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 ...
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 ...
Have a listen to the first ever Underrated ML podcast! We'll walk you through two papers which we found really interesting followed by a few questions and then finally finishing with our verdict on what we believe was the most underrated paper! Links to the papers can be found below. Critical Learning Periods in Deep Neural Networks - https://arxiv.org/abs/1711.08856 A scalable pipeline for designing reconfigurable organisms - https://www.pnas.org/content/117/4/1853
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 ...
