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  • title: Metropolis-Hastings Data Augmentation for Graph Neural Networks
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            Metropolis-Hastings Data Augmentation for Graph Neural Networks
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            Metropolis-Hastings Data Augmentation for Graph Neural Networks

            Dez 6, 2021

            Sprecher:innen

            HP

            Hyeonjin Park

            Sprecher:in · 0 Follower:innen

            SL

            Seunghun Lee

            Sprecher:in · 0 Follower:innen

            SK

            Sihyeon Kim

            Sprecher:in · 0 Follower:innen

            Über

            Graph Neural Networks (GNNs) often suffer from weak-generalization due to sparsely labeled data despite their promising results on various graph-based tasks. Data augmentation is a prevalent remedy to improve the generalization ability of models in many domains. However, due to the non-Euclidean nature of data space and the dependencies between samples, designing effective augmentation on graphs is challenging. In this paper, we propose a novel framework Metropolis-Hastings Data Augmentation (MH…

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            NeurIPS 2021

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            Mathematik

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            KI und Datenwissenschaft

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            Über NeurIPS 2021

            Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.

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