6 citations · 20 across the 6 of their papers we have counts for
6 papers · 1 filter
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…
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…
Graphs, Entities, and Step Mixture
Kyuyong Shin, Wonyoung Shin, Jung-Woo Ha +1
Existing approaches for graph neural networks commonly suffer from the oversmoothing issue, regardless of how neighborhoods are aggregated. Most methods also focus on transductive…
Which Ads to Show? Advertisement Image Assessment with Auxiliary Information via Multi-step Modality Fusion
Kyung-Wha Park, JungHoon Lee, Sunyoung Kwon +3
Assessing aesthetic preference is a fundamental task related to human cognition. It can also contribute to various practical applications such as image creation for online advertis…