Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio. Jastrzk ebski, S., Kenton, Z., Arpit, D., Ballas, N., Fischer, A., Bengio, Y., & Storkey, A. J. In Artificial Neural Networks and Machine Learning - ICANN 2018 - 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III, volume 11141, of Lecture Notes in Computer Science, pages 392–402, 2018. Springer.
Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio [link]Paper  doi  bibtex   
@inproceedings{DBLP:conf/icann/JastrzebskiKABF18,
title = {Width of Minima Reached by Stochastic Gradient Descent is Influenced
by Learning Rate to Batch Size Ratio},
author = {Stanis{ł}aw Jastrz{k e}bski and
Zachary Kenton and
Devansh Arpit and
Nicolas Ballas and
Asja Fischer and
Yoshua Bengio and
Amos J. Storkey},
url = {https://doi.org/10.1007/978-3-030-01424-7_39},
doi = {10.1007/978-3-030-01424-7_39},
year  = {2018},
date = {2018-01-01},
booktitle = {Artificial Neural Networks and Machine Learning - ICANN 2018 - 27th
International Conference on Artificial Neural Networks, Rhodes, Greece,
October 4-7, 2018, Proceedings, Part III},
volume = {11141},
pages = {392--402},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {76-8485, sda-pub},
pubstate = {published},
tppubtype = {inproceedings}
}

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