649 results for "uber estimate" across all locations
uberX pickups have landed at Sydney Airport!
UberBlack: Your luxury ride returns to Basel
Starting June 7th, you can request your UberBlack ride to get you around Switzerland’s art capital effortlessly and in style.
Uber’s impact on Taxi Crime in Chicago
Taxicabs have been the venue for thousands of crimes committed in Chicago in the last decade. However, Uber’s entrance into Chicago has shown a favorable correlation with the decline of taxi crime rates.
uberX and uberPOOL land at Sea-Tac Airport
uberPOOL and uberX are now available at Sea-Tac Airport. Collect your bags and let Uber take care of your ride.
From UberPOP to UberX
The Mirage of Action-Dependent Baselines in Reinforcement Learning
G. Tucker, S. Bhupatiraju, S. Gu, R. Turner, Z. Ghahramani, S. Levine
Policy gradient methods are a widely used class of model-free reinforcement learning algorithms where a state-dependent baseline is used to reduce gradient estimator variance. Several recent papers extend the baseline to depend on both the state and action and suggest that this significantly reduces variance and improves sample efficiency without introducing bias into the gradient estimates. […] [PDF]
International Conference on Machine Learning (ICML), 2018
Deep Multi-Sensor Lane Detection
M. Bai, G. Mattyus, N. Homayounfar, S. Wang, S. K. Lakshmikanth, R. Urtasun
Reliable and accurate lane detection has been a long-standing problem in the field of autonomous driving. In recent years, many approaches have been developed that use images (or videos) as input and reason in image space. In this paper we argue that accurate image estimates do not translate to precise 3D lane boundaries, which are the input required by modern motion planning algorithms. […] [PDF]
International Conference on Intelligent Robots and Systems (IROS), 2018
GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation
X. Qi, R. Liao, Z. Liu, R. Urtasun, J. Jia
In this paper, we propose Geometric Neural Network (GeoNet) to jointly predict depth and surface normal maps from a single image. Building on top of two-stream CNNs, our GeoNet incorporates geometric relation between depth and surface normal via the new depth-to-normal and normal-to-depth networks. […] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018