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.
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.
The AI Seminar is generously sponsored by Apple.