Hierarchical Expert Networks for Meta-Learning

Heinke Hihn; Daniel A. Braun

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arXiv · 1911.00348

Hierarchical Expert Networks for Meta-Learning

Heinke Hihn·Daniel A. Braun

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Abstract

The goal of meta-learning is to train a model on a variety of learning tasks, such that it can adapt to new problems within only a few iterations. Here we propose a principled information-theoretic model that optimally partitions the underlying problem space such that specialized expert decision-makers solve the resulting sub-problems. To drive this specialization we impose the same kind of information processing constraints both on the partitioning and the expert decision-makers. We argue that this specialization leads to efficient adaptation to new tasks. To demonstrate the generality of our approach we evaluate three meta-learning domains: image classification, regression, and reinforcement learning.

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BibTeX RIS

Heinke Hihn, Daniel A. Braun. 2019-10-31. Hierarchical Expert Networks for Meta-Learning. https://arxiv.org/abs/1911.00348

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