4 citations · 9 across the 6 of their papers we have counts for
6 papers
Wisteria: A Unified Multi-Scale Feature Learning Framework for DNA Language Model
Weihua Wang, Haoji Li, Feilong Bao +2
DNA language model aims to decipher the regulatory grammar and semantic of genomes by capturing long range dependencies in DNA sequences. Existing methods emphasize long range toke…
Debate to Align: Reliable Entity Alignment through Two-Stage Multi-Agent Debate
Cunda Wang, Ziying Ma, Po Hu +2
Entity alignment (EA) aims to identify entities referring to the same real-world object across different knowledge graphs (KGs). Recent approaches based on large language models (L…
Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation
Weihua Wang, Qiuyu Liang, Feilong Bao +1
Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models…
Unifying Dual-Space Embedding for Entity Alignment via Contrastive Learning
Cunda Wang, Weihua Wang, Qiuyu Liang +2
Entity alignment aims to match identical entities across different knowledge graphs (KGs). Graph neural network-based entity alignment methods have achieved promising results in Eu…
Fully Hyperbolic Rotation for Knowledge Graph Embedding
Qiuyu Liang, Weihua Wang, Feilong Bao +1
Hyperbolic rotation is commonly used to effectively model knowledge graphs and their inherent hierarchies. However, existing hyperbolic rotation models rely on logarithmic and expo…
L^2GC:Lorentzian Linear Graph Convolutional Networks for Node Classification
Qiuyu Liang, Weihua Wang, Feilong Bao +1
Linear Graph Convolutional Networks (GCNs) are used to classify the node in the graph data. However, we note that most existing linear GCN models perform neural network operations…