2218 results for "earn" across all locations
Attribute-Based Access Control at Uber
Learn about how the core security engineering team defined and implemented an Attribute-Based Access Control policy model at Uber, where 70 services have already adopted it for different authorization needs.
Learning Joint 2D-3D Representations for Depth Completion
Y. Chen, B. Yang, M. Liang, R. Urtasun
We design a simple yet effective architecture that fuses information between 2D and 3D representations at multiple levels to learn fully fused joint representations at multiple levels, and show state-of-the-art results on the KITTI depth completion benchmark. [PDF]
International Conference on Computer Vision (ICCV), 2019
Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform
Z. Zhao, R. Anand, M. Wang
In machine learning applications for online product offerings and marketing strategies, there are often hundreds or thousands of features available to build such models. Feature selection is one essential method in such applications for multiple objectives: improving the prediction accuracy by eliminating irrelevant features, accelerating the model training and prediction speed, reducing the monitoring and maintenance workload for feature data pipeline, and providing better model interpretation and diagnosis capability. […] [PDF]
IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2019
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints
M. Li, E. Yumer, D. Ramanan
Current approaches for hyper-parameter tuning and neural architecture search tend to be limited by practical resource constraints. Therefore, we introduce a formal setting for studying training under the non-asymptotic, resource-constrained regime, i.e. budgeted training. We analyze the following problem: “given a dataset, algorithm, and resource budget, what is the best achievable performance?” [PDF]
International Conference on Learning Representations (ICLR), 2020
UberPITCH – Celebrating Entrepreneurship Across Europe
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning
A. Papangelis, Y.-C. Wang, P. Molino, G. Tur
We present the first complete attempt at concurrently training conversational agents that communicate only via self-generated language. Using DSTC2 as seed data, we trained natural language understanding (NLU) and generation (NLG) networks for each agent and let the agents interact online. […] [PDF]
Special Interest Group on Discourse and Dialogue (SIGDIAL), 2019
Step out of the city with UberIntercity
Step out of the city with UberIntercity
End-to-end Learning of Multi-sensor 3D Tracking by Detection
D. Frossard, R. Urtasun
In this paper we propose a novel approach to tracking by detection that can exploit both cameras as well as LIDAR data to produce very accurate 3D trajectories. Towards this goal, we formulate the problem as a linear program that can be solved exactly, and learn convolutional networks for detection as well as matching in an end-to-end manner. […] [PDF]
International Conference on Robotics and Automation (ICRA), 2018
The Top 5 Farmers Markets in Toronto