Configuration entropy serves as a reliable proxy for the learned skills of reinforcement learning agents performing tasks in discrete space, validated through walker encounters and chess engine tests.
SimRank: a measure of structural-context similarity
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Empirical comparison shows gradient-based explanations for GNN node similarities are actionable, consistent, and retain effects when sparsified, unlike mutual information explanations.
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Smart Walkers in Discrete Space
Configuration entropy serves as a reliable proxy for the learned skills of reinforcement learning agents performing tasks in discrete space, validated through walker encounters and chess engine tests.
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Explaining Graph Neural Networks for Node Similarity on Graphs
Empirical comparison shows gradient-based explanations for GNN node similarities are actionable, consistent, and retain effects when sparsified, unlike mutual information explanations.