论文标题
深入学习归因序列
Deep Learning on Attributed Sequences
论文作者
论文摘要
最新的特征学习研究已扩展到序列数据，其中每个实例由一系列具有可变长度的异质项目组成。但是，在许多现实世界中，数据以归因序列的形式存在，该序列由一组固定尺寸的属性和可变长度序列组成，它们之间具有依赖关系。在属性序列上下文中，由于序列及其相关属性之间的依赖性，特征学习仍然具有挑战性。在本文中，我们专注于分析和建立有关归因序列的四个新问题的深度学习模型。我们在现实世界数据集上进行的广泛实验表明，所提出的解决方案可显着提高每个任务的性能，而不是属性序列的最新方法。
Recent research in feature learning has been extended to sequence data, where each instance consists of a sequence of heterogeneous items with a variable length. However, in many real-world applications, the data exists in the form of attributed sequences, which is composed of a set of fixed-size attributes and variable-length sequences with dependencies between them. In the attributed sequence context, feature learning remains challenging due to the dependencies between sequences and their associated attributes. In this dissertation, we focus on analyzing and building deep learning models for four new problems on attributed sequences. Our extensive experiments on real-world datasets demonstrate that the proposed solutions significantly improve the performance of each task over the state-of-the-art methods on attributed sequences.
