Hierarchical Expert Networks for Meta-Learning

Autor:	Hihn, Heinke, Braun, Daniel A.
Rok vydání:	2019
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Presented at the 4th ICML Workshop on Life Long Machine Learning, 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.00348 Zobrazit plný text záznamu View this record from Arxiv