12 citations · 13 across the 4 of their papers we have counts for
4 papers · 1 filter
The Companion Model -- a Canonical Model in Graph Signal Processing
John Shi, Jose M. F. Moura
This paper introduces a graph signal model defined by a graph and a shift, the graph and the $\t…
Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology
Mark Cheung, John Shi, Oren Wright +3
Deep learning, particularly convolutional neural networks (CNNs), have yielded rapid, significant improvements in computer vision and related domains. But conventional deep learnin…
Pooling in Graph Convolutional Neural Networks
Mark Cheung, John Shi, Lavender Yao Jiang +2
Graph convolutional neural networks (GCNNs) are a powerful extension of deep learning techniques to graph-structured data problems. We empirically evaluate several pooling methods…
Graph Signal Processing: Modulation, Convolution, and Sampling
John Shi, Jose M. F. Moura
To analyze data supported by arbitrary graphs G, DSP has been extended to Graph Signal Processing (GSP) by redefining traditional DSP concepts like shift, filtering, and Fourier tr…