Tim delves into the spline theory of neural networks, explaining how neurons act as basis functions in chopping up ambient space. Zach discusses a surprising paper where a simple recipe involving Xgboost and label propagation outperformed many GNNs, emphasizing the importance of error propagation in graph-based models.
