[AI Seminar] AI Seminar sponsored by Apple -- Simon Du -- March 27

[Adams Wei Yu]

A gentle reminder that the talk will be tomorrow (Tuesday) noon at *GHC
6115 (unusual room).*

On Sat, Mar 24, 2018 at 8:50 PM, Adams Wei Yu <weiyu at cs.cmu.edu> wrote:

> Dear faculty and students,
>
> We look forward to seeing you next Tuesday, March 27, at noon in *GHC
> 6115 (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,  Simon Du <http://www.cs.cmu.edu/~ssdu/> will give the
> following talk:
>
> Title:  On the Power of Randomly Initialized Gradient Descent for
> Learning Convolutional Neural Networks
>
> Abstract:
>
> Convolutional neural networks trained by randomly initialized (stochastic)
> gradient descent have achieved the state-of-art performances in many
> applications. However, its theoretical properties remain elusive from an
> optimization point of view. In this talk, I will present two results on
> explaining the success of gradient descent.
>
> In the first part, I will show under certain structural conditions of the
> input distribution, randomly initialized gradient descent provably learns a
> convolutional filter with ReLU activation and average pooling. This is the
> first recovery guarantee of gradient-based algorithms for learning a
> convolutional filter on general input distributions.
>
> In the second part of the talk, I will show if the input distribution is
> Gaussian, then randomly initialized gradient descent with
> weight-normalization learns a ReLU activated one-hidden-layer convolutional
> neural network where both the convolutional weights and the output weights
> are to be optimized. To the best our knowledge, this is the first recovery
> guarantee of randomly initialized gradient-based algorithms for neural
> networks that contain more than one layers to be learned.
>
> This talk is based on works with Jason D. Lee, Barnabas Poczos, Aarti
> Singh and Yuandong Tian.
>
>
> Bio:
> Simon Shaolei Du is a PhD student in the Machine Learning Department at
> the School of Computer Science, Carnegie Mellon University, advised by
> Professor Aarti Singh and Professor Barnabas Poczos. His research interests
> broadly include topics in theoretical machine learning and statistics, such
> as deep learning, matrix factorization, convex/non-convex optimization,
> transfer learning, reinforcement learning, non-parametric statistics and
> robust statistics. Currently he is also developing methods for precision
> agriculture. In 2011, he earned his high school degree from The
> Experimental High School Attached to Beijing Normal University. In 2015, he
> obtained his B.S. in Engineering Math & Statistics and B.S. in Electrical
> Engineering & Computer Science from University of California, Berkeley. He
> has also spent time working at research labs of Microsoft and Facebook.
>
>
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