자료유형  

 학위논문

Control Number  

0017164314

International Standard Book Number  

9798342130127

Dewey Decimal Classification Number  

519.54

Main Entry-Personal Name  

Cardona, Daniel Antonio.

Publication, Distribution, etc. (Imprint  

[S.l.] : Purdue University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

64 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

General Note  

Advisor: Sabbaghi, Arman;Rao, Vinayak A.P.

Dissertation Note  

Thesis (Ph.D.)--Purdue University, 2024.

Summary, Etc.  

요약As machine learning continues to grow and surprise us, its complexity grows as well. Indeed, many machine learning models have become black boxes. Yet, there is a prevailing need for practicality. This dissertation offers some practicality on generative modeling and synthetic data, a recently popular application of generative models. First, Lightweight Chained Universal Approximators (LiCUS) is proposed. Motivated by statistical sampling principles, LiCUS tackles a simplified generative task with its universal approximation property while having a minimal computational bottleneck. When compared to a generative adversarial network (GAN) and variational auto-encoder (VAE), LiCUS empirically yields synthetic data with greater utility for a classifier on the Modified National Institute of Standards and Technology (MNIST) dataset. Second, following on its potential for informative synthetic data, LiCUS undergoes an extensive synthetic data supplementation experiment. The experiment largely serves as an informative starting point for practical use of synthetic data via LiCUS. In addition, by proposing a gold standard of reserved data, the experimental results suggest that additional data collection may generally outperform models supplemented with synthetic data, at least when using LiCUS. Given that the experiment was conducted on two datasets, future research could involve further experimentation on a greater number and variety of datasets, such as images. Lastly, generative machine learning generally demands large datasets, which is not guaranteed in practice. To alleviate this demand, one could offer expert knowledge. This is demonstrated by applying an expert-informed Wasserstein GAN with gradient penalty (WGAN-GP) on network flow traffic from NASA's Operational Simulation for Small Satellites (NOS3). If one were to directly apply a WGAN-GP, it would fail to respect the physical limitations between satellite components and permissible communications amongst them. By arming a WGAN-GP with cyber-security software Argus, the informed WGAN-GP could produce permissible satellite network flows when given as little as 10,000 flows. In all, this dissertation illustrates how machine learning processes could be modified under a more practical lens and incorporate pre-existing statistical principles and expert knowledge.

Subject Added Entry-Topical Term  

Normal distribution.

Subject Added Entry-Topical Term  

Neural networks.

Subject Added Entry-Topical Term  

Satellites.

Subject Added Entry-Topical Term  

Markov analysis.

Subject Added Entry-Topical Term  

Aerospace engineering.

Added Entry-Corporate Name  

Purdue University.

Host Item Entry  

Dissertations Abstracts International. 86-04B.

Electronic Location and Access  

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Control Number  

joongbu:657640