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.

本综述旨在阐明 PFMs 在可扩展性、安全性、逻辑推理能力、跨领域学习能力以及面向 AGI 的友好交互能力方面的研究。This survey aims to shed light on the research of the PFMs on scalability, security, logical reasoning ability, cross-domain learning ability, and the user-friendly interactive ability for artificial general intelligence.

本综述从数据管理视角对数据收集进行全面研究，提供了相关操作的研究全景图、何时使用何种技术的指导原则，并识别出值得关注的研究挑战。This survey performs a comprehensive study of data collection from a data management point of view, providing a research landscape of these operations, guidelines on which technique to use when, and identify interesting research challenges.

在大规模复杂的移动边缘网络中，涉及具有不同约束的异构设备，这为大规模 FL 实施带来了通信成本、资源分配以及隐私安全方面的挑战。In a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved, this raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale.

本综述旨在通过对基本思想与关键原则的系统回顾以及对不同算法与技术的合理分类，提供在线机器学习文献的全面综述。This survey aims to provide a comprehensive survey of the online machine learning literatures through a systematic review of basic ideas and key principles and a proper categorization of different algorithms and techniques.

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.