Brain extraction, registration and segmentation are indispensable
prepro...
Irregularly-sampled time series (ITS) are native to high-impact domains ...
Brain extraction and registration are important preprocessing steps in
n...
Deformable image registration, i.e., the task of aligning multiple image...
Early classification algorithms help users react faster to their machine...
One-shot learning has become an important research topic in the last dec...
Learning to compare two objects are essential in applications, such as
d...
Sparse inverse covariance estimation (i.e., edge de-tection) is an impor...
Distance metric learning has attracted much attention in recent years, w...
Mining tasks over sequential data, such as clickstreams and gene sequenc...
Personalized recommendation algorithms learn a user's preference for an ...
Following the success of deep convolutional networks in various vision a...
Generative Adversarial Networks (GANs) have shown great capacity on imag...
Random walks are at the heart of many existing network embedding methods...
Graphs (networks) are ubiquitous and allow us to model entities (nodes) ...
Graph classification is a problem with practical applications in many
di...
Random walks are at the heart of many existing deep learning algorithms ...
Mining discriminative subgraph patterns from graph data has attracted gr...
Collective classification has been intensively studied due to its impact...
Mining discriminative features for graph data has attracted much attenti...