126 citations · 402 across the 32 of their papers we have counts for
5 papers · 1 filter
Spatio-Temporal Graph Scattering Transform
Chao Pan, Siheng Chen, Antonio Ortega
Although spatio-temporal graph neural networks have achieved great empirical success in handling multiple correlated time series, they may be impractical in some real-world scenari…
Wireless 3D Point Cloud Delivery Using Deep Graph Neural Networks
Takuya Fujihashi, Toshiaki Koike-Akino, Siheng Chen +1
In typical point cloud delivery, a sender uses octree-based digital video compression to send three-dimensional (3D) points and color attributes over band-limited links. However, t…
Efficient and Stable Graph Scattering Transforms via Pruning
Vassilis N. Ioannidis, Siheng Chen, Georgios B. Giannakis
Graph convolutional networks (GCNs) have well-documented performance in various graph learning tasks, but their analysis is still at its infancy. Graph scattering transforms (GSTs)…
Large-scale 3D point cloud representations via graph inception networks with applications to autonomous driving
Siheng Chen, Sufeng. Niu, Tian Lan +1
We present a novel graph-neural-network-based system to effectively represent large-scale 3D point clouds with the applications to autonomous driving. Many previous works studied t…
Multiresolution Representations for Piecewise-Smooth Signals on Graphs
Siheng Chen, Aarti Singh, Jelena Kovačević
What is a mathematically rigorous way to describe the taxi-pickup distribution in Manhattan, or the profile information in online social networks? A deep understanding of represent…