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Underrated ML
Sara Hooker & Sean Hooker
10 episodes
8 months ago
This week we are joined by Ari Morcos. Ari is a research scientist at Facebook AI Research (FAIR) in Menlo Park working on understanding the mechanisms underlying neural network computation and function, and using these insights to build machine learning systems more intelligently. In particular, he has worked on a variety of topics, including understanding the lottery ticket hypothesis, self-supervised learning, the mechanisms underlying common regularizers, and the properties predictive of ...
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Technology
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All content for Underrated ML is the property of Sara Hooker & Sean Hooker and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
This week we are joined by Ari Morcos. Ari is a research scientist at Facebook AI Research (FAIR) in Menlo Park working on understanding the mechanisms underlying neural network computation and function, and using these insights to build machine learning systems more intelligently. In particular, he has worked on a variety of topics, including understanding the lottery ticket hypothesis, self-supervised learning, the mechanisms underlying common regularizers, and the properties predictive of ...
Show more...
Technology
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Abstract Reasoning and Peer Reviews
Underrated ML
1 hour 15 minutes
3 years ago
Abstract Reasoning and Peer Reviews
This weeks episode we take a look at Abstract Reasoning within Neural Networks as well as discussing the current review system surrounding ML papers. We are also very happy to have Jacob Buckman join us on the podcast this week. Jacob is currently undertaking a PhD at Mila having previously been a researcher at Google Brain with Sara Hooker. His main research interests lie in deep reinforcement learning with a particular focus on sample-efficiency. Please let us know who you thought prese...
Underrated ML
This week we are joined by Ari Morcos. Ari is a research scientist at Facebook AI Research (FAIR) in Menlo Park working on understanding the mechanisms underlying neural network computation and function, and using these insights to build machine learning systems more intelligently. In particular, he has worked on a variety of topics, including understanding the lottery ticket hypothesis, self-supervised learning, the mechanisms underlying common regularizers, and the properties predictive of ...