Machine learningMachine learning
自监督高斯过程
自监督高斯过程（SSL-GP）将高斯过程的原则性不确定性量化与自监督预训练相结合，在拟合小标记集上的GP之前，从无标记数据中学习表达性核或潜在表示。这使得该方法在标记数据稀少的环境中特别强大，在这些环境中，传统的GP会过拟合或产生校准不良的不确定性估计。
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来源
- Fortuin, V., Rätsch, G., & Mandt, S. (2020). GP-VAE: Deep probabilistic time series imputation using Gaussian process variational autoencoders. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 108, 1651–1661. link ↗
- Gaussian process. Wikipedia. link ↗
如何引用本页
ScholarGate. (2026, June 3). Self-supervised Gaussian Process (SSL-GP). ScholarGate. https://scholargate.app/zh/machine-learning/self-supervised-gaussian-process
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