All polynomial-time computable functions are compositionally sparse, and this property is the proposed reason deep networks avoid the curse of dimensionality and achieve practical success.
Repetita Iuvant : Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions , May 2024 b
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Position: A Theory of Deep Learning Must Include Compositional Sparsity
All polynomial-time computable functions are compositionally sparse, and this property is the proposed reason deep networks avoid the curse of dimensionality and achieve practical success.