Advances in Deep Generative Modeling With Applications to Image Generation and Neuroscience
Deep generative modeling is an increasingly popular area of machine learning that takes advantage of recent developments in neural networks in order to estimate the distribution of observed data. In this dissertation we introduce three advances in this area. The first one, Maximum Entropy Flow Netwo...
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| Language: | English |
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2019
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| Online Access: | https://doi.org/10.7916/d8-yp88-e002 |