Hierarchical Expert Networks for Meta-Learning
This addresses the challenge of few-shot learning in meta-learning for AI systems, though it appears incremental as it builds on existing partitioning and specialization concepts.
The paper tackles the problem of enabling models to adapt quickly to new tasks by proposing a hierarchical expert network that partitions the problem space for specialized decision-making, achieving efficient adaptation across image classification, regression, and reinforcement learning domains.
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.