5 citations · 11 across the 6 of their papers we have counts for
6 papers
Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks
Yonghao Liu, Mengyu Li, Fausto Giunchiglia +4
Graph neural networks have been demonstrated as a powerful paradigm for effectively learning graph-structured data on the web and mining content from it.Current leading graph model…
A Simple Graph Contrastive Learning Framework for Short Text Classification
Yonghao Liu, Fausto Giunchiglia, Lan Huang +3
Short text classification has gained significant attention in the information age due to its prevalence and real-world applications. Recent advancements in graph learning combined…
Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning
Yonghao Liu, Mengyu Li, Wei Pang +4
Short text classification, as a research subtopic in natural language processing, is more challenging due to its semantic sparsity and insufficient labeled samples in practical sce…
Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport
Yonghao Liu, Fausto Giunchiglia, Ximing Li +3
Graph few-shot learning has garnered significant attention for its ability to rapidly adapt to downstream tasks with limited labeled data, sparking considerable interest among rese…
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training
Yonghao Liu, Mengyu Li, Ximing Li +5
Node classification is an essential problem in graph learning. However, many models typically obtain unsatisfactory performance when applied to few-shot scenarios. Some studies hav…
Resolving Word Vagueness with Scenario-guided Adapter for Natural Language Inference
Yonghao Liu, Mengyu Li, Di Liang +5
Natural Language Inference (NLI) is a crucial task in natural language processing that involves determining the relationship between two sentences, typically referred to as the pre…