[AI Seminar] ai-seminar-announce Digest, Vol 79, Issue 3

[Adams Wei Yu]

A gentle reminder that the talk will happen tomorrow (Tuesday) noon. This
is the last AI seminar in 2017.

On Sun, Dec 10, 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 -- Veeranjaneyulu  Sadhanala --
>       Dec 12 (Adams Wei Yu)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Sun, 10 Dec 2017 00:06:40 -0800
> From: Adams Wei Yu <weiyu at cs.cmu.edu>
> To: ai-seminar-announce at cs.cmu.edu
> Cc: Veeranjaneyulu Sadhanala <veeranjaneyulus at gmail.com>
> Subject: [AI Seminar] AI Seminar sponsored by Apple -- Veeranjaneyulu
>         Sadhanala -- Dec 12
> Message-ID:
>         <CABzq7epyXB7yEg11M5RFnvt5PQtuhfCdopduPKMptYz620jeJA at mail.
> gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Dear faculty and students,
>
> We look forward to seeing you next Tuesday, Dec 12, at noon in NSH 3305 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, Veeranjaneyulu Sadhanala <http://www.cs.cmu.edu/~vsadhana/>
> will give the following talk:
>
> Title: Escaping saddle points in neural network training and other
> non-convex optimization problems
>
> Abstract:
>
> In non-convex optimization problems, first-order based methods can get
> stuck at saddle points which are not even local minima. The generalization
> error at saddle points is typically large and hence it is important to move
> away from them. We discuss recently developed algorithms to escape saddle
> points. In particular, we discuss gradient descent perturbed with additive
> isotropic noise and Newton method with cubic regularization. They converge
> to \epsilon-second order stationary points (informally, local minima) in
> O(polylog(d) / \epsilon^2) time and O(1/ \epsilon^1.5) iterations
> respectively under some conditions on the structure of the objective
> function.
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