Hierarchical expert networks for meta-learning
| Autor: | Hihn, Heinke, Braun, Daniel Alexander |
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| Přispěvatelé: | European Union (EU), Horizon 2020 |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| DOI: | 10.18725/oparu-38510 |
| 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. acceptedVersion |
| Databáze: | OpenAIRE |
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