1762 results for "airport" across all locations
A little kindness goes a long way
A little kindness goes a long way – happy compliments day
Uber Se Lance À Geneve… Enfin!
Et oui, c’est aujourd’hui! Uber s’ouvre enfin aux habitants de la Cité de Calvin et ce sans que le lac soit en flamme.
Greater Accessibility for Riders
Today we’re excited to announce the next step in providing increased mobility to Torontonians with uberASSIST.
‘DrinkWise’ and Uber Home
Meet Onur, a deaf partner driver from London
We caught up with Onur for Deaf Awareness Week.
The “Cab” is back: Uber Pedicabs at SXSW
At SXSW? Uber is! We’ll be rocking Uber Pedicabs from the 10th-15th with 20% of proceeds going to LIVESTRONG. Let’s pedal toward a cure (and to the SXSW after parties 🙂
The “Cab” is back: Uber Pedicabs at SXSW
At SXSW? Uber is! We’ll be rocking Uber Pedicabs from the 10th-15th with 20% of proceeds going to LIVESTRONG. Let’s pedal toward a cure (and to the SXSW after parties 🙂
Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving
N. Djuric, V. Radosavljevic, H. Cui, T. Nguyen, F.-C. Chou, T.-H. Lin, N. Singh, J. Schneider
We introduce an approach that takes into account a current world state and produces rasterized representations of each traffic actor’s vicinity. The raster images are then used as inputs to deep convnets to infer future movement of actors while also accounting for and capturing inherent uncertainty of the prediction task, with extensive experiments on real-world data strongly suggest benefits of the proposed approach. [PDF]
Winter Conference on Applications of Computer Vision (WACV), 2020
Few-Shot Learning Through an Information Retrieval Lens
E. Triantafillou, R. Zemel, R. Urtasun
Few-shot learning refers to understanding new concepts from only a few examples. We propose an information retrieval-inspired approach for this problem that is motivated by the increased importance of maximally leveraging all the available information in this low-data regime. [PDF]
Code: [LINK]
Advances in Neural Information Processing Systems (NeurIPS), 2017
Matching Adversarial Networks
G. Mattyus, R. Urtasun
Generative Adversarial Nets (GANs) and Conditonal GANs (CGANs) show that using a trained network as loss function (discriminator) enables to synthesize highly structured outputs (e.g. natural images). However, applying a discriminator network as a universal loss function for common supervised tasks (e.g. semantic segmentation, line detection, depth estimation) is considerably less successful. […] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2018