We propose spectral-inspired GNNs that exploit the advantages from both global and local methods.
A simple generative model in which spectral graph embedding for subsequent inference succeeds whereas unsupervised GCN fails.
A CNN encoder-decoder model to navigate traffic environment using bird's eye view images.
Our work generalizes adversarial attacks in regression settings and develops a comprehensive approach to measure attack success.