A. Yu. Drozdov

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Interpretable variables are useful in generative models. Generative Adversarial Networks (GANs) are generative models that are flexible in their input. The Information Maximizing GAN (InfoGAN) ties the output of the generator to a component of its input called the latent codes. By forcing the output to be tied to this input component, we can control some(More)
The paper is devoted to the integration of the compiler based on the LLVM library with the tools created using the Universal Translation Library (UTL)—automatic parallelizer and vectorizer. The intermediate representations used in the libraries to be integrated are analyzed and compared. Mechanisms which had to be implemented for integration are described.(More)
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