HNNs recover known sparse hierarchies on synthetic tasks and match or exceed dense DNNs on real datasets while using orders of magnitude fewer parameters and showing lower hyperparameter sensitivity.
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Compositional Sparsity as an Inductive Bias for Neural Architecture Design
HNNs recover known sparse hierarchies on synthetic tasks and match or exceed dense DNNs on real datasets while using orders of magnitude fewer parameters and showing lower hyperparameter sensitivity.