What does Theta Mean
Training and testing must match. Task must share structure.
The “Structure” means statistical dependence on shared latent information $\theta$.
Bayesian Meta-Learning Approaches
第一个想到的方法是让模型直接输出关于$y\^{ts}$分布的参数值。
好处是简单、能够结合其他的多种方法。
坏处是不能得到模型函数的不确定性原因，如确定数据点之间的不确定性如何关联。只能够表达有限的相对于目标$y^{ts}$的分布类别。倾向于产生低校准的不确定性估计。
The Bayesian Deep Learning Toolbox
The goal is to represent distributions with neural networks.
Bayesian black-box meta-learning
Bayesian optimization-based meta-learning
Use ensembles to model non-gaussian posterior
Sample Parameter Vectors
Sample parameter vectors with a procedure like Hamiltonian Monte Carlo to model non-Gaussian posterior over all parameters:
Methods Summary
How to Evaluate a Bayesian Meta-Learner
Use the standard benchmarks i.e. MiniImagenet accuracy:
Pros:
- Standardized
- Real Images
- Good check that the approach didn’t break anything
Cons:
- Metrics like accuracy don’t evaluate uncertainty
- Tasks may not exhibit ambiguity
- Uncertainty may not be useful on this dataset
Note: Cover Picture