2275 results for "earn" across all locations
Behind the Wheel with… Pin, NYC uberX partner
This week, we’re Behind the Wheel with Pin, one of our top-rated uberX partners here in New York City. Read on to learn more about Pin’s story.
Under the Hood of Uber ATG’s Machine Learning Infrastructure and Versioning Control Platform for Self-Driving Vehicles
Managing multiple machine learning models to enable self-driving vehicles is a challenge. Uber ATG developed a model life cycle for quick iterations and a tool for continuous delivery and dependency management.
No Coding Required: Training Models with Ludwig, Uber’s Open Source Deep Learning Toolbox
Uber AI’s Piero Molino discusses Ludwig’s origin story, common use cases, and how others can get started with this powerful deep learning framework built on top of TensorFlow.
Use Passkeys Wherever You Sign in to Uber
Are you scared of forgetting the passwords for your online accounts? This Cybersecurity Awareness Month, Uber is rolling out support for passkeys on our mobile apps and websites. Learn about why passkeys are the future of passwordless authentication.
Visualizing Traffic Safety with Uber Movement Data and Kepler.gl
Learn how to use Kepler.gl for data visualization through our tutorial, where we show how easy it is to load multiple datasets into Kepler.gl to visualize traffic safety in Manhattan.
Bypassing Large Diffs in SubmitQueue
Learn how we optimized SubmitQueue’s speculation algorithm, landing changes out of order while keeping the main branch green, which improved wait times to land code by 74%.
Differential Backups in MyRocks Based Distributed Databases at Uber
Learn about how the Storage team at Uber significantly reduced costs and improved speed for backups of its Petabyte-scale, MyRocks-based distributed databases by devising a Differential Backups solution.
Open Source at Uber: Meet Alex Sergeev, Horovod Project Lead
We sat down with Horovod project lead, Alex Sergeev, to discuss his path to open source and what most excites him about the future of Uber’s distributed deep learning framework.
Learning Joint 2D-3D Representations for Depth Completion
Y. Chen, B. Yang, M. Liang, R. Urtasun
We design a simple yet effective architecture that fuses information between 2D and 3D representations at multiple levels to learn fully fused joint representations at multiple levels, and show state-of-the-art results on the KITTI depth completion benchmark. [PDF]
International Conference on Computer Vision (ICCV), 2019
