论文

对聚焦MLOps工具的学术文献进行系统综述，揭示其功能、范围及其旨在解决的挑战，并突出真实MLOps pipeline中各工具间互操作性的重要性。A systematic review of the academic literature focused on MLOps tools is conducted to reveal their function, scope, and the challenges they are designed to address and highlight the importance of interoperability across MLOps tools in real-world MLOps pipelines.

本文对度量学习文献进行了系统综述，阐述了每种方法的优缺点，并介绍了近期涌现的一系列强大替代方法，包括非线性度量学习、相似性学习与局部度量学习。A systematic review of the metric learning literature is proposed, highlighting the pros and cons of each approach and presenting a wide range of methods that have recently emerged as powerful alternatives, including nonlinear metric learning, similarity learning and local metric learning.

从算法建模、应用和理论分析角度对 MTL 的综述，给出了 MTL 的定义，并将不同 MTL 算法分为五类：特征学习方法、低秩方法、任务聚类方法、任务关系学习方法和分解方法A survey for MTL from the perspective of algorithmic modeling, applications and theoretical analyses, which gives a definition of MTL and classify different MTL algorithms into five categories, including feature learning approach, low-rank approach, task clustering approach,task relation learning approach and decomposition approach.