用概率推理实现高效组合式元学习，仅凭单例即可快速适应新任务。
Compositional meta-learning through probabilistic task inference
- 将任务建模为可复用计算单元的结构化组合，通过生成模型捕捉共性
- 在规则与运动学习任务中准确恢复真实组件和统计特性
- 无需参数更新即可基于约束假设测试快速推断新解，适合小样本场景
为从极少经验中解决新任务，有效复用过往任务知识至关重要，这正是元学习的核心问题。组合式解决方案——将通用计算元素灵活重组为新配置——特别适用于元学习。本文提出一种组合式元学习模型，显式将任务表示为可复用计算的结构化组合。通过学习一个生成模型，捕捉任务族中共享的底层组件及其统计规律，将学习新任务转化为概率推理问题，从而无需参数更新即可通过高度约束的假设检验找到解决方案。模型在规则学习与运动学习任务中成功恢复了真实组件与统计特性，并能仅凭单个示例快速推断新解。该框架结合神经网络的表达力与概率推理的数据效率，实现了快速组合式元学习。
原文摘要 · Abstract (English)
To solve a new task from minimal experience, it is essential to effectively reuse knowledge from previous tasks, a problem known as meta-learning. Compositional solutions, where common elements of computation are flexibly recombined into new configurations, are particularly well-suited for meta-learning. Here, we propose a compositional meta-learning model that explicitly represents tasks as structured combinations of reusable computations. We achieve this by learning a generative model that captures the underlying components and their statistics shared across a family of tasks. This approach transforms learning a new task into a probabilistic inference problem, which allows for finding solutions without parameter updates through highly constrained hypothesis testing. Our model successfully recovers ground truth components and statistics in rule learning and motor learning tasks. We then demonstrate its ability to quickly infer new solutions from just single examples. Together, our framework joins the expressivity of neural networks with the data-efficiency of probabilistic inference to achieve rapid compositional meta-learning.
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