Area of research
Artificial Intelligence · Computer Vision and Pattern Recognition
Research interest
Research interests include Topic Modeling, Multimodal Machine Learning Applications, Advanced Graph Neural Networks, and Natural Language Processing Techniques.
A new semi-supervised fuzzy clustering method based on latent representation learning and information fusion
A two-stage framework by leveraging large language model for predicting clinical trial outcomes
Low-FaceNet: Face Recognition-Driven Low-Light Image Enhancement
Improving Topic Tracing with a Textual Reader for Conversational Knowledge Based Question Answering
A New Multi-level Knowledge Retrieval Model for Task-Oriented Dialogue
A new graph-based clustering method with dual-feature regularization and Laplacian rank constraint
Trilinear Distillation Learning and Question Feature Capturing for Medical Visual Question Answering
OdeBERT: One-stage Deep-supervised Early-exiting BERT for Fast Inference in User Intent Classification
Recent progress in leveraging deep learning methods for question answering
A Bi-level representation learning model for medical visual question answering
Fast medical concept normalization for biomedical literature based on stack and index optimized self-attention
Lexicon-Based Sentiment Convolutional Neural Networks for Online Review Analysis
Sentiment strength detection with a context-dependent lexicon-based convolutional neural network
Segment-level joint topic-sentiment model for online review analysis
A bibliometric analysis of natural language processing in medical research
Natural Language Processing Empowered Mobile Computing
Social emotion classification of short text via topic-level maximum entropy model