30 citations · 51 across the 7 of their papers we have counts for
7 papers
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models
Baoshuo Kan, Teng Wang, Wenpeng Lu +3
Pre-trained vision-language models, e.g., CLIP, working with manually designed prompts have demonstrated great capacity of transfer learning. Recently, learnable prompts achieve st…
Medical Question Summarization with Entity-driven Contrastive Learning
Wenpeng Lu, Sibo Wei, Xueping Peng +3
By summarizing longer consumer health questions into shorter and essential ones, medical question-answering systems can more accurately understand consumer intentions and retrieve…
Tri-Attention: Explicit Context-Aware Attention Mechanism for Natural Language Processing
Rui Yu, Yifeng Li, Wenpeng Lu +1
In natural language processing (NLP), the context of a word or sentence plays an essential role. Contextual information such as the semantic representation of a passage or historic…
Modeling Multi-interest News Sequence for News Recommendation
Rongyao Wang, Wenpeng Lu
A session-based news recommender system recommends the next news to a user by modeling the potential interests embedded in a sequence of news read/clicked by her/him in a session.…
Modeling Complex Dependencies for Session-based Recommendations via Graph Neural Networks
Qian Zhang, Wenpeng Lu
Session-based recommendations (SBRs) capture items' dependencies from the sessions to recommend the next item. In recent years, Graph neural networks (GNN) based SBRs have become t…
Aspect-driven User Preference and News Representation Learning for News Recommendation
Rongyao Wang, Wenpeng Lu, Shoujin Wang +3
News recommender systems are essential for helping users to efficiently and effectively find out those interesting news from a large amount of news. Most of existing news recommend…
Jointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation
Shoujin Wang, Longbing Cao, Liang Hu +4
A transaction-based recommender system (TBRS) aims to predict the next item by modeling dependencies in transactional data. Generally, two kinds of dependencies considered are intr…