Harmonic Analysis and Machine Learning
| dc.contributor.advisor | Czaja, Wojciech | en_US |
| dc.contributor.advisor | Levy, Doron | en_US |
| dc.contributor.author | Pekala, Michael | en_US |
| dc.contributor.department | Applied Mathematics and Scientific Computation | en_US |
| dc.contributor.publisher | Digital Repository at the University of Maryland | en_US |
| dc.contributor.publisher | University of Maryland (College Park, Md.) | en_US |
| dc.date.accessioned | 2019-06-22T05:31:08Z | |
| dc.date.available | 2019-06-22T05:31:08Z | |
| dc.date.issued | 2018 | en_US |
| dc.description.abstract | This dissertation considers data representations that lie at the interesection of harmonic analysis and neural networks. The unifying theme of this work is the goal for robust and reliable machine learning. Our specific contributions include a new variant of scattering transforms based on a Haar-type directional wavelet, a new study of deep neural network instability in the context of remote sensing problems, and new empirical studies of biomedical applications of neural networks. | en_US |
| dc.identifier | https://doi.org/10.13016/jz7j-erqc | |
| dc.identifier.uri | http://hdl.handle.net/1903/22151 | |
| dc.language.iso | en | en_US |
| dc.subject.pqcontrolled | Applied mathematics | en_US |
| dc.subject.pquncontrolled | machine learning | en_US |
| dc.title | Harmonic Analysis and Machine Learning | en_US |
| dc.type | Dissertation | en_US |
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