论文标题
转移，多任务和元学习系统之间的同态
Homomorphisms Between Transfer, Multi-Task, and Meta-Learning Systems
论文作者
论文摘要
转移学习，多任务学习和元学习是与学习跨学习任务的概括有关的主题，并且与一般智能密切相关。但是，在文献中，它们之间的形式，一般系统差异并没有得到充实。缺乏系统水平的形式主义导致在协调相关的跨学科工程工作方面遇到困难。该手稿将转移学习，多任务学习和元学习作为抽象学习系统形式化，与正式的最小主义摘要系统理论一致。此外，它利用提出的形式主义将学习的三个概念与组成，等级和结构同态有关。发现很容易用投入输出系统来描绘，从而强调了划定传输，多任务和元学习之间正式的一般系统差异的便利性。
Transfer learning, multi-task learning, and meta-learning are well-studied topics concerned with the generalization of knowledge across learning tasks and are closely related to general intelligence. But, the formal, general systems differences between them are underexplored in the literature. This lack of systems-level formalism leads to difficulties in coordinating related, inter-disciplinary engineering efforts. This manuscript formalizes transfer learning, multi-task learning, and meta-learning as abstract learning systems, consistent with the formal-minimalist abstract systems theory of Mesarovic and Takahara. Moreover, it uses the presented formalism to relate the three concepts of learning in terms of composition, hierarchy, and structural homomorphism. Findings are readily depicted in terms of input-output systems, highlighting the ease of delineating formal, general systems differences between transfer, multi-task, and meta-learning.
