Artificial Intelligence has suddenly gone from the fringes of science to being everywhere. So how did we get here? And where's this all heading? In this new series of Science Friction, we're finding out.
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[23] Simon Du - Gradient Descent for Non-convex Problems in Modern Machine Learning
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Manage episode 302418422 series 2982803
Content provided by The Thesis Review and Sean Welleck. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Thesis Review and Sean Welleck or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Simon Shaolei Du is an Assistant Professor at the University of Washington. His research focuses on theoretical foundations of deep learning, representation learning, and reinforcement learning. Simon's PhD thesis is titled "Gradient Descent for Non-convex Problems in Modern Machine Learning", which he completed in 2019 at Carnegie Mellon University. We discuss his work related to the theory of gradient descent for challenging non-convex problems that we encounter in deep learning. We cover various topics including connections with the Neural Tangent Kernel, theory vs. practice, and future research directions. Episode notes: https://cs.nyu.edu/~welleck/episode23.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview
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47 episodes
MP3•Episode home
Manage episode 302418422 series 2982803
Content provided by The Thesis Review and Sean Welleck. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Thesis Review and Sean Welleck or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Simon Shaolei Du is an Assistant Professor at the University of Washington. His research focuses on theoretical foundations of deep learning, representation learning, and reinforcement learning. Simon's PhD thesis is titled "Gradient Descent for Non-convex Problems in Modern Machine Learning", which he completed in 2019 at Carnegie Mellon University. We discuss his work related to the theory of gradient descent for challenging non-convex problems that we encounter in deep learning. We cover various topics including connections with the Neural Tangent Kernel, theory vs. practice, and future research directions. Episode notes: https://cs.nyu.edu/~welleck/episode23.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview
…
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47 episodes
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