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Friday, January 25 • 11:05am - 11:30am
Meta-Learning Deep Networks

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Deep learning has enabled significant advances in a variety of domains; however, it relies heavily on large labeled datasets. I will discuss how we can use meta-learning, or learning to learn, to enable us to adapt deep models to new tasks with tiny amounts of data, by leveraging data from other tasks. By using these meta-learning techniques, I will show how we can build better unsupervised learning algorithms, build agents that can adapt online to changing environments, and build robots that can interact with a new object by watching a single demonstration.

avatar for Chelsea Finn, Google Brain & Berkeley AI Research

Chelsea Finn, Google Brain & Berkeley AI Research

Research Scientist & Post-doctoral Scholar, Google Brain & Berkeley AI Research
Chelsea Finn is a research scientist at Google Brain and post-doctoral scholar at Berkeley AI Research. Starting in 2019, she will join the faculty in CS at Stanford University. She is interested in how learning algorithms can enable machines to acquire general notions of intelligence... Read More →

Friday January 25, 2019 11:05am - 11:30am
Grand Ballroom Hyatt Regency San Francisco, 5 Embarcadero Center, San Francisco, CA 94111, USA