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%0 Journal Article
%1 poggio2017deepbut
%A Poggio, Tomaso
%A Mhaskar, Hrushikesh
%A Rosasco, Lorenzo
%A Miranda, Brando
%A Liao, Qianli
%D 2017
%J International Journal of Automation and Computing
%K approximate deep-learning generalization theory
%P 1--17
%T Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review
%U https://cbmm.mit.edu/publications/why-and-when-can-deep-not-shallow-networks-avoid-curse-dimensionality-review-0
@article{poggio2017deepbut,
added-at = {2019-10-03T12:35:37.000+0200},
author = {Poggio, Tomaso and Mhaskar, Hrushikesh and Rosasco, Lorenzo and Miranda, Brando and Liao, Qianli},
biburl = {https://www.bibsonomy.org/bibtex/22b688147c794d234e82d8a9de41c5e20/kirk86},
description = {Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review | The Center for Brains, Minds & Machines},
interhash = {99e38247d0adfb5f03c3fb72f78d657e},
intrahash = {2b688147c794d234e82d8a9de41c5e20},
journal = {International Journal of Automation and Computing},
keywords = {approximate deep-learning generalization theory},
pages = {1--17},
timestamp = {2019-10-03T12:35:37.000+0200},
title = {Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review},
url = {https://cbmm.mit.edu/publications/why-and-when-can-deep-not-shallow-networks-avoid-curse-dimensionality-review-0},
year = 2017
}