ReLU networks' division of input space into convex polytopal regions permits direct extraction of causal rules that exactly match the original network's linear behavior in each region.
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Causal Explanations from the Geometric Properties of ReLU Neural Networks
ReLU networks' division of input space into convex polytopal regions permits direct extraction of causal rules that exactly match the original network's linear behavior in each region.