Classification of long sequential data is an important Machine Learning task
and appears in many application scenarios. Recurrent Neural Networks,
Transformers, and Convolutional Neural Networks are three major techniques for
learning from sequential data. Among these methods, Temporal Convolutional
Networks (TCNs) which are scalable to very long sequences have achieved
remarkable progress in time series regression. However, the performance of TCNs
for sequence classification is not satisfactory because they use a skewed
connection protocol and output classes at the last position. Such asymmetry
restricts their performance for classification which depends on the whole
sequence. In this work, we propose a symmetric multi-scale architecture called
Circular Dilated Convolutional Neural Network (CDIL-CNN), where every position
has an equal chance to receive information from other positions at the previous
layers. Our model gives classification logits in all positions, and we can
apply a simple ensemble learning to achieve a better decision. We have tested
CDIL-CNN on various long sequential datasets. The experimental results show
that our method has superior performance over many state-of-the-art approaches.

本文提出了一种基于循环膨胀卷积神经网络（Circular Dilated Convolutional Neural Network，CDIL-CNN）的对称多尺度架构，旨在解决长序列数据分类问题中 TCN 方法因使用不平衡连接协议和类别输出限制性等问题表现不佳的问题。研究者使用 CDIL-CNN 在多个长序列数据集上进行了测试，结果表明其较之前其他方法表现更出色。