How CLEVER is your neural network? Robustness evaluation against adversarial examples
February 3, 2020
Neural networks are particularly vulnerable to adversarial inputs. Carefully designed perturbations can lead a well-trained model to misbehave, raising new concerns about safety-critical and security-critical applications. Pin-Yu Chen offers an overview of CLEVER, a comprehensive robustness measure that can be used to assess the robustness of any neural network classifiers.
Distributed deep learning in the cloud: Build an end-to-end application involving computer vision and geospatial data
January 13, 2020
High-resolution land cover maps help quantify long-term trends like deforestation and urbanization but are prohibitively costly and time intensive to produce. Mary Wahl and Banibrata De demonstrate how to use Microsofts Cognitive Toolkit and Azure cloud resources to produce land cover maps from aerial imagery by training a semantic segmentation DNNboth on single VMs and at scale on GPU clusters.
Working with the data of sports
November 18, 2019
Sports analytics today is more than a matter of analyzing box scores and play-by-play statistics. Faced with detailed on-field or on-court data from every game, sports teams face challenges in data management, data engineering, and analytics. Thomas Miller details the challenges faced by a Major League Baseball team as it sought competitive advantage through data science and deep learning.