Web Picks (week of 3 April 2017)

Every two weeks, we find the most interesting data science links from around the web and collect them in Data Science Briefings, the DataMiningApps newsletter. Subscribe now for free if you want to be the first to get up to speed on interesting resources.

  • Dissecting Trump’s Most Rabid Online Following
    “President Donald Trump’s administration, in its turbulent first months, has drawn fire from both the left and the right, but one group has shown nothing but unbridled enthusiasm for the president’s actions thus far: the over 380,000 members of r/The_Donald, one of the thousands of comment boards on Reddit, the fifth-most-popular website in the U.S.” Interesting analysis!
  • Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
    “Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples.”
  • Failures of Deep Learning (paper)
    “In recent years, Deep Learning has become the go-to solution for a broad range of applications, often outperforming state-of-the-art. However, it is important, for both theoreticians and practitioners, to gain a deeper understanding of the difficulties and limitations associated with common approaches and algorithms. We describe four families of problems for which some of the commonly used existing algorithms fail or suffer significant difficulty. We illustrate the failures through practical experiments, and provide theoretical insights explaining their source, and how they might be remedied.”