69 citations · 336 across the 32 of their papers we have counts for
32 papers
One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data
Yao Su, Zhentian Qian, Lei Ma +2
Brain extraction, registration and segmentation are indispensable preprocessing steps in neuroimaging studies. The aim is to extract the brain from raw imaging scans (i.e., extract…
Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks
Thomas Hartvigsen, Jidapa Thadajarassiri, Xiangnan Kong +1
Irregularly-sampled time series (ITS) are native to high-impact domains like healthcare, where measurements are collected over time at uneven intervals. However, for many classific…
ERNet: Unsupervised Collective Extraction and Registration in Neuroimaging Data
Yao Su, Zhentian Qian, Lifang He +1
Brain extraction and registration are important preprocessing steps in neuroimaging data analysis, where the goal is to extract the brain regions from MRI scans (i.e., extraction s…
ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration
Yao Su, Xin Dai, Lifang He +1
Deformable image registration, i.e., the task of aligning multiple images into one coordinate system by non-linear transformation, serves as an essential preprocessing step for neu…
Stop&Hop: Early Classification of Irregular Time Series
Thomas Hartvigsen, Walter Gerych, Jidapa Thadajarassiri +2
Early classification algorithms help users react faster to their machine learning model's predictions. Early warning systems in hospitals, for example, let clinicians improve their…
One-Shot Learning on Attributed Sequences
Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner +2
One-shot learning has become an important research topic in the last decade with many real-world applications. The goal of one-shot learning is to classify unlabeled instances when…
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…
TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks
Xinyue Liu, Xiangnan Kong, Lei Liu +1
Generative Adversarial Networks (GANs) have shown great capacity on image generation, in which a discriminative model guides the training of a generative model to construct images…
Learning Role-based Graph Embeddings
Nesreen K. Ahmed, Ryan Rossi, John Boaz Lee +4
Random walks are at the heart of many existing network embedding methods. However, such algorithms have many limitations that arise from the use of random walks, e.g., the features…
Inductive Representation Learning in Large Attributed Graphs
Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou +4
Graphs (networks) are ubiquitous and allow us to model entities (nodes) and the dependencies (edges) between them. Learning a useful feature representation from graph data lies at…
Deep Graph Attention Model
John Boaz Lee, Ryan Rossi, Xiangnan Kong
Graph classification is a problem with practical applications in many different domains. Most of the existing methods take the entire graph into account when calculating graph feat…
A Framework for Generalizing Graph-based Representation Learning Methods
Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou +4
Random walks are at the heart of many existing deep learning algorithms for graph data. However, such algorithms have many limitations that arise from the use of random walks, e.g.…
Badge System Analysis and Design
Jiawei Zhang, Xiangnan Kong, Philip S. Yu
To incentivize users' participations and steer their online activities, online social networks start to provide users with various kinds of rewards for their contributions to the s…
Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification
Bokai Cao, Xiangnan Kong, Jingyuan Zhang +2
Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc.…
A review of heterogeneous data mining for brain disorders
Bokai Cao, Xiangnan Kong, Philip S. Yu
With rapid advances in neuroimaging techniques, the research on brain disorder identification has become an emerging area in the data mining community. Brain disorder data poses ma…
Partial Network Alignment with Anchor Meta Path and Truncated Generic Stable Matching
Jiawei Zhang, Weixiang Shao, Senzhang Wang +2
To enjoy more social network services, users nowadays are usually involved in multiple online social networks simultaneously. The shared users between different networks are called…
Discovering Organizational Correlations from Twitter
Jingyuan Zhang, Xiaoxiao Shi, Xiangnan Kong +2
Organizational relationships are usually very complex in real life. It is difficult or impossible to directly measure such correlations among different organizations, because impor…
DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages
Lifang He, Xiangnan Kong, Philip S. Yu +3
With advances in data collection technologies, tensor data is assuming increasing prominence in many applications and the problem of supervised tensor learning has emerged as a top…
Large-Scale Multi-Label Learning with Incomplete Label Assignments
Xiangnan Kong, Zhaoming Wu, Li-Jia Li +4
Multi-label learning deals with the classification problems where each instance can be assigned with multiple labels simultaneously. Conventional multi-label learning approaches ma…