2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 2 cited
On the Comparison between Multi-modal and Single-modal Contrastive Learning
Wei Huang, Andi Han, Yongqiang Chen +3
Multi-modal contrastive learning with language supervision has presented a paradigm shift in modern machine learning. By pre-training on a web-scale dataset, multi-modal contrastiv…
cs.LG2024
LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs
Siqing Li, Jin-Duk Park, Wei Huang +3
Heterogeneous graph neural networks (HGNNs) have significantly propelled the information retrieval (IR) field. Still, the effectiveness of HGNNs heavily relies on high-quality labe…
cs.LG2024
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples
Dake Bu, Wei Huang, Taiji Suzuki +4
Neural Network-based active learning (NAL) is a cost-effective data selection technique that utilizes neural networks to select and train on a small subset of samples. While existi…