论文标题
使用循环扩张卷积神经网络对长顺序数据进行分类
Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networks
论文作者
论文摘要
长顺序数据的分类是一项重要的机器学习任务，并且出现在许多应用程序方案中。复发性神经网络，变压器和卷积神经网络是从顺序数据中学习的三种主要技术。在这些方法中，可以扩展到很长序列的时间卷积网络（TCN）在时间序列回归中取得了显着的进展。但是，TCN用于序列分类的性能并不令人满意，因为它们在最后一个位置使用了偏斜的连接协议和输出类。这样的不对称性限制了它们的性能进行分类，这取决于整个序列。在这项工作中，我们提出了一种称为循环扩张卷积神经网络（CDIL-CNN）的对称的多尺度架构，每个位置都有同等机会从先前层的其他位置接收信息。我们的模型在所有职位上都提供了分类逻辑，我们可以应用一个简单的合奏学习来实现更好的决定。我们已经在各种长顺序数据集上测试了CDIL-CNN。实验结果表明，我们的方法在许多最新方法上具有出色的性能。
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.
