Meta-Learning with Variational Bayes
- URL: http://arxiv.org/abs/2103.02265v1
- Date: Wed, 3 Mar 2021 09:02:01 GMT
- Title: Meta-Learning with Variational Bayes
- Authors: Lucas D. Lingle
- Abstract summary: We introduce a new approach to address the more general problem of generative meta-learning.
Our contribution leverages the AEVB framework and mean-field variational Bayes, and creates fast-adapting latent-space generative models.
At the heart of our contribution is a new result, showing that for a broad class of deep generative latent variable models, the relevant VB updates do not depend on any generative neural network.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The field of meta-learning seeks to improve the ability of today's machine
learning systems to adapt efficiently to small amounts of data. Typically this
is accomplished by training a system with a parametrized update rule to improve
a task-relevant objective based on supervision or a reward function. However,
in many domains of practical interest, task data is unlabeled, or reward
functions are unavailable. In this paper we introduce a new approach to address
the more general problem of generative meta-learning, which we argue is an
important prerequisite for obtaining human-level cognitive flexibility in
artificial agents, and can benefit many practical applications along the way.
Our contribution leverages the AEVB framework and mean-field variational Bayes,
and creates fast-adapting latent-space generative models. At the heart of our
contribution is a new result, showing that for a broad class of deep generative
latent variable models, the relevant VB updates do not depend on any generative
neural network.
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