | Time: 0.036 (s/iter) -> 0.022 (s/iter); 0.186 (s/iter) -> 0.022 (s/iter) | Helped |
| use orthogonal signature features and PDE-based kernel computation | SigGPDE · RightWhaleCalls | Mean Acc.: 87.70\pm 0.42 -> 86.76\pm 0.36; 86.97\pm 0.11 -> 86.76\pm 0.36 | Hurt |
| use orthogonal signature features and PDE-based kernel computation | SigGPDE · RightWhaleCalls | NLPP: 0.357\pm0.003 -> 0.382\pm0.002; 0.367\pm0.005 -> 0.382\pm0.002 | Hurt |
| use orthogonal signature features and PDE-based kernel computation | SigGPDE · RightWhaleCalls | Time: 0.048 (s/iter) -> 0.008 (s/iter); 0.438 (s/iter) -> 0.008 (s/iter) | Helped |
| increase the number of inducing variables | SigGPDE · satellite time series classification | performance across monitored metrics | Helped |
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