One-Shot Learning on Attributed Sequences

no code implementations • 23 Jan 2022 • Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner, Aditya Arora, Jihane Zouaoui

In this paper, we study the problem of one-shot learning on attributed sequences, where each instance is composed of a set of attributes (e. g., user profile) and a sequence of categorical items (e. g., clickstream).

Network Intrusion Detection One-Shot Learning

MLAS: Metric Learning on Attributed Sequences

no code implementations • 8 Nov 2020 • Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner, Jihane Zouaoui, Aditya Arora

Distance metric learning has attracted much attention in recent years, where the goal is to learn a distance metric based on user feedback.

Attribute Metric Learning

Attributed Sequence Embedding

no code implementations • 3 Nov 2019 • Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner, Jihane Zouaoui, Aditya Arora

This problem is core to many important data mining tasks ranging from user behavior analysis to the clustering of gene sequences.

Clustering