A survey on text classification: From traditional to deep learning

Q Li, H Peng, J Li, C Xia, R Yang, L Sun… - ACM Transactions on …, 2022 - dl.acm.org
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 …

Graph structure learning with variational information bottleneck

Q Sun, J Li, H Peng, J Wu, X Fu, C Ji… - Proceedings of the AAAI …, 2022 - ojs.aaai.org
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 …

A survey on text classification: From shallow to deep learning

Q Li, H Peng, J Li, C Xia, R Yang, L Sun, PS Yu… - arXiv preprint arXiv …, 2020 - arxiv.org
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 …

FedBERT: When Federated Learning Meets Pre-training

Y Tian, Y Wan, L Lyu, D Yao, H Jin, L Sun - ACM Transactions on …, 2022 - dl.acm.org
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 …

Reinforced, incremental and cross-lingual event detection from social messages

H Peng, R Zhang, S Li, Y Cao, S Pan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …

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 …

Se-gsl: A general and effective graph structure learning framework through structural entropy optimization

D Zou, H Peng, X Huang, R Yang, J Li, J Wu… - Proceedings of the …, 2023 - dl.acm.org
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 …

Deep reinforcement learning guided graph neural networks for brain network analysis

X Zhao, J Wu, H Peng, A Beheshti, JJM Monaghan… - Neural Networks, 2022 - Elsevier
Modern neuroimaging techniques enable us to construct human brains as brain networks or
connectomes. Capturing brain networks' structural information and hierarchical patterns is …

Comprehensive graph gradual pruning for sparse training in graph neural networks

C Liu, X Ma, Y Zhan, L Ding, D Tao… - … on Neural Networks …, 2023 - ieeexplore.ieee.org
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 …

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 …