2270 results for "earn" across all locations
【Uber App Guide for Riders】learn more about Upfront Fares
Upfront fare is calculated based on your trip’s expected time and distance, the vehicle option you select (UberX, Black, UberXL, etc) and also accounts for the predicted traffic and availability of driver-partners at the time of the request. If demand is high and dynamic pricing is in effect, your upfront fare may be higher.
UberMENTOR: Ride and Learn from Jacksonville’s Best and Brightest
Fraud Detection: Using Relational Graph Learning to Detect Collusion
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Three Approaches to Scaling Machine Learning with Uber Seattle Engineering
At an April 2019 meetup on ML and AI at Uber Seattle, members of our engineering team discussed three different approaches to enhancing our ML ecosystem.
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch
S. Duggal, S. Wang, W.-C. Ma, R. Hu, R. Urtasun
We propose a real-time dense depth estimation approach using stereo image pairs, which utilizes differentiable Patch Match to progressively prune the stereo matching search space. Our model achieves competitive performance on the KITTI benchmark despite running in real time. [PDF]
International Conference on Computer Vision (ICCV), 2019
Electrifying for a greener San Francisco
Uber is working together with Plug In America in San Fransisco around electric vehicles as part of our commitment to a more sustainable future
Uber Sets Asia in Motion with Singapore Launch
Uber launches in Singapore, not a rainy day too soon!
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning
S. Gu, T. Lillicrap, R. Turner, Z. Ghahramani, B. Schölkopf, S. Levine
Off-policy model-free deep reinforcement learning methods using previously collected data can improve sample efficiency over on-policy policy gradient techniques. On the other hand, on-policy algorithms are often more stable and easier to use. […] [PDF]
Advances in Neural Information Processing Systems (NeurIPS), 2017
End-to-end Interpretable Neural Motion Planner
W. Zeng, W. Luo, S. Suo, A. Sadat, B. Yang, S. Casas, R. Urtasun
In this paper, we propose a neural motion planner for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users. Towards this goal, we design a holistic model that takes as input raw LIDAR data and an HD map and produces interpretable intermediate representations in the form of 3D detections and their future trajectories, as well as a cost volume defining the goodness of each position that the self-driving car can take within the planning horizon. […] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
