Abstract: Building datasets for machine learning has been a major problem in computer vision for over two decades now, and, because of its rich history and straightforward goal, serves as a great example of how the field has evolved. We'll illustrate how training modern convoluti…

Abstract:

Building datasets for machine learning has been a major problem in computer vision for over two decades now, and, because of its rich history and straightforward goal, serves as a great example of how the field has evolved. We'll illustrate how training modern convolutional neural networks (convnets) differs from research done in the past. In particular, we'll discuss the notion of a deconvolutional network and demonstrate how they can be used to visualize intermediate feature layers and the operation of a classifier.

Ryan's bio:



Ryan Compton currently heads applied machine learning at Clarifai. His day-to-day is designing datasets to train neural nets and then shipping them into production. Ryan holds a PhD in mathematics from UCLA with a focus on sparsity-promoting optimization and was previously on staff at Howard Hughes laboratories. Some of his research has been covered in Forbes, the New York Observer, and Business Insider among other places.



