28 citations · 104 across the 13 of their papers we have counts for
13 papers
Self-learn to Explain Siamese Networks Robustly
Chao Chen, Yifan Shen, Guixiang Ma +4
Learning to compare two objects are essential in applications, such as digital forensics, face recognition, and brain network analysis, especially when labeled data is scarce and i…
Gaussian Mixture Graphical Lasso with Application to Edge Detection in Brain Networks
Hang Yin, Xinyue Liu, Xiangnan Kong
Sparse inverse covariance estimation (i.e., edge de-tection) is an important research problem in recent years, wherethe goal is to discover the direct connections between a set ofn…
MLAS: Metric Learning on Attributed Sequences
Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner +2
Distance metric learning has attracted much attention in recent years, where the goal is to learn a distance metric based on user feedback. Conventional approaches to metric learni…
Attributed Sequence Embedding
Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner +2
Mining tasks over sequential data, such as clickstreams and gene sequences, require a careful design of embeddings usable by learning algorithms. Recent research in feature learnin…
Signed Distance-based Deep Memory Recommender
Thanh Tran, Xinyue Liu, Kyumin Lee +1
Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them. However, some of the existing recommendation models (e…
Higher-order Graph Convolutional Networks
John Boaz Lee, Ryan A. Rossi, Xiangnan Kong +3
Following the success of deep convolutional networks in various vision and speech related tasks, researchers have started investigating generalizations of the well-known technique…