2226 results for "earn" across all locations
Applying Machine Learning in Internal Audit with Sparsely Labeled Data
Learnings in Web Development: Design Patterns, Elm, and Progressive Enhancement
Uber’s Destination:Web meetup series gives great insight about the most current web building tools and techniques. These three videos from Uber presenters offer tips on a mysterious design pattern, the Elm language, and Progressive Enhancement.
Learn how Uber Freight is helping United Reuse grow its business
Thanks to Uber Freight’s instant quotes, the industrial packaging company now saves hundreds of hours in booking time.
5 key learnings from Uber’s leadership panel on the power of visibility
To celebrate International Women’s Day, we hosted a panel of leaders from across Uber showcasing the power of visibility. Ana Loibner, Global Mobility Chief of Staff and Women at Uber Global Board Member, shares what she learned.
Learning a Generative Model for Multi-Step Human-Object Interactions from Videos
H. Wang, S. Pirk, V. Kim, E. Yumer, L. Guibas
Creating dynamic virtual environments consisting of humans interacting with objects is a fundamental problem in computer graphics. While it is well-accepted that agent interactions play an essential role in synthesizing such scenes, most extant techniques exclusively focus on static scenes, leaving the dynamic component out. In this paper, we present a generative model to synthesize plausible multi-step dynamic human–object interactions. […] [PDF]
European Association for Computer Graphics (Eurographics), 2019
Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects
W. Y. Zou, S. Shyam, M. Mui, M. Wang, J. Pedersen, Z. Ghahramani
Causal inference methods are widely applied in the fields of medicine, policy, and economics. Central to these applications is the estimation of treatment effects to make decisions. Current methods make binary yes-or-no decisions based on the treatment effect of a single outcome dimension. These methods are unable to capture continuous space treatment policies with a measure of intensity. […] [PDF]
2020
Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning at Uber
Uber built Manifold, a model-agnostic visualization tool for ML performance diagnosis and model debugging, to facilitate a more informed and actionable model iteration process.
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch
S. Duggal, S. Wang, W.-C. Ma, R. Hu, R. Urtasun
We propose a real-time dense depth estimation approach using stereo image pairs, which utilizes differentiable Patch Match to progressively prune the stereo matching search space. Our model achieves competitive performance on the KITTI benchmark despite running in real time. [PDF]
International Conference on Computer Vision (ICCV), 2019
Three Approaches to Scaling Machine Learning with Uber Seattle Engineering
At an April 2019 meetup on ML and AI at Uber Seattle, members of our engineering team discussed three different approaches to enhancing our ML ecosystem.
Fraud Detection: Using Relational Graph Learning to Detect Collusion
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