Abstract: Graph convolutional networks (GCNs) have become a powerful deep learning approach for graph-structured data. Different from traditional neural networks such as convolutional neural networks, ...
Looking back, 2016 was an incredible year for Hip Hop and R&B. We saw major albums drop from Drake, Beyoncé, and Kanye West, ...
Abstract: The existence of missing values in time-series data has brought great inconvenience to our lives. In this paper, we propose a Stacked Spatio-Temporal Graph ...
pggb builds pangenome variation graphs from a set of input sequences. A pangenome variation graph is a kind of generic multiple sequence alignment. It lets us understand any kind of sequence variation ...
F. Gama, A. G. Marques, G. Leus, and A. Ribeiro, "Convolutional Neural Network Architectures for Signals Supported on Graphs," IEEE Trans. Signal Process., vol. 67 ...
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