2318 results for "earn" across all locations
Step out of the city with UberIntercity
Step out of the city with UberIntercity
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
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.
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.
Continuous Integration and Deployment for Machine Learning Online Serving and Models
Applying Machine Learning in Internal Audit with Sparsely Labeled Data
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.
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
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