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Uncertainty in Model-Agnostic Meta-Learning using Variational Inference
[article]
2019
arXiv
pre-print
We introduce a new, rigorously-formulated Bayesian meta-learning algorithm that learns a probability distribution of model parameter prior for few-shot learning. The proposed algorithm employs a gradient-based variational inference to infer the posterior of model parameters to a new task. Our algorithm can be applied to any model architecture and can be implemented in various machine learning paradigms, including regression and classification. We show that the models trained with our proposed
arXiv:1907.11864v2
fatcat:zzcguqsu6jafxakg3zsefhtji4