maml
Probabilistic Model-Agnostic Meta-Learning
Chelsea Finn, Kelvin Xu, Sergey Levine
In this paper,we propose aprobabilisticmeta-learning algorithm that can sample models for a new task from a modeldistribution.
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General Response
We thank all the reviewers for their insightful and encouraging comments. Per your suggestion, we will update the appendix by adding more explanations about the proof ideas. Similarly, we can extend convergence results in Theorem 4 in Appendix from FS to IFS. We expect that the approach developed in this paper will fuel this future investigation. We will update this into the revision.
A Pseudo codes
Dermnet-S chooses the top-30 classes for meta-training.
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Meta-ticket: Findingoptimalsubnetworksfor few-shotlearningwithinrandomlyinitialized neuralnetworks
Thecorrespondingfm (x) where means element-wise Rn.
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Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters
However, the theoretical convergence of ANIL has not been studied yet.
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