2269 results for "earn" across all locations
Shop. Drop. Ride with Uber. #ShopTheCity
The City of Melbourne for the the first time, have created an Uber pick-up and drop off zone in the Melbourne CBD. Yes you heard right, Melbournians will be able to shop to they drop at #ShopTheCity and be picked up in style with Uber.
Your 2018 spring training drive guide
Find the best time to drive with Uber and make the most of spring training this month
Ringing in 2017 in Kuala Lumpur
Uber Melbourne Statement
Uber statement on Melbourne incident
uberPOOL’s Expanded Coverage is Arriving Now
We’re excited to announce that we’re expanding uberPOOL’s coverage area in Atlanta, so that more riders can access our lowest-cost option.
Something’s rolling into Pine Bluff…
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
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
T. Adel, Z. Ghahramani, A. Weller
Interpretability of representations in both deep generative and discriminative models is highly desirable. Current methods jointly optimize an objective combining accuracy and interpretability. However, this may reduce accuracy, and is not applicable to already trained models. We propose two interpretability frameworks. First, we provide an interpretable lens for an existing model. We use a generative model which takes as input the representation in an existing (generative or discriminative) model, weakly supervised by limited side information. […] [PDF]
International Conference on Machine Learning (ICML), 2018
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