As deep learning continues to push the boundaries with applications previously thought impossible, it has become more important than ever to reduce the associated resource costs. Data is not always abundant, labelling costs valuable human time, and deep models are demanding of computer hardware. In this dissertation, I will examine questions of minimalism in deep learning. I will show that deep learning can learn with fewer measurements, fewer weights, and less information. With minimalism, deep learning can become even more ubiquitous, succeeding in more applications and on more everyday devices.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/43932 |
| Date | 24 February 2022 |
| Creators | Jensen, Louis |
| Contributors | Chin, Sang (Peter) |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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