论文标题
使用特定注意力改善了变化点检测
Usage of specific attention improves change point detection
论文作者
论文摘要
变化点是数据分布突然改变的时刻。当前的变更点检测方法基于适合顺序数据的复发神经方法。但是，最近的作品表明，基于注意机制的变压器比许多任务的标准复发模型表现更好。在较长序列的情况下，最大的好处是明显的。在本文中，我们研究了对变更点检测任务的不同注意事项，并提出了与手头任务相关的特定注意力形式。我们表明，使用一种特殊的注意力形式优于最先进的结果。
The change point is a moment of an abrupt alteration in the data distribution. Current methods for change point detection are based on recurrent neural methods suitable for sequential data. However, recent works show that transformers based on attention mechanisms perform better than standard recurrent models for many tasks. The most benefit is noticeable in the case of longer sequences. In this paper, we investigate different attentions for the change point detection task and proposed specific form of attention related to the task at hand. We show that using a special form of attention outperforms state-of-the-art results.
