迁移、多任务和元学习系统之间的同构
Homomorphisms Between Transfer, Multi-Task, and Meta-Learning Systems
摘要：
迁移学习、多任务学习和元学习都是关于跨学习任务的知识概括的研究课题，与一般智力密切相关。但是，正式的，一般的系统之间的差异，在文献中探索不足。这种缺乏系统级形式主义的情况导致了协调相关的跨学科工程工作的困难。这份手稿将迁移学习、多任务学习和元学习形式化为抽象学习系统，与Mesarovic和Takahara的形式最简抽象系统理论相一致。此外，它使用所提出的形式主义，涉及的组成，层次结构和结构同态方面的学习的三个概念。研究结果很容易描述的输入输出系统，突出了容易划定正式的，一般的系统之间的差异转移，多任务，和元学习。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
