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Meta Unsupervised Learning
Oriol Vinyals

Sat Dec 09 03:30 PM -- 04:00 PM (PST) @
in Workshop on Meta-Learning »

In this talk I'll cover some recent work on few shot learning which we did at DeepMind. I'll describe how the work in MANN and Matching Networks influenced our most recent work on few shot learning for distributions, "Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions".

Author Information

Oriol Vinyals (Google DeepMind)

Oriol Vinyals is a Research Scientist at Google. He works in deep learning with the Google Brain team. Oriol holds a Ph.D. in EECS from University of California, Berkeley, and a Masters degree from University of California, San Diego. He is a recipient of the 2011 Microsoft Research PhD Fellowship. He was an early adopter of the new deep learning wave at Berkeley, and in his thesis he focused on non-convex optimization and recurrent neural networks. At Google Brain he continues working on his areas of interest, which include artificial intelligence, with particular emphasis on machine learning, language, and vision.

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