Making AI Interpretable with Generative Adversarial Networks

It has been quite awhile since I have posted, largely because soon after I started my job at Square I had a child! I hope to have some newer blog post soon. But along those lines I want to share a blog post I did with a coworker (Juan Hernandez) for Square that gives a taste of some of the cool data science work we have been up to. This post covers work we did to create a framework for making models interpretable.

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Damien RJ Damien is a highly experienced researcher with a background in clinical and applied research. Like JSL, he got his PhD at UCLA. He has many years of experience working with imaging, and has a particularly strong background in image segmentation, registration, detection, data analysis, and more recently machine learning. He now works as a data-scientist at Square in San Francisco.