Training generative neural networks via Maximum Mean Discrepancy optimization
Gintare Karolina Dziugaite; Daniel M. Roy; Zoubin Ghahramani · 2015 · arXiv (Cornell University)
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
We consider training a deep neural network to generate samples from an unknown distribution given i.i.d. data. We frame learning as an optimization minimizing a two-sample test statistic---informally speaking, a good generator network…
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Metadata source: OpenAlex · DOI 10.48550/arxiv.1505.03906