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.

翻译：长期连续数据分类是一项重要的机器学习任务,在许多应用情景中都可以看到。经常的神经网络、变异器和进化神经网络是从连续数据中学习的三大主要技术。在这些方法中,可伸缩到非常长序列的时空革命网络(TCNs)在时间序列回归中取得了显著进展。然而,TCNs用于序列分类的性能并不令人满意,因为它们在最后位置上使用了扭曲的连接协议和产出类别。这种不对称限制了它们进行取决于整个序列的分类的性能。在这项工作中,我们提出了一个称为圆形变异神经网络(CDIL-CNN)的对称多尺度结构(CDIL-CNN),其中每个位置都有同等的机会从先前各层的其他位置获得信息。我们的模型在所有位置上都提供分类登录记录,我们可以应用简单的混合学习来作出更好的决定。我们已经在各种长顺序数据集中测试了CDIL-CNN。实验结果表明,我们的方法优于许多州级方法。