[AI Seminar] ai-seminar-announce Digest, Vol 77, Issue 6

[Adams Wei Yu]

A gentle reminder that the talk will happen tomorrow (Tuesday) noon in NSH
1507.

On Sun, Oct 22, 2017 at 9:00 AM, <ai-seminar-announce-request at cs.cmu.edu>
wrote:

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