2216 results for "earn" across all locations
Uber + AutoGuru: rethinking the car-servicing experience
How innovation enabled AutoGuru to rethink the car-servicing experience for customers and mechanics.
Inside Uber HQ: the story behind Share Trip
Get the inside scoop on our Share Trip feature as we interview Uber product manager Ambar.
Austin’s top 5 outdoor activities for summer
With parks, pools, and pretty natural attractions, Austin boasts many places to enjoy the outdoors this summer 2017.
Celebrating International Women’s Day in Washington, D.C. with the Family
5 Must-see Attractions for Animal Enthusiasts in Miami
Three key learnings from Uber Latin America’s women in tech event series
During Uber’s Ada Talks event series, panels composed of women in tech at Uber, offer an exciting glimpse into day-to-day life, their exciting and impactful work, and how they continue to grow their careers at Uber.
Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning
C. DeChant, T. Wiesner-Hanks, S, Chen, E. Stewart, J. Yosinski, M. Gore, R. Nelson, and H. Lipson
Northern leaf blight (NLB) can cause severe yield loss in maize; however, scouting large areas to accurately diagnose the disease is time consuming and difficult. We demonstrate a system capable of automatically identifying NLB lesions in field-acquired images of maize plants with high reliability. […] [PDF]
Phytopathology, 2017
Deep Curiosity Search: Intra-Life Exploration Can Improve Performance on Challenging Deep Reinforcement Learning Problems
C. Stanton, J. Clune
Traditional exploration methods in RL require agents to perform random actions to find rewards. But these approaches struggle on sparse-reward domains like Montezuma’s Revenge where the probability that any random action sequence leads to reward is extremely low. Recent algorithms have performed well on such tasks by encouraging agents to visit new states or perform new actions in relation to all prior training episodes (which we call across-training novelty). […] [PDF]
2018
Uber-Text: A Large-Scale Dataset for Optical Character Recognition from Street-Level Imagery
Y. Zhang, L. Gueguen, I. Zharkov, P. Zhang, K. Seifert, B. Kadlec
Optical Character Recognition (OCR) approaches have been widely advanced in recent years thanks to the resurgence of deep learning. The state-of-the-art models are mainly trained on the datasets consisting of the constrained scenes. Detecting and recognizing text from the real-world images remains a technical challenge. […] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2017
Photo-Sketching: Inferring Contour Drawings from Images
M. Li, Z. Lin, R. Mech, E. Yumer, D. Ramanan
Edges, boundaries and contours are important subjects of study in both computer graphics and computer vision. On one hand, they are the 2D elements that convey 3D shapes, on the other hand, they are indicative of occlusion events and thus separation of objects or semantic concepts. In this paper, we aim to generate contour drawings, boundary-like drawings that capture the outline of the visual scene. Prior art often cast this problem as boundary detection. […] [PDF]
Winter Conference on Applications of Computer Vision (WACV), 2019