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The paper proposes A*-Inspired Batch Selection (A*-BS), a lightweight strategy that treats mini-batch scheduling as a heuristic search to improve CNN training efficiency. On MedMNIST tasks, a simple CNN with A*-BS outperforms deeper ResNet-18/50 baselines in accuracy and training speed.
Explains how to use the A* algorithm for pathfinding around circular obstacles by converting the environment into a graph using tangent visibility and bitangent lines.
This Twitter thread highlights Stanford CS221 lecture 6 on heuristics, explaining how A* search improves agent efficiency by using heuristics to guide decision-making. Key takeaways include building heuristics by relaxing problems, the danger of bad heuristics, and the optimality of A* with the right estimate.
This paper models the Resource-Constrained Project Scheduling Problem as optimal search over a Petri net reachability graph and solves it with A* guided by a consistent heuristic combining critical path and resource lower bounds, outperforming MIP baselines on PSPLIB benchmarks.