Multi-task learning (MTL) has gained significant popularity in recommend...
Encrypted traffic classification is receiving widespread attention from
...
Temporal Graph Learning, which aims to model the time-evolving nature of...
Kernels on discrete structures evaluate pairwise similarities between ob...
Photo retouching aims to adjust the luminance, contrast, and saturation ...
As a powerful tool for modeling complex relationships, hypergraphs are
g...
The goal of recommender systems is to provide ordered item lists to user...
Recently, the pretrain-finetuning paradigm has attracted tons of attenti...
Recent studies show that depression can be partially reflected from huma...
With the recent success of graph convolutional networks (GCNs), they hav...
Collaborative filtering (CF) is a widely studied research topic in
recom...
Due to the high efficiency and less weather dependency, autonomous
green...
Model-based reinforcement learning is a widely accepted solution for sol...
HyperGraph Convolutional Neural Networks (HGCNNs) have demonstrated thei...
Though the multiscale graph learning techniques have enabled advanced fe...
Recently, the teacher-student knowledge distillation framework has
demon...
Tagging has been recognized as a successful practice to boost relevance
...
Graph Identification (GI) has long been researched in graph learning and...
Social media has been developing rapidly in public due to its nature of
...
Deep neural networks (DNNs) have been applied in class incremental learn...
Principal Component Analysis (PCA) is a popular tool for dimensionality
...