方法对比
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| 贝叶斯少样本学习× | 高斯过程× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2018-2019 | 2006 (book); roots in Kriging, 1951) |
| 提出者≠ | Gordon et al.; Finn, Xu & Levine | Rasmussen, C. E. & Williams, C. K. I. |
| 类型≠ | Probabilistic meta-learning | Probabilistic non-parametric model |
| 开创性文献≠ | Gordon, J., Bronskill, J., Bauer, M., Nowozin, S. & Turner, R. E. (2019). Meta-Learning Probabilistic Inference for Prediction. International Conference on Learning Representations (ICLR 2019). link ↗ | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 |
| 别名 | Bayesian meta-learning, probabilistic few-shot learning, amortized Bayesian few-shot learning, Bayesian FSL | GP, Gaussian Process Regression, GPR, Kriging |
| 相关≠ | 5 | 3 |
| 摘要≠ | Bayesian few-shot learning combines Bayesian inference with meta-learning to enable a model to generalize from as few as one to five labeled examples per class. By treating task-specific parameters as random variables and learning an informative prior across many training tasks, the method produces calibrated uncertainty estimates alongside predictions — a key advantage over deterministic few-shot learners. | A Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks. |
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