# Hierarchical Expert Networks for Meta-Learning

Source: [Hierarchical Expert Networks for Meta-Learning](<https://www.sota2.com/research/paper/68fa68481e209131cea1b134>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:33:00.328Z

Authors: Heinke Hihn, Daniel A. Braun

Published: 2019-10-31T06:23:52.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1911.00348>)

[PDF](<https://arxiv.org/pdf/1911.00348.pdf>)