With the performance of deep neural networks (DNNs) remarkably improving...
Deep learning classifiers achieve state-of-the-art performance in variou...
Large language models (LLMs) have witnessed a meteoric rise in popularit...
Adversarial examples are crafted by adding indistinguishable perturbatio...
Federated Semi-Supervised Learning (FSSL) aims to learn a global model f...
Copyright protection of the Federated Learning (FL) model has become a m...
Deep learning models trained on large-scale data have achieved encouragi...
Graph Neural Networks (GNNs) have achieved great success in mining
graph...
Tensor factorization has received increasing interest due to its intrins...
Crowd counting is a regression task that estimates the number of people ...
Despite enormous research interest and rapid application of federated
le...
Natural language video localization (NLVL) is an important task in the
v...
Tensor factorization has been proved as an efficient unsupervised learni...
Representation learning on static graph-structured data has shown a
sign...
Adversarial data examples have drawn significant attention from the mach...
Federated learning enables multiple clients, such as mobile phones and
o...
Federated learning is a prominent framework that enables clients (e.g.,
...
As the successor of H.265/HEVC, the new versatile video coding standard
...
As an important perceptual characteristic of the Human Visual System (HV...
Tensor factorization has been demonstrated as an efficient approach for
...
While functional magnetic resonance imaging (fMRI) is important for
heal...
Stackelberg security game models and associated computational tools have...