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16 JAN

SBNet: Leveraging Activation Block Sparsity for Speeding up Convolutional Neural Networks

Uber ATG Toronto developed Sparse Blocks Network (SBNet), an open source algorithm for TensorFlow, to speed up inference of our 3D vehicle detection systems while lowering computational costs.

10 NOV

Engineering More Reliable Transportation with Machine Learning and AI at Uber

In this article, we highlight how Uber leverages machine learning and artificial intelligence to tackle engineering challenges at scale.

5 OCT

Engineering a Million-Mile Journey with Uber ATG

Uber ATG's Poornima Kaniarasu shares how she found her "place" developing the machine learning technologies behind our self-driving vehicles.

28 AUG

Engineering Uber’s Self-Driving Car Visualization Platform for the Web

Uber Engineering's Data Visualization Team and ATG built a new web-based platform that helps engineers and operators better understand information collected during testing of its self-driving vehicles.

9 JUN

Engineering Extreme Event Forecasting at Uber with Recurrent Neural Networks

Recurrent neural networks equip Uber Engineering's new forecasting model to more accurately predict rider demand during extreme events.

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