Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Developed by Uber AI Labs, Generative Teaching Networks (GTNs) automatically generate training data, learning environments, and curricula to help AI agents rapidly learn.
In 2019, Uber's Data Platform team leveraged data science to improve the efficiency of our infrastructure, enabling us to compute optimum datastore and hardware usage.
We share technical challenges and lessons learned while productionizing and scaling XGBoost to train distributed gradient boosted algorithms at Uber.
Plug and Play Language Model, introduced by Uber AI Labs, gives NLP practitioners the flexibility to plug in one or more simple attribute models into a large, unconditional language model.
By integrating graph learning techniques with our Uber Eats recommendation system, we created a more seamless and individualized user experience for eaters on our platform.
Uber introduces RxCentral, an open source library to reliably and repeatedly connect Bluetooth devices using a platform-agnostic, reactive design.
Uber’s observability engineers present their work on distributed tracing (Jaeger), sampling (XYS), and metrics processing (M3).
Meet a few of the engineers from Uber's Boulder, Colorado office, working on everything from maps to new mobility to large-scale distributed systems.
As part of Uber Visualization's all-team hackathon, we built Urban Symphony, an Uber Movement visualization that adds an audio component to traffic speed patterns.
To simplify the Uber Eats experience for our restaurant-partners, we built Menu Maker, a web-based tool for seamlessly managing menus on the Uber Eats app.
Uber engineers describe Cadence, Uber’s open source workflow orchestration tool, its architecture, and its use in a series of informative presentations.
With the release of deck.gl version 7.3, Uber’s open source visualization tool now supports rendering massive geospatial data sets formatted according to the OGC 3D Tiles community standard.
Uber's IT Engineering team builds the tools and systems that help other Uber employees do their jobs. Meet a few of these remarkable behind-the-scenes engineers.
To accommodate additional ML use cases, Uber evolved Michelangelo's application of the Apache Spark MLlib library for greater flexibility and extensibility.
Uber has embraced Presto, a high performance, distributed SQL query engine, and joined the Presto Foundation. Meet the Uber engineers who contribute to and use Presto on a daily basis.
When developing Uber's self driving car systems, engineers found a way to identify edge case scenarios amongst terabytes of sensor data representing real-world situations.