Skin lesion segmentation is a fundamental task in dermoscopic image anal...
Brain extraction, registration and segmentation are indispensable
prepro...
Deep multi-view subspace clustering (DMVSC) has recently attracted incre...
State abstraction optimizes decision-making by ignoring irrelevant
envir...
The advancement of imaging devices and countless images generated everyd...
The accuracy of facial expression recognition is typically affected by t...
The Pretrained Foundation Models (PFMs) are regarded as the foundation f...
Brain extraction and registration are important preprocessing steps in
n...
Deformable image registration, i.e., the task of aligning multiple image...
Normative modeling is an emerging and promising approach to effectively ...
Multi-view graph clustering (MGC) methods are increasingly being studied...
Cluster analysis plays an indispensable role in machine learning and dat...
The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment...
Human brains lie at the core of complex neurobiological systems, where t...
Brain networks characterize complex connectivities among brain regions a...
With the representation learning capability of the deep learning models,...
Identification of brain regions related to the specific neurological
dis...
Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) a...
Mapping the connectome of the human brain using structural or functional...
The ability of an agent to change its objectives in response to unexpect...
Drug development is time-consuming and expensive. Repurposing existing d...
This paper presents a novel graph-based kernel learning approach for
con...
To capture the semantic graph structure from raw text, most existing
sum...
In this paper, we propose MGNet, a simple and effective multiplex graph
...
Interpretable brain network models for disease prediction are of great v...
Multimodal brain networks characterize complex connectivities among diff...
Multi-view clustering, a long-standing and important research problem,
f...
Multi-modal clustering, which explores complementary information from
mu...
Graph embedding is essential for graph mining tasks. With the prevalence...
Graph representation learning has achieved great success in many areas,
...
With recent advances in data collection from multiple sources, multi-vie...
Along with the rapid expansion of information technology and digitalizat...
With the advance of the multi-media and multi-modal data, multi-view
clu...
Graph neural networks (GNNs) have been widely used in deep learning on
g...
Events are happening in real-world and real-time, which can be planned a...
Depression is one of the most common mental illness problems, and the
sy...
Graph representation learning has attracted increasing research attentio...
Mining Electronic Health Records (EHRs) becomes a promising topic becaus...
Recent years have witnessed the emergence and flourishing of hierarchica...
Filtering multi-dimensional images such as color images, color videos,
m...
Review rating prediction of text reviews is a rapidly growing technology...
Mixup is the latest data augmentation technique that linearly interpolat...
Generative commonsense reasoning which aims to empower machines to gener...
Name disambiguation aims to identify unique authors with the same name.
...
We present Luce, the first life-long predictive model for automated prop...
Node representation learning for directed graphs is critically important...
Text classification is the most fundamental and essential task in natura...
Ensuring the privacy of sensitive data used to train modern machine lear...
CNNs, RNNs, GCNs, and CapsNets have shown significant insights in
repres...
We propose a sparse and low-rank tensor regression model to relate a
uni...