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Adversarial nets need neither Markov chains nor unrolled approximate inference networks for training or sample generation.

Drawn from what Yoshua Bengio said

SubjectGANs
What this subject means

GANs Two networks trained against each other, one generating fake examples and one judging them, until the generator's output is convincing.

What Yoshua Bengio actually said

Word for word, with the source under each one. They did not write this page.

  1. Yoshua Bengio

    Deep-learning researcher and Turing Award laureate

    There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples.

    Where this was said

    ↗Generative Adversarial Networks (with 7 co-authors)arxiv.org

    All 2 korrents from this piecethis one is 1st

Added to korrents 10 Jun 2014 · How quotes work · Something wrong? Tell us

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