AMC: Adaptive Learning Rate Adjustment Based on Model Complexity
AMC: Adaptive Learning Rate Adjustment Based on Model Complexity
About this item
Full title
Author / Creator
Cheng, Weiwei , Pu, Rong and Wang, Bin
Publisher
Basel: MDPI AG
Journal title
Language
English
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Publication information
Publisher
Basel: MDPI AG
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More information
Scope and Contents
Contents
An optimizer plays a decisive role in the efficiency and effectiveness of model training in deep learning. Although Adam and its variants are widely used, the impact of model complexity on training is not considered, which leads to instability or slow convergence when a complex model is trained. To address this issue, we propose an AMC (Adam with M...
Alternative Titles
Full title
AMC: Adaptive Learning Rate Adjustment Based on Model Complexity
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_9464ee3def414b9c840896cd60fbf1d1
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_9464ee3def414b9c840896cd60fbf1d1
Other Identifiers
ISSN
2227-7390
E-ISSN
2227-7390
DOI
10.3390/math13040650
