SeqNAS: Neural Architecture Search for Event Sequence Classification

Abstract

Neural Architecture Search (NAS) methods are widely used in various industries to obtain high-quality, task-specific solutions with minimal human intervention. Event Sequences (EvS) find widespread use in various industrial applications, including churn prediction, customer segmentation, fraud detection, and fault diagnosis, among others. Such data consist of categorical and real-valued components with irregular timestamps. Despite the usefulness of NAS methods, previous approaches only have been applied to other domains: images, texts or time series. Our work addresses this limitation by introducing a novel NAS algorithm — SeqNAS, specifically designed for event sequence classification. We develop a simple yet expressive search space that leverages commonly used building blocks for event sequence classification, including multi-head self attention, convolutions, and recurrent cells. To perform the search, we adopt sequential Bayesian Optimization and utilize previously trained models as an ensemble of teachers to augment knowledge distillation. As a result of our work, we demonstrate that our method surpasses state-of-the-art NAS methods and popular architectures suitable for sequence classification and holds great potential for various industrial applications.

Keywords

• NAS
• temporal point processes
• event sequences
• RNN
• transformers
• knowledge distillation