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 ...
Understanding a microprocessor and the evolution of hardware
Underrated ML
1 hour 10 minutes
3 years ago
Understanding a microprocessor and the evolution of hardware
This week we are joined by Julius Adebayo. Julius is a CS PhD student at MIT, interested in safe deployment of ML based systems as it relates to privacy/security, interpretability, fairness and robustness. He is motivated by the need to ensure that ML based systems demonstrate safe behaviour when deployed. On this weeks episode we discuss how the evolution of hardware has progressed overtime and what that means for deep learning research. We also analyse how microprocessors can aid developme...
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 ...
