使用对比学习从连续时间序列中编码根树中的二元事件
Encoding Binary Events from Continuous Time Series in Rooted Trees using Contrastive Learning
AI总结：
针对本地网络根树拓扑结构推断问题，提出一种对比学习方法，从连续时间序列数据中学习二元事件编码器，初步结果表明该方法具有学习有价值编码器的潜力。
摘要：
宽带基础设施所有者并不总是知道客户在本地网络中是如何连接的，这些网络被结构化为根树。最近的一项研究能够使用来自树叶节点（客户）的离散时间序列数据来推断本地网络的拓扑结构。在本研究中，我们提出了一种对比方法，用于从连续时间序列数据中学习二元事件编码器。作为初步结果，我们表明我们的方法在学习有价值的编码器方面具有一定的潜力。
英文摘要：
Broadband infrastructure owners do not always know how their customers are connected in the local networks, which are structured as rooted trees. A recent study is able to infer the topology of a local network using discrete time series data from the leaves of the tree (customers). In this study we propose a contrastive approach for learning a binary event encoder from continuous time series data. As a preliminary result, we show that our approach has some potential in learning a valuable encoder.
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。
