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2269 results for "earn" across all locations

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Drive 1 November 2023 / Australia

Introducing Uber Comfort Electric

As of October 25 2023, eligible driver-partners can now take Uber Comfort Electric trips. Uber Comfort Electric introduces a more sustainable ridesharing choice, by allowing riders to request trips in fully electric vehicles.

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Drive 12 July 2024 / Hong Kong

有關 Uber 車資的通知

為配合整體市場變化及保持可靠的平台體驗,我們將由 2024 年 7 月 14 日起調整平台車資。

Uber AI, Engineering 1 March 2017 / Global

Deep Bayesian Active Learning with Image Data

Y. Gal, R. Islam, Z. Ghahramani
Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. […] [PDF]
International Conference on Machine Learning (ICML), 2017

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Business 14 March 2018 / Global

5 dicas para facilitar suas viagens ao aeroporto

Frequently traveling for work? In this post, we look at 6 tips to get more out of the Uber app for a smoother airport ride.

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Products 5 January 2016 / North Carolina

How to Request a Ride at Raleigh-Durham International Airport (RDU)

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Products 10 May 2016 / Nashville

Driver promotions 18 March / Hong Kong

推薦朋友成爲司機夥伴

【領取豐厚推薦獎賞】介紹朋友,成為 Uber 司機夥伴

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Promotions 1 March 2016 / Montreal

Montreal, Investors are Waiting for Your #UberPITCH

Entrepreneurs, visionaries and self-starters–your big break is arriving now. On April 7, from 11am to 3pm, you have the chance to make your passion a reality with UberPitch.

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Uber AI 10 April 2018 / Global

Differentiable Plasticity: A New Method for Learning to Learn

Differentiable Plasticity is a new machine learning method for training neural networks to change their connection weights adaptively even after training is completed, allowing a form of learning inspired by the lifelong plasticity of biological brains.

Uber AI, Engineering 1 July 2018 / Global

Learning to Reweight Examples for Robust Deep Learning

M. Ren, W. Zeng, B. Yang, R. Urtasun
Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns. However, they can also easily overfit to training set biases and label noises. […] [PDF]
International Conference on Machine Learning ( ICML), 2018