Two papers presented at NeurIPS delve into the challenges of optimal ordering in computation graphs, focusing on neural topological ordering and batch Bayesian optimization. A novel attention-based graph neural network, called topoformer, effectively combines local and global graph information, outperforming traditional algorithms in both synthetic and real-world scenarios. The discussion highlights the complexity of executing operations efficiently while adhering to precedence constraints.
