Linganisha mbinu

	Kujifunza kwa Kiasi Kidogo kwa Mbinu ya Bayesian	Kujifunza kwa Kuhamisha kwa Bayesian
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2018-2019	2006–2010
Mwanzilishi	Gordon et al.; Finn, Xu & Levine	Raina, R.; Ng, A. Y.; Koller, D. (and subsequent community)
Aina	Probabilistic meta-learning	Probabilistic transfer / domain adaptation framework
Chanzo asilia	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).	Raina, R., Ng, A. Y., & Koller, D. (2006). Constructing informative priors using transfer learning. In Proceedings of the 23rd International Conference on Machine Learning (ICML), pp. 713–720. ACM.
Majina mbadala	Bayesian meta-learning, probabilistic few-shot learning, amortized Bayesian few-shot learning, Bayesian FSL	BTL, Bayesian domain adaptation, probabilistic transfer learning, Bayesian knowledge transfer
Zinazohusiana	5	4
Muhtasari	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.	Bayesian Transfer Learning is a probabilistic framework that uses knowledge from a data-rich source domain to construct informative priors for a model trained on a data-scarce target domain. By encoding source-domain knowledge as prior distributions over parameters, the framework lets the model generalize well on the target task even with very limited labeled examples.