<div dir="ltr">A gentle reminder that the talk will happen tomorrow (Tuesday) noon in NSH 1507 (unusual place).</div><div class="gmail_extra"><br><div class="gmail_quote">On Sun, Nov 5, 2017 at 10:00 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 -- Hanxiao Liu -- Nov 07<br>
      (Adams Wei Yu)<br>
<br>
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Message: 1<br>
Date: Sat, 4 Nov 2017 05:13:58 -0600<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>
Subject: [AI Seminar] AI Seminar sponsored by Apple -- Hanxiao Liu --<br>
        Nov 07<br>
Message-ID:<br>
        <CABzq7ep-Wcy=<a href="mailto:9x%2BGLOx91jNOnJW6YdEmu%2B6YAaGNxjAoONm51w@mail.gmail.com">9x+<wbr>GLOx91jNOnJW6YdEmu+<wbr>6YAaGNxjAoONm51w@mail.gmail.<wbr>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, Nov 07, 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, Hanxiao Liu <<a href="http://www.cs.cmu.edu/~hanxiaol/" rel="noreferrer" target="_blank">http://www.cs.cmu.edu/~<wbr>hanxiaol/</a>> will give the<br>
following talk:<br>
<br>
Title: Hierarchical Representations for Efficient Architecture Search<br>
<br>
Abstract:<br>
<br>
We explore efficient neural architecture search methods and present a<br>
simple yet powerful evolutionary algorithm that can discover new<br>
architectures achieving state of the art results. Our approach combines a<br>
novel hierarchical genetic representation scheme that imitates the<br>
modularized design pattern commonly adopted by human experts, and an<br>
expressive search space that supports complex topologies. Our algorithm<br>
efficiently discovers architectures that outperform a large number of<br>
manually designed models for image classification, obtaining top-1 error of<br>
3.6% on CIFAR-10 and 20.3% when transferred to ImageNet, which is<br>
competitive with the best existing neural architecture search approaches<br>
and represents the new state of the art for evolutionary strategies on this<br>
task. We also present results using random search, achieving 0.3% less<br>
top-1 accuracy on CIFAR-10 and 0.1% less on ImageNet whilst reducing the<br>
architecture search time from 36 hours down to 1 hour.<br>
<br>
This is joint work with Karen Simonyan, Oriol Vinyals, Chrisantha Fernando<br>
and Koray Kavukcuoglu at DeepMind.<br>
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End of ai-seminar-announce Digest, Vol 78, Issue 1<br>
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