Trends in Reinforcement Learning with Pablo Samuel Castro - #443

Trends in Reinforcement Learning with Pablo Samuel Castro - #443

Today we kick off our annual AI Rewind series joined by friend of the show Pablo Samuel Castro, a Staff Research Software Developer at Google Brain. Pablo joined us earlier this year for a discussion about Music & AI, and his Geometric Perspective on Reinforcement Learning, as well our RL office hours during the inaugural TWIMLfest. In today’s conversation, we explore some of the latest and greatest RL advancements coming out of the major conferences this year, broken down into a few major themes, Metrics/Representations, Understanding and Evaluating Deep Reinforcement Learning, and RL in the Real World. This was a very fun conversation, and we encourage you to check out all the great papers and other resources available on the show notes page.

Jaksot(779)

SLIDE: Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning with Beidi Chen - #356

SLIDE: Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning with Beidi Chen - #356

Beidi Chen is part of the team that developed a cheaper, algorithmic, CPU alternative to state-of-the-art GPU machines. They presented their findings at NeurIPS 2019 and have since gained a lot of att...

12 Maalis 202031min

Advancements in Machine Learning with Sergey Levine - #355

Advancements in Machine Learning with Sergey Levine - #355

Today we're joined by Sergey Levine, an Assistant Professor at UC Berkeley. We last heard from Sergey back in 2017, where we explored Deep Robotic Learning. Sergey and his lab’s recent efforts have be...

9 Maalis 202043min

Secrets of a Kaggle Grandmaster with David Odaibo - #354

Secrets of a Kaggle Grandmaster with David Odaibo - #354

Imagine spending years learning ML from the ground up, from its theoretical foundations, but still feeling like you didn’t really know how to apply it. That’s where David Odaibo found himself in 2015,...

5 Maalis 202041min

NLP for Mapping Physics Research with Matteo Chinazzi - #353

NLP for Mapping Physics Research with Matteo Chinazzi - #353

Predicting the future of science, particularly physics, is the task that Matteo Chinazzi, an associate research scientist at Northeastern University focused on in his paper Mapping the Physics Researc...

2 Maalis 202035min

Metric Elicitation and Robust Distributed Learning with Sanmi Koyejo - #352

Metric Elicitation and Robust Distributed Learning with Sanmi Koyejo - #352

The unfortunate reality is that many of the most commonly used machine learning metrics don't account for the complex trade-offs that come with real-world decision making. This is one of the challenge...

27 Helmi 202056min

High-Dimensional Robust Statistics with Ilias Diakonikolas - #351

High-Dimensional Robust Statistics with Ilias Diakonikolas - #351

Today we’re joined by Ilias Diakonikolas, faculty in the CS department at the University of Wisconsin-Madison, and author of the paper Distribution-Independent PAC Learning of Halfspaces with Massart ...

24 Helmi 202036min

How AI Predicted the Coronavirus Outbreak with Kamran Khan - #350

How AI Predicted the Coronavirus Outbreak with Kamran Khan - #350

Today we’re joined by Kamran Khan, founder & CEO of BlueDot, and professor of medicine and public health at the University of Toronto. BlueDot has been the recipient of a lot of attention for being th...

19 Helmi 202051min

Turning Ideas into ML Powered Products with Emmanuel Ameisen - #349

Turning Ideas into ML Powered Products with Emmanuel Ameisen - #349

Today we’re joined by Emmanuel Ameisen, machine learning engineer at Stripe, and author of the recently published book “Building Machine Learning Powered Applications; Going from Idea to Product.” In ...

17 Helmi 202042min

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