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Created by Uber in 2017, Pyro was voted in by the Linux Foundation Deep Learning Technical Board as the latest incubation project to join its foundation.
We spoke with Fritz Obermeyer and Noah Goodman, Pyro project co-leads, about the potential of open source AI software at Uber and beyond.
Censored time-to-event data is critical to the proper modeling and understanding of customer engagement on the Uber platform. In this article, we demonstrate an easier way to model this data using Pyro.
Pyro is an open source probabilistic programming language that unites modern deep learning with Bayesian modeling for a tool-first approach to AI.
Uber recounts its many engagements with the open source community during 2019, from contributing projects to joining and founding new open source support organizations.
In 2019, Uber AI built tools and systems that leverage ML to improve location accuracy and enhance real-time forecasting, among other applications on our platform.
Uber is presenting 11 papers at the NeurIPS 2019 conference in Vancouver, Canada, as well as sponsoring workshops including Women in Machine Learning (WiML) and Black in AI.
Uber's 2020 AI Residency will focus on initiatives related to our self-driving car project through Uber Advanced Technology Group (ATG).
Uber introduces Hypothesis GU Func, a new extension to Hypothesis, as an open source Python package for unit testing.
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.
Zoubin Ghahramani, Head of Uber AI, discusses how we use artificial intelligence techniques to make our platform more efficient for users.
Uber Engineering Manager and open source software community member Felix Cheung talks about his work with the Apache Software Foundation, open source at Uber, and XGBoost, a machine learning library for optimized distributed gradient boosting.
Uber AI developed Ludwig, a code-free deep learning toolbox, to make deep learning more accessible to non-experts and enable faster model iteration cycles.
Uber hosted its first Open Summit on November 15, inviting the open source community to learn about our open source projects from the engineers who use them every day. Check out highlights from the day, including keynotes from the Linux Foundation's Jim Zemlin and Uber AI's Zoubin Ghahramani.
The Uber AI Residency is a 12-month training program for academics and professionals interested in becoming an AI researcher with Uber AI Labs or Uber ATG.
Uber open source projects leads give updates on seven of our projects, all of which will be showcased at the upcoming Uber Open Summit 2018.
Keynote speakers include Jim Zemlin, executive director of the Linux Foundation, and Zoubin Ghahramani, chief scientist at Uber AI Labs.
With a solid margin, Uber senior data scientist Slawek Smyl won the M4 Competition with his hybrid Exponential Smoothing-Recurrent Neural Networks (ES-RNN) forecasting method.
Interested in accelerating your career by tackling some of Uber’s most challenging AI problems? Apply for the Uber AI Residency, a research fellowship dedicated to fostering the next generation of AI talent.
Uber ATG Toronto developed Sparse Blocks Network (SBNet), an open source algorithm for TensorFlow, to speed up inference of our 3D vehicle detection systems while lowering computational costs.
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