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| 자기 지도 가우시안 프로세스× | 베이즈 가우시안 과정× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2019–2021 | 1978–2006 |
| 창시자≠ | Fortuin, V. et al.; broader self-supervised GP literature | O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I. |
| 유형≠ | Probabilistic model (self-supervised GP pretraining + kernel learning) | Probabilistic kernel model |
| 원전≠ | 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 ↗ | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 |
| 별칭 | SSL-GP, self-supervised GP, self-supervised GPR, self-supervised Gaussian process regression | GP regression, GPR, Gaussian process model, GP classifier |
| 관련≠ | 6 | 3 |
| 요약≠ | 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. | A Bayesian Gaussian Process (GP) places a probability distribution directly over functions, using a kernel to encode similarity between inputs. After observing data, Bayes' rule converts this prior into a posterior that yields not just point predictions but calibrated uncertainty estimates at every new input — making it one of the most principled probabilistic models in machine learning. |
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