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2217 results for "earn" across all locations

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Products 11 April 2017 / Toronto

Scarborough and Brampton, meet uberPOOL

Starting today, uberPOOL will also be available in Brampton and Scarborough. uberPOOL will give residents the option to share their journey with another rider heading in the same direction at the same time. This will mean more people in fewer cars, cheaper rides for passengers, and less time between trips for drivers.

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Promotions 28 June 2017 / New Delhi

Let’s empower our loved ones

A workshop on digital empowerment for the young folks over the ages of 55yrs

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Products 22 June 2017 / Florida

Uber everywhere

As of July 1, Uber will be available everywhere in Florida.

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Stories 23 September 2017 / South Africa

Uber Movement – finding smarter ways to move forward

We’re excited to announce the launch of Movement – a website that uses Uber’s data to help urban planners make informed decisions about our cities.

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Products 9 August 2017 / Mississippi

Something’s rolling into Meridian, Mississippi Delta, and Golden Triangle

Uber is launching in Meridian, Mississippi Delta, and Golden Triangle on Friday, August 18.

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Promotions 17 March 2017 / Michigan

You’re invited to an Uber Day at the Pistons Game!

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Promotions 20 April 2016 / Los Angeles

Share Your Ride, Reduce Your Carbon Footprint

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Products 23 July 2015 / Canada

London, Waterloo Region, Hamilton, & Guelph. Your Uber is Arriving Now!

Uber AI, Engineering 1 July 2018 / Global

Variational Bayesian dropout: pitfalls and fixes

J. Hron, A. Matthews, Z. Ghahramani
Dropout, a stochastic regularisation technique for training of neural networks, has recently been reinterpreted as a specific type of approximate inference algorithm for Bayesian neural networks. The main contribution of the reinterpretation is in providing a theoretical framework useful for analysing and extending the algorithm […] [PDF]
International Conference on Machine Learning (ICML), 2018

Uber AI, Engineering 9 June 2017 / Global

Time-series extreme event forecasting with neural networks at Uber

N. Laptev, J. Yosinski, L. Li, S. Smyl
Accurate time-series forecasting during high variance segments (e.g., holidays), is critical for anomaly detection, optimal resource allocation, budget planning and other related tasks. At Uber accurate prediction for completed trips during special events can lead to a more efficient driver allocation resulting in a decreased wait time for the riders. [PDF]
International Conference on Machine Learning (ICML), 2017

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