[AI Seminar] AI Seminar sponsored by Apple -- Simon Shaolei Du

[Han Zhao]

Dear faculty and students:

We look forward to seeing you next Tuesday, Feb. 19th, at noon in *NSH
3305 *for our AI Seminar sponsored by Apple. To learn more about the
seminar series,
please visit the website.
On Tuesday, Simon Shaolei Du will give the following talk:
*Title: Understanding Optimization and Generalization in Deep Learning: A
Trajectory-based Analysis*

*Abstract*: In this talk, I will present recent progress on understanding
deep neural networks by analyzing the trajectory of the gradient descent
algorithm. Using this analysis technique, we are able to explain:
1) why gradient descent finds a global minimum of the training loss even
though the objective function is highly non-convex, and
2) why a neural network can generalize even the number of parameters in the
neural network is more than the number of training data.

Based on joint work with Sanjeev Arora,  Wei Hu, Jason D. Lee, Haochuan Li,
Zhiyuan Li, Barnabas Poczos, Aarti Singh, Liwei Wang, Ruosong Wang, Xiyu
Zhai

References:
https://arxiv.org/abs/1810.02054
https://arxiv.org/abs/1811.03804
https://arxiv.org/abs/1901.08584

Bio:
Simon Shaolei Du is a Ph.D. student in the Machine Learning Department at
the School of Computer Science, Carnegie Mellon University, advised by
Professor Aarti Singh and Professor Barnabás Póczos.  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. In 2015, he obtained his B.S. in
Engineering Math & Statistics and B.S. in Electrical Engineering & Computer
Science from the University of California, Berkeley. He has also spent time
working at research labs of Microsoft and Facebook.
-- 

*Han ZhaoMachine Learning Department*


*School of Computer ScienceCarnegie Mellon UniversityMobile: +1-*
*412-652-4404*
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