@inproceedings{c627c7e4960e476585862e6987f0ca8d,
title = "Autonomous Learning Rate Optimization for Deep Learning",
abstract = "A significant question in deep learning is: what should that learning rate be? The answer to this question is often tedious and time consuming to obtain, and a great deal of arcane knowledge has accumulated in recent years over how to pick and modify learning rates to achieve optimal training performance. Moreover, the long hours spent carefully crafting the perfect learning rate can be more demanding than optimizing network architecture itself. Advancing automated machine learning, we propose a new answer to the great learning rate question: the Autonomous Learning Rate Controller. Source code is available at https://github.com/fastestimator/ARC/tree/v1.0.",
keywords = "AutoML, Deep Learning, Learning Rate, Optimization",
author = "Xiaomeng Dong and Tao Tan and Michael Potter and Tsai, \{Yun Chan\} and Gaurav Kumar and Saripalli, \{V. Ratna\} and Theodore Trafalis",
year = "2022",
doi = "10.1007/978-3-031-24866-5\_22",
language = "English",
isbn = "9783031248658",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "292--305",
editor = "Simos, \{Dimitris E.\} and Rasskazova, \{Varvara A.\} and Francesco Archetti and Kotsireas, \{Ilias S.\} and Pardalos, \{Panos M.\}",
booktitle = "Learning and Intelligent Optimization - 16th International Conference, LION 16 2022, Revised Selected Papers",
address = "Germany",
note = "16th International Conference on Learning and Intelligent Optimization, LION 16 2022 ; Conference date: 05-06-2022 Through 10-06-2022",
}