How Uber Uses Convolutional Neural Networks

The article discusses the application of relational graph learning, specifically relational graph convolutional networks (RGCN), to detect collusion in fraudulent activities within the Uber platfor...

The article discusses how Uber Eats utilizes graph learning techniques to enhance its food recommendation system, improving user experience by providing personalized dish and restaurant suggestions.

The article discusses the role of women in data science at Uber, highlighting their contributions to various projects and the importance of diversity in technical fields.

The article discusses an innovative approach to enhance the performance of Convolutional Neural Networks (CNNs) by utilizing JPEG's internal representations.

The article discusses the limitations of Convolutional Neural Networks (CNNs) in performing coordinate transformations and introduces the CoordConv layer as a solution.

The article discusses the importance of accurately segmenting building footprints using a novel framework called Deep Structured Active Contours (DSAC).

The article presents a novel approach for semi-automatic annotation of object instances in images, shifting from traditional pixel-labeling to polygon prediction.

The article discusses SBNet, an open-source algorithm developed by Uber ATG that leverages activation block sparsity to enhance the speed of Convolutional Neural Networks (CNNs).

The article discusses the Reversible Residual Network (RevNet), a variant of Residual Networks (ResNets) that allows for backpropagation without storing intermediate activations, thereby reducing m...