@inproceedings{f2556c0a7a8442718214e835050730ac,
title = "AdaNet: Adaptive structural learning of artificial neural networks",
abstract = "We present new algorithms for adaptively learning artificial neural networks. Our algorithms (AdaNet) adaptively learn both the structure of the network and its weights. They are based on a solid theoretical analysis, including data-dependent generalization guarantees that we prove and discuss in detail. We report the results of large-scale experiments with one of our algorithms on several binary classification tasks extracted from the CIFAR-10 dataset and on the Criteo dataset. The results demonstrate that our algorithm can automatically learn network structures with very competitive performance accuracies when compared with those achieved by neural networks found by standard approaches.",
author = "Corinna Cortes and Xavier Gonzalvo and Vitaly Kuznetsov and Mehryar Mohri and Scott Yang",
note = "Publisher Copyright: {\textcopyright} 2017 by the author (s).; 34th International Conference on Machine Learning, ICML 2017 ; Conference date: 06-08-2017 Through 11-08-2017",
year = "2017",
language = "English (US)",
series = "34th International Conference on Machine Learning, ICML 2017",
publisher = "International Machine Learning Society (IMLS)",
pages = "1452--1466",
booktitle = "34th International Conference on Machine Learning, ICML 2017",
}