239 citations · 374 across the 20 of their papers we have counts for
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stat.ML2019
Modeling and Forecasting Art Movements with CGANs
Edoardo Lisi, Mohammad Malekzadeh, Hamed Haddadi +2
Conditional Generative Adversarial Networks~(CGAN) are a recent and popular method for generating samples from a probability distribution conditioned on latent information. The lat…
stat.ML2019
Interpreting Deep Neural Networks Through Variable Importance
Jonathan Ish-Horowicz, Dana Udwin, Seth Flaxman +2
While the success of deep neural networks (DNNs) is well-established across a variety of domains, our ability to explain and interpret these methods is limited. Unlike previously p…