Matus Telgarsky (2016)
arXiv.
DOI: https://doi.org/10.48550/arxiv.1602.04485
Abstract. Constructs explicit functions that can be computed by a deep network with polynomial width but require exponential width for any network of lesser depth, a rigorous demonstration of depth separation in neural networks.
Tags: neural-networks theory depth