[AI Seminar] AI Seminar -- Kenji Kawaguchi, Special Priorities in Deep Learning and AI

[Han Zhao]

Dear faculty and students:

We look forward to seeing you next Tuesday, Oct. 22nd, at noon in *NSH
3305 *for our AI Seminar sponsored by Apple. To learn more about the
seminar series,
please visit the website <http://www.cs.cmu.edu/~aiseminar/>.
On Tuesday, Kenji Kawaguchi will give the following talk:
*Title: *Special Priorities in Deep Learning and AI

*Abstract:* Deep learning and AI have provided high-impact data-driven
methods in various applications. However, theoretical guarantees on deep
learning and AI tend to provide too pessimistic insights with a gap from
practical observations, because of hidden special properties of deep
learning and AI problems. Identifying such special properties can provide
novel theoretical insights, and is potentially helpful for designing
methods and deriving better guarantees. In this talk, I will discuss
special properties on non-convex optimization landscapes of deep neural
networks and machine learning models, as well as their implications on
gradient descent methods and the results on real-world applications based
on theoretical insights.

*Bio:* Kenji Kawaguchi is a Ph.D. candidate at Massachusetts Institute of
Technology (MIT), advised by Prof. Leslie Pack Kaelbling. He received his
M.S. in Electrical Engineering and Computer Science from MIT. His research
interests span machine learning, deep learning, artificial intelligence,
convex/nonconvex optimization and Bayesian optimization. His research has
been cited widely in academia and used in classes. He was invited to speak
at the 2019 International Congress on Industrial and Applied Mathematics
Minisymposium on Theoretical Foundations of Deep Learning. In 2018, he was
invited for a summer research visit at Microsoft Research in Redmond. He
was awarded the Funai Overseas Scholarship in 2014 and was selected for the
Nakajimi Foundation Fellowship in 2013.
-- 

*Han ZhaoMachine Learning Department*


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