A Hierarchical Bayesian Model for Few-Shot Meta Learning

Published: 16 Jan 2024, Last Modified: 07 Mar 2024ICLR 2024 spotlightEveryoneRevisionsBibTeX
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Keywords: Bayesian models, Meta learning, Few-shot learning
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TL;DR: A novel hierarchical Bayesian model for the few-shot meta learning problem, with the efficient one-time episodic learning algorithm that can scale up to modern architectures (eg, ViT).
Abstract: We propose a novel hierarchical Bayesian model for the few-shot meta learning problem. We consider episode-wise random variables to model episode-specific generative processes, where these local random variables are governed by a higher-level global random variable. The global variable captures information shared across episodes, while controlling how much the model needs to be adapted to new episodes in a principled Bayesian manner. Within our framework, prediction on a novel episode/task can be seen as a Bayesian inference problem. For tractable training, we need to be able to relate each local episode-specific solution to the global higher-level parameters. We propose a Normal-Inverse-Wishart model, for which establishing this local-global relationship becomes feasible due to the approximate closed-form solutions for the local posterior distributions. The resulting algorithm is more attractive than the MAML in that it does not maintain a costly computational graph for the sequence of gradient descent steps in an episode. Our approach is also different from existing Bayesian meta learning methods in that rather than modeling a single random variable for all episodes, it leverages a hierarchical structure that exploits the local-global relationships desirable for principled Bayesian learning with many related tasks.
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Primary Area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
Submission Number: 1947