@article {4433,
	title = {Complexity Control by Gradient Descent in Deep Networks},
	journal = {Nature Communications},
	volume = {11},
	year = {2020},
	month = {02/2020},
	abstract = {<p>Overparametrized deep network predict well despite the lack of an explicit complexity control during training such as an explicit regularization term. For exponential-type loss functions, we solve this puzzle by showing an effective regularization effect of gradient descent in terms of the normalized weights that are relevant for classification.</p>
},
	doi = {https://doi.org/10.1038/s41467-020-14663-9},
	url = {https://www.nature.com/articles/s41467-020-14663-9},
	author = {Tomaso Poggio and Qianli Liao and Andrzej Banburski}
}
