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Pathfinder Discovery Networks for Neural Message Passing
In this work we propose Pathfinder Discovery Networks (PDNs), a method f...
10/24/2020 ∙ by Benedek Rozemberczki, et al. ∙
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Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
In this work, we examine a novel forecasting approach for COVID-19 case ...
07/06/2020 ∙ by Amol Kapoor, et al. ∙
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Scaling Graph Neural Networks with Approximate PageRank
Graph neural networks (GNNs) have emerged as a powerful approach for sol...
07/03/2020 ∙ by Aleksandar Bojchevski, et al. ∙
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Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks
We investigate under and overfitting in Generative Adversarial Networks ...
10/30/2019 ∙ by Ben Adlam, et al. ∙
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Nostalgin: Extracting 3D City Models from Historical Image Data
What did it feel like to walk through a city from the past? In this work...
05/06/2019 ∙ by Amol Kapoor, et al. ∙
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MixHop: Higher-Order Graph Convolution Architectures via Sparsified Neighborhood Mixing
Existing popular methods for semi-supervised learning with Graph Neural ...
04/30/2019 ∙ by Sami Abu-El-Haija, et al. ∙
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N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification
Graph Convolutional Networks (GCNs) have shown significant improvements ...
02/24/2018 ∙ by Sami Abu-El-Haija, et al. ∙
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