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.
Online Learning and Information Exponents : On The Importance of Batch size, and Time / Complexity Tradeoffs , June 2024 a
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.