Browsing by Subject "Data Augmentation"
Now showing items 1-2 of 2
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Automatic Machine Learning in Optimization and Data Augmentation
(2022-05)This dissertation introduces Automatic Machine Learning (AutoML) as a potential approach to overcome current deep learning challenges on efficiency and cost. It also proposes two novel AutoML workflows to areas in deep ... -
Investigating the potential of synthetic data for enabling AI-based zero-touch network automation
(2021)The essence and importance of rich and relevant data can not be overemphasized in the field of artificial intelligence. From machine learning to deep learning models, the performance of a model is majorly dependent on the ...