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Episode 15: Martín Arjovsky, INRIA, on benchmarks for robustness and geometric information theory

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Manage episode 304703069 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://podcastplayer.com/legal.

Martín Arjovsky did his Ph.D. at NYU with Leon Bottou. Some of his well-known works include the Wasserstein GAN and a paradigm called Invariant Risk Minimization. In this episode, we discuss out-of-distribution generalization, geometric information theory, and the importance of good benchmarks.

  continue reading

37 episodes

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iconShare
 
Manage episode 304703069 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://podcastplayer.com/legal.

Martín Arjovsky did his Ph.D. at NYU with Leon Bottou. Some of his well-known works include the Wasserstein GAN and a paradigm called Invariant Risk Minimization. In this episode, we discuss out-of-distribution generalization, geometric information theory, and the importance of good benchmarks.

  continue reading

37 episodes

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