<div dir="ltr">A gentle reminder that the talk will happen tomorrow (Tuesday) noon in NSH 1507.</div><div class="gmail_extra"><br><div class="gmail_quote">On Sun, Oct 22, 2017 at 9:00 AM,  <span dir="ltr"><<a href="mailto:ai-seminar-announce-request@cs.cmu.edu" target="_blank">ai-seminar-announce-request@cs.cmu.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Send ai-seminar-announce mailing list submissions to<br>
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Today's Topics:<br>
<br>
   1.  AI Seminar sponsored by Apple -- Zhiting Hu -- October   24<br>
      (Adams Wei Yu)<br>
<br>
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Message: 1<br>
Date: Sat, 21 Oct 2017 18:51:49 -0700<br>
From: Adams Wei Yu <<a href="mailto:weiyu@cs.cmu.edu">weiyu@cs.cmu.edu</a>><br>
To: <a href="mailto:ai-seminar-announce@cs.cmu.edu">ai-seminar-announce@cs.cmu.edu</a><br>
Cc: Zhiting Hu <<a href="mailto:zhitinghu@gmail.com">zhitinghu@gmail.com</a>><br>
Subject: [AI Seminar] AI Seminar sponsored by Apple -- Zhiting Hu --<br>
        October 24<br>
Message-ID:<br>
        <<a href="mailto:CABzq7eq8jD_JaouoBdkBxEz3qfoYJ-vO1HAJJO6S2cJ%2BXmfnww@mail.gmail.com">CABzq7eq8jD_<wbr>JaouoBdkBxEz3qfoYJ-<wbr>vO1HAJJO6S2cJ+Xmfnww@mail.<wbr>gmail.com</a>><br>
Content-Type: text/plain; charset="utf-8"<br>
<br>
Dear faculty and students,<br>
<br>
We look forward to seeing you next Tuesday, October 24, at noon in NSH 1507<br>
(unusual place) for AI Seminar sponsored by Apple. To learn more about the<br>
seminar series, please visit the AI Seminar webpage<br>
<<a href="http://www.cs.cmu.edu/~aiseminar/" rel="noreferrer" target="_blank">http://www.cs.cmu.edu/~<wbr>aiseminar/</a>>.<br>
<br>
On Tuesday, Zhiting Hu <<a href="http://www.cs.cmu.edu/~zhitingh/" rel="noreferrer" target="_blank">http://www.cs.cmu.edu/~<wbr>zhitingh/</a>> will give the<br>
following talk:<br>
<br>
Title: On Unifying Deep Generative Models<br>
<br>
Abstract:<br>
<br>
Deep generative models have achieved impressive success in recent years.<br>
Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs),<br>
as powerful frameworks for deep generative model learning, have largely<br>
been considered as two distinct paradigms and received extensive<br>
independent study respectively. This paper establishes formal connections<br>
between deep generative modeling approaches through a new formulation of<br>
GANs and VAEs. We show that GANs and VAEs involve minimizing KL divergences<br>
of respective posterior and inference distributions with opposite<br>
directions, extending the two learning phases of classic wake-sleep<br>
algorithm, respectively. The unified view provides a powerful tool to<br>
analyze a diverse set of existing model variants, and enables to exchange<br>
ideas across research lines in a principled way. For example, we transfer<br>
the importance weighting method in VAE literatures for improved GAN<br>
learning, and enhance VAEs with an adversarial mechanism for leveraging<br>
generated samples. Quantitative experiments show generality and<br>
effectiveness of the imported extensions.<br>
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Subject: Digest Footer<br>
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End of ai-seminar-announce Digest, Vol 77, Issue 6<br>
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