论文标题
软注意卷积神经网络，以序列罕见事件检测
Soft Attention Convolutional Neural Networks for Rare Event Detection in Sequences
论文作者
论文摘要
序列中的自动事件检测是时间数据分析的重要方面。这些事件可以是峰值的形式，数据分布的变化，光谱特征的变化等。在这项工作中，我们提出了一种基于软发注意力的卷积神经网络（CNN）方法，用于以序列中的稀有事件检测。为了进行演示，我们尝试了井原木，我们旨在检测描述地质层变化（又称井顶/标记）的事件。将井的日志（单元或多元）输入到软注意CNN中，并训练模型以定位标记位置。注意机制使机器能够相对扩展任务的相关日志功能。实验结果表明，我们的方法能够以高精度定位罕见的事件。
Automated event detection in the sequences is an important aspect of temporal data analytics. The events can be in the form of peaks, changes in data distribution, changes of spectral characteristics etc. In this work, we propose a Soft-Attention Convolutional Neural Network (CNN) based approach for rare event detection in sequences. For the purpose of demonstration, we experiment with well logs where we aim to detect events depicting the changes in the geological layers (a.k.a. well tops/markers). Well logs (single or multivariate) are inputted to a soft attention CNN and a model is trained to locate the marker position. Attention mechanism enables the machine to relatively scale the relevant log features for the task. Experimental results show that our approach is able to locate the rare events with high precision.
