Chelsea Finn on Meta Learning & Model Based Reinforcement Learning
The Gradient: Perspectives on AI · Andrey Kurenkov
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Show Notes
In episode 13 of The Gradient Podcast, we interview Stanford Professor Chelsea Finn
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Chelsea is an Assistant Professor at Stanford University. Her lab, IRIS, studies intelligence through robotic interaction at scale, and is affiliated with SAIL and the Statistical ML Group. I also spend time at Google as a part of the Google Brain team. Her research deals with the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction.
Links:
* Learning to Learn with Gradients
* Visual Model-Based Reinforcement Learning as a Path towards Generalist Robots
* RoboNet: A Dataset for Large-Scale Multi-Robot Learning
* Greedy Hierarchical Variational Autoencoders for Large-Scale Video
* Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks
Podcast Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music".
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