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Found 5 papers, 2 papers with code
A Graph Signal Processing Approach For Real-Time Traffic Prediction In Transportation Networks
Building a prediction model for transportation networks is challenging because spatio-temporal dependencies of traffic data in different roads are complex and the graph constructed from road networks is very large.
Spatially-Coupled Neural Network Architectures
In this work, we leverage advances in sparse coding techniques to reduce the number of trainable parameters in a fully connected neural network.
Semi-Implicit Graph Variational Auto-Encoders
Compared to VGAE, the derived graph latent representations by SIG-VAE are more interpretable, due to more expressive generative model and more faithful inference enabled by the flexible semi-implicit construction.
Variational Graph Recurrent Neural Networks
Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant.
Ranked #2 on
Dynamic Link Prediction
on DBLP Temporal
PolarAir: A Compressed Sensing Scheme for Over-the-Air Federated Learning
The goal is to create a low complexity, linear compression strategy, called PolarAir, that reduces the size of the gradient at the user side to lower the number of channel uses needed to transmit it.
