论文标题
基于注意力标记的基于注意力的神经网络
An Attention Based Neural Network for Jet Tagging
论文作者
论文摘要
卷积神经网络是使用喷气图像作为喷气标记问题的输入的基本结构。但是，他们在培训过程中所学到的东西总是很难通过功能地图来理解。受到机器学习领域流行的注意机制的启发，我们提出了一种新型的基于注意力的神经网络（ABNN），以了解此问题。 ABNN将喷气图像与来自信号的平均喷射图像和背景相结合，以生成注意图，这些图表显然显示出根据喷气机的不同起源而具有重要意义。与类似体系结构中的网络相比，该网络实现了更好的性能，这表明注意力机制在其他作品中使用的潜力。
Convolutional neural networks are basic structures using jet images as input for the jet tagging problems. However, what they have learned during the training process is always difficult to understand just through feature maps. Inspired by the attention mechanism popular in machine learning fields, we propose a novel attention-based neural network (ABNN) to get insight of this problem. The ABNN combines a jet image with average jet images from the signal and the background to generate attention maps which show clearly the relevant importance according to the different origination of jets. Compared with networks in the similar architecture, this network achieves better performance, which indicates the potential of attention mechanism to use in other works.
