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Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning
In recent years, graph neural networks (GNNs) have been widely adopted i...
03/01/2021 ∙ by Dasol Hwang, et al. ∙
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M2FN: Multi-step Modality Fusion for Advertisement Image Assessment
Assessing advertisements, specifically on the basis of user preferences ...
01/31/2021 ∙ by Kyung-Wha Park, et al. ∙
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Which Strategies Matter for Noisy Label Classification? Insight into Loss and Uncertainty
Label noise is a critical factor that degrades the generalization perfor...
08/14/2020 ∙ by Wonyoung Shin, et al. ∙
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Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs
Graph neural networks have shown superior performance in a wide range of...
07/16/2020 ∙ by Dasol Hwang, et al. ∙
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Multi-Manifold Learning for Large-scale Targeted Advertising System
Messenger advertisements (ads) give direct and personal user experience ...
07/05/2020 ∙ by Kyuyong Shin, et al. ∙
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Graphs, Entities, and Step Mixture
Existing approaches for graph neural networks commonly suffer from the o...
05/18/2020 ∙ by Kyuyong Shin, et al. ∙
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Which Ads to Show? Advertisement Image Assessment with Auxiliary Information via Multi-step Modality Fusion
Assessing aesthetic preference is a fundamental task related to human co...
10/06/2019 ∙ by Kyung-Wha Park, et al. ∙
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DNA Steganalysis Using Deep Recurrent Neural Networks
The technique of hiding messages in digital data is called a steganograp...
04/27/2017 ∙ by Ho Bae, et al. ∙
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