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Episode 14: Yash Sharma, MPI-IS, on generalizability, causality, and disentanglement

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Manage episode 303053803 series 2906499
Content provided by Kanjun Qiu. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Kanjun Qiu 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.

Yash Sharma is a Ph.D. student at the International Max Planck Research School for Intelligent Systems. He previously studied electrical engineering at Cooper Union and has spent time at Borealis AI and IBM Research. Yash’s early work was on adversarial examples and his current research interests span a variety of topics in representation disentanglement. In this episode, we discuss robustness to adversarial examples, causality vs. correlation in data, and how to make deep learning models generalize better.

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34 episodes

Artwork
iconShare
 
Manage episode 303053803 series 2906499
Content provided by Kanjun Qiu. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Kanjun Qiu 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.

Yash Sharma is a Ph.D. student at the International Max Planck Research School for Intelligent Systems. He previously studied electrical engineering at Cooper Union and has spent time at Borealis AI and IBM Research. Yash’s early work was on adversarial examples and his current research interests span a variety of topics in representation disentanglement. In this episode, we discuss robustness to adversarial examples, causality vs. correlation in data, and how to make deep learning models generalize better.

  continue reading

34 episodes

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