Research map / Time series / Causal and generative time series
A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train GP parameters end-to-end rather than separately by marginal likelihood | UAC LogReg · UWaveGestureLibraryAll test | accuracy (%): 78.23 -> 79.24 | Helped |
| Train GP parameters end-to-end rather than separately by marginal likelihood | UAC MLP · UWaveGestureLibraryAll test | accuracy (%): 87.05 -> 87.95 | Helped |
| Train GP parameters end-to-end rather than separately by marginal likelihood | UAC ConvNet · UWaveGestureLibraryAll test | accuracy (%): 88.17 -> 91.41 | Helped |
| Train MEG kernel GP parameters end-to-end rather than separately by marginal likelihood | UAC MEG kernel · UWaveGestureLibraryAll test | accuracy (%): 84.82 -> 86.61 | Helped |
| Add posterior uncertainty sampling instead of using only the posterior mean (ablation) | End-to-end ConvNet · UWaveGestureLibraryAll test | accuracy (%): 89.84 -> 91.41 | Helped |
| Add posterior uncertainty sampling instead of using only the posterior mean (ablation) | End-to-end MLP · UWaveGestureLibraryAll test | accuracy (%): 86.49 -> 87.95 | Helped |
| Add posterior uncertainty sampling instead of using only the posterior mean (ablation) | End-to-end LogReg · UWaveGestureLibraryAll test | accuracy (%): 79.12 -> 79.24 | Helped |
| Add posterior uncertainty sampling instead of using only the posterior mean (ablation) | Marginal-likelihood ConvNet · UWaveGestureLibraryAll test | accuracy (%): 87.61 -> 88.17 | Helped |
| Add posterior uncertainty sampling instead of using only the posterior mean (ablation) | Marginal-likelihood MLP · UWaveGestureLibraryAll test | accuracy (%): 85.49 -> 87.05 | Helped |
| Add posterior uncertainty sampling instead of using only the posterior mean (ablation) | Marginal-likelihood LogReg · UWaveGestureLibraryAll test | accuracy (%): 77.90 -> 78.23 | Helped |
Closest papers by meaning
- Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification (NeurIPS 2018 · similarity 0.86)
- KernelMatmul: Scaling Gaussian Processes to Large Time Series (AAAI 2025 · similarity 0.85)
- Fourier Feature Approximations for Periodic Kernels in Time-Series Modelling (AAAI 2018 · similarity 0.85)
- Adaptive Time Encoding for Irregular Multivariate Time-Series Classification (NeurIPS 2025 · similarity 0.85)
- Scalable Multi-Class Gaussian Process Classification using Expectation Propagation (ICML 2017 · similarity 0.84)
- Learning under Temporal Label Noise (ICLR 2025 · similarity 0.82)
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