A survey on text classification: From traditional to deep learning
Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …
processing. The last decade has seen a surge of research in this area due to the …
Graph structure learning with variational information bottleneck
Abstract Graph Neural Networks (GNNs) have shown promising results on a broad spectrum
of applications. Most empirical studies of GNNs directly take the observed graph as input …
of applications. Most empirical studies of GNNs directly take the observed graph as input …
A survey on text classification: From shallow to deep learning
Text classification is the most fundamental and essential task in natural language
processing. The last decade has seen a surge of research in this area due to the …
processing. The last decade has seen a surge of research in this area due to the …
FedBERT: When Federated Learning Meets Pre-training
The fast growth of pre-trained models (PTMs) has brought natural language processing to a
new era, which has become a dominant technique for various natural language processing …
new era, which has become a dominant technique for various natural language processing …
Reinforced, incremental and cross-lingual event detection from social messages
Detecting hot social events (eg, political scandal, momentous meetings, natural hazards,
etc.) from social messages is crucial as it highlights significant happenings to help people …
etc.) from social messages is crucial as it highlights significant happenings to help people …
Internet financial fraud detection based on graph learning
R Li, Z Liu, Y Ma, D Yang, S Sun - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The rapid development of information technology such as the Internet of Things, Big Data,
artificial intelligence, and blockchain has changed the transaction mode of the financial …
artificial intelligence, and blockchain has changed the transaction mode of the financial …
Se-gsl: A general and effective graph structure learning framework through structural entropy optimization
Graph Neural Networks (GNNs) are de facto solutions to structural data learning. However, it
is susceptible to low-quality and unreliable structure, which has been a norm rather than an …
is susceptible to low-quality and unreliable structure, which has been a norm rather than an …
Deep reinforcement learning guided graph neural networks for brain network analysis
Modern neuroimaging techniques enable us to construct human brains as brain networks or
connectomes. Capturing brain networks' structural information and hierarchical patterns is …
connectomes. Capturing brain networks' structural information and hierarchical patterns is …
Comprehensive graph gradual pruning for sparse training in graph neural networks
Graph neural networks (GNNs) tend to suffer from high computation costs due to the
exponentially increasing scale of graph data and a large number of model parameters …
exponentially increasing scale of graph data and a large number of model parameters …
A descriptive human visual cognitive strategy using graph neural network for facial expression recognition
S Liu, S Huang, W Fu, JCW Lin - International Journal of Machine …, 2024 - Springer
In the period of rapid development on the new information technologies, computer vision
has become the most common application of artificial intelligence, which is represented by …
has become the most common application of artificial intelligence, which is represented by …