2269 results for "earn" across all locations
General Latent Feature Modeling for Data Exploration Tasks
I. Valera, M. Pradier, Z. Ghahramani
This paper introduces a general Bayesian non- parametric latent feature model suitable to per- form automatic exploratory analysis of heterogeneous datasets, where the attributes describing each object can be either discrete, continuous or mixed variables. The proposed model presents several important properties. […] [PDF]
ICML Workshop on Human Interpretability in Machine Learning (ICML), 2017
Deep Watershed Transform for Instance Segmentation
M. Bai, R. Urtasun
Most contemporary approaches to instance segmentation use complex pipelines involving conditional random fields, recurrent neural networks, object proposals, or template matching schemes. In our paper, we present a simple yet powerful end-to-end convolutional neural network to tackle this task. […] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2017
A Student City Guide to Accra
New to Accra? Get to know your new home with our top picks on how to get to know more about your city.
Uber Community: The Ladies Series
We’re continuing our Uber Community interview series with Cristina Riesen, a well-known name in the tech industry. As the former General Manager of Evernote EMEA and a startup mentor, lecturer and entrepreneur, she’s always on the go and we’re grateful that she chooses Uber for her trips.
Meet Emilee, Software Engineer
Emilee’s mantra? “All or nothing.” See how this Uber for Business software engineer conquers work, friendships, and goal-setting.
Deep Rigid Instance Scene Flow
W.-C. Ma, S. Wang, R. Hu, Y. Xiong, R. Urtasun
In this paper we tackle the problem of scene flow estimation in the context of self-driving. We leverage deep learning techniques as well as strong priors as in our application domain the motion of the scene can be composed by the motion of the robot and the 3D motion of the actors in the scene. […] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Multi-Task Multi-Sensor Fusion for 3D Object Detection
M. Liang, B. Yang, Y. Chen, R. Hu, R. Urtasun
In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground estimation and depth completion. […] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
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.
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
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