A Survey of Deep Neural Nets for Machine Learning and Some Comments on Learning From Sparsity
UCLA Center for Applied Statistics
Thursday, January 21, 2016|
3:00pm - 4:30pm
3725 Beyster Building
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About the Event
This talk surveys how deep neural networks are used in machine learning. We comment on how some of the activations can be interpreted. We also comment on how a manifold structure can be learned by recording sparse activation patterns.
John Flynn is Mike Flynn’s younger brother. He received a Ph.D. in number theory from Berkeley in 2001 and a masters in statistics from UCLA in 2013. He currently works in a research group in UCLA focusing on statistics and machine learning applied to computer vision.
Contact: Fran Doman
Sponsor(s): MICL Seminar Series
Faculty Sponsor: Mike Flynn
Open to: Public