2213 results for "earn" across all locations
外送合作夥伴之保險保障
Uber Eats 為了讓外送合作夥伴依法享有保險保障，除了提供團體傷害險給符合資格的外送合作夥伴，Uber Eats 平台亦將自 2024 年 6 月 1 日起為外送合作夥伴提供第三人責任險，並全額支付保險保費。
List of partners recommended by the drivers
The list includes partners who obtained high cooperation ratings from drivers based on the survey.
Список партнеров, имеющих лицензию такси для пассажирских перевозок
Lista obejmuje partnerów, którzy uzyskali wysokie oceny współpracy od kierowców na podstawie ankiety.
Uber Eats 服務範圍與時段
請點擊各個地圖連結查看各地區服務範圍，全台營運時段大多為 06:00 – 02:00 ，雙北、桃園、新竹、台中、彰化、台南、高雄部分區域已有開放 24 小時營運，夥伴可參考建議上線時段上線接單。
Atualizações no seu mapa de ganhos
Conheça o novo Seguro Renda Protegida oferecido pela XCover para motoristas parceiros da Uber.
Procesul de Colantare Uber
Aici vei găsi toată informația necesară pentru a participa în procesul nostru de colantare și care sunt Termenii și Condițiile noastre pentru a putea beneficia de bonusul de colantare.
【優夥禮遇】UAG 兌換方式
在優夥禮遇中，你可以享有 UAG 配件最低6折起的超殺優惠！輕薄、耐用又耐摔的軍規級手機殼，讓你的手機在極端環境下也不受到損壞，為你的手機換一件新衣服吧！
Differentiable plasticity: training plastic neural networks with backpropagation
T. Miconi, J. Clune, K. Stanley
How can we build agents that keep learning from experience, quickly and efficiently, after their initial training? Here we take inspiration from the main mechanism of learning in biological brains: synaptic plasticity, carefully tuned by evolution to produce efficient lifelong learning. We show that plasticity, just like connection weights, can be optimized by gradient descent in large (millions of parameters) recurrent networks with Hebbian plastic connections. […] [PDF]
International Conference on Machine Learning (ICML), 2018
Incremental Few-Shot Learning with Attention Attractor Networks
M. Ren, R. Liao, E. Fetaya, R. Zemel
This paper addresses this problem, incremental few- shot learning, where a regular classification network has already been trained to recognize a set of base classes, and several extra novel classes are being considered, each with only a few labeled examples. After learning the novel classes, the model is then evaluated on the overall classification performance on both base and novel classes. To this end, we propose a meta-learning model, the Attention Attractor Networks, which regularizes the learning of novel classes. [PDF]
Conference on Neural Information Processing Systems (NeurIPS), 2019
New app features and data show how Uber can improve safety on the road
We believe that technologies like Uber provide an incredible opportunity to improve road safety in new and innovative ways—before, during and after every ride. Today, we’re excited to announce several new safety pilots to improve rider and driver safety.