2 citations · 5 across the 13 of their papers we have counts for
7 papers · 1 filter
TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection
Wei Wu, Zhuoshi Pan, Chao Wang +6
Rapid advances in Large Language Models (LLMs) have spurred demand for processing extended context sequences in contemporary applications. However, this progress faces two challeng…
Towards Constraint-aware Learning for Resource Allocation in NFV Networks
Tianfu Wang, Long Yang, Chao Wang +6
Virtual Network Embedding (VNE) is a fundamental resource allocation challenge that is associated with hard and multifaceted constraints in network function virtualization (NFV). E…
Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding with LLMs
Wei Wu, Chao Wang, Liyi Chen +6
Proteins, as essential biomolecules, play a central role in biological processes, including metabolic reactions and DNA replication. Accurate prediction of their properties and fun…
COMET: NFT Price Prediction with Wallet Profiling
Tianfu Wang, Liwei Deng, Chao Wang +5
As the non-fungible token (NFT) market flourishes, price prediction emerges as a pivotal direction for investors gaining valuable insight to maximize returns. However, existing wor…
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
Tianfu Wang, Qilin Fan, Chao Wang +4
Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Rein…
DGR: A General Graph Desmoothing Framework for Recommendation via Global and Local Perspectives
Leilei Ding, Dazhong Shen, Chao Wang +3
Graph Convolutional Networks (GCNs) have become pivotal in recommendation systems for learning user and item embeddings by leveraging the user-item interaction graph's node informa…
AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Wei Wu, Chao Wang, Dazhong Shen +3
Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture colla…