Dissertation Defense Schedule

Academic Excellence

Sharing original dissertation research is a principle to which the University of Delaware is deeply committed. It is the single most important assignment our graduate students undertake and upon completion is met with great pride.

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PROGRAM | Electrical & Computer Engineering

Applications and Evaluation of Deep Generative Augmentation Methods

By: Pasquale Zingo Chair: Andrew Novocin

ABSTRACT

High quality datasets drive the success of machine learning applications; however, quality datasets are not always available as financial, security, and temporal factors all contribute to the difficulty of publishing datasets sufficient for machine learning applications. Often when data is present but insufficient, data augmentation techniques are utilized in order to improve the performance of predictors. While data augmentation methods are well understood and have been used effectively in the image domain, it is often unclear how to create variations on tabular and time series data that do not lose important patterns in the new synthetic data.

 

In this work, we investigate the effectiveness of generative machine learning to augment tabular and time series datasets. To do so we introduce a novel generative model tool, the GvR metric, to compare different generators in their capacity for augmentation. We also create a new methodology for creating comparative visualizations of real and generated datasets. Finally, we demonstrate the abstract efficacy of these methods by applying them in widely disparate domains: network traffic classification and predicting streamflow for a local river system.

 

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Dissertation Manual

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Defense Submission Form

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