39 citations · 59 across the 8 of their papers we have counts for
8 papers
3D Graph Contrastive Learning for Molecular Property Prediction
Kisung Moon, Sunyoung Kwon
Self-supervised learning (SSL) is a method that learns the data representation by utilizing supervision inherent in the data. This learning method is in the spotlight in the drug f…
Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning
Dasol Hwang, Jinyoung Park, Sunyoung Kwon +3
In recent years, graph neural networks (GNNs) have been widely adopted in the representation learning of graph-structured data and provided state-of-the-art performance in various…
M2FN: Multi-step Modality Fusion for Advertisement Image Assessment
Kyung-Wha Park, Jung-Woo Ha, JungHoon Lee +3
Assessing advertisements, specifically on the basis of user preferences and ad quality, is crucial to the marketing industry. Although recent studies have attempted to use deep neu…
Which Strategies Matter for Noisy Label Classification? Insight into Loss and Uncertainty
Wonyoung Shin, Jung-Woo Ha, Shengzhe Li +3
Label noise is a critical factor that degrades the generalization performance of deep neural networks, thus leading to severe issues in real-world problems. Existing studies have e…
Multi-Manifold Learning for Large-scale Targeted Advertising System
Kyuyong Shin, Young-Jin Park, Kyung-Min Kim +1
Messenger advertisements (ads) give direct and personal user experience yielding high conversion rates and sales. However, people are skeptical about ads and sometimes perceive the…
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs
Dasol Hwang, Jinyoung Park, Sunyoung Kwon +3
Graph neural networks have shown superior performance in a wide range of applications providing a powerful representation of graph-structured data. Recent works show that the repre…