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]](https://bibbase.org/img/filetypes/link.svg "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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