Post #271

DeepMind's Traffic Prediction with Advanced Graph Neural Networks A new blog post by DeepMind has been released recently that describes how you can apply GNN for travel time predictions. There are not many details about the model itself (which makes me wonder if deep net trained across all supersegments would suffice), but there are curious details about training. 1. As the road network is huge I suppose, they use sampling sampling of subgraphs in proportion to traffic density. This should be similar to GraphSAGE-like approaches. 2. Sampled subgraphs can vary a lot in a single batch. So they use RL to select subgraph properly. I guess it's some form of imitation learning that selects graphs in a batch based on some objective value. 3. They use MetaGradients algorithm to select a learning rate, which was previously used to parametrize returns in RL. I guess it parametrizes learning rate instead in this blog post.