Catatan bukti metode

Self-supervised Gaussian Process

Self-supervised Gaussian Process (SSL-GP) combines the principled uncertainty quantification of Gaussian processes with self-supervised pretraining, learning expressive kernels or latent representations from unlabeled data before fitting a GP on a small labeled set. This makes the approach especially powerful in low-labeled-data regimes where a conventional GP would overfit or produce poorly calibrated uncertainty estimates.

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Self-supervised Gaussian Process (SSL-GP)

Catatan metode taksonomi · ml-model / machine-learning

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