Autonomous Learning Rate Optimization for Deep Learning

Xiaomeng Dong, Tao Tan, Michael Potter, Yun Chan Tsai, Gaurav Kumar, V. Ratna Saripalli, Theodore Trafalis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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

Original languageEnglish
Title of host publicationLearning and Intelligent Optimization - 16th International Conference, LION 16 2022, Revised Selected Papers
EditorsDimitris E. Simos, Varvara A. Rasskazova, Francesco Archetti, Ilias S. Kotsireas, Panos M. Pardalos
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages14
ISBN (Print)9783031248658
Publication statusPublished - 2022
Externally publishedYes
Event16th International Conference on Learning and Intelligent Optimization, LION 16 2022 - Milos Island, Greece
Duration: 5 Jun 202210 Jun 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13621 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Conference on Learning and Intelligent Optimization, LION 16 2022
CityMilos Island


  • AutoML
  • Deep Learning
  • Learning Rate
  • Optimization


Dive into the research topics of 'Autonomous Learning Rate Optimization for Deep Learning'. Together they form a unique fingerprint.

Cite this