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Pyro Accepted by the LF Deep Learning Foundation as a Hosted Project

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.

Uber Open Source: Catching Up with Fritz Obermeyer and Noah Goodman from the Pyro...

We spoke with Fritz Obermeyer and Noah Goodman, Pyro project co-leads, about the potential of open source AI software at Uber and beyond.

Modeling Censored Time-to-Event Data Using Pyro, an Open Source Probabilistic Programming Language

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.

Uber AI Labs Open Sources Pyro, a Deep Probabilistic Programming Language

Pyro is an open source probabilistic programming language that unites modern deep learning with Bayesian modeling for a tool-first approach to AI.
Uber open source logo

Uber Open Source in 2019: Community Engagement and Contributions

Uber recounts its many engagements with the open source community during 2019, from contributing projects to joining and founding new open source support organizations.
Uber AI in 2019: Advancing Mobility with Artificial Intelligence

Uber AI in 2019: Advancing Mobility with Artificial Intelligence

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 Goes to NeurIPS 2019

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.

Announcing the 2020 Uber AI Residency

Uber's 2020 AI Residency will focus on initiatives related to our self-driving car project through Uber Advanced Technology Group (ATG).
Dragonfly

Introducing Hypothesis GU Funcs, an Open Source Python Package for Unit Testing

Uber introduces Hypothesis GU Func, a new extension to Hypothesis, as an open source Python package for unit testing.

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.

Science at Uber: Applying Artificial Intelligence at Uber

Zoubin Ghahramani, Head of Uber AI, discusses how we use artificial intelligence techniques to make our platform more efficient for users.
Seattle skyline

Uber Open Source: Catching Up with Felix Cheung, Data Platform Engineering Manager

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.

Introducing Ludwig, a Code-Free Deep Learning Toolbox

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 AI Chief Scientist Zoubin Ghahramani speaks at Uber Open Summit 2018

Collaboration at Scale: Highlights from Uber Open Summit 2018

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.

Announcing the 2019 Uber AI Residency

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.
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Preview 7 Open Source Projects from the Uber Open Summit

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.

Announcing Uber Open Summit 2018: Collaboration at Scale

Keynote speakers include Jim Zemlin, executive director of the Linux Foundation, and Zoubin Ghahramani, chief scientist at Uber AI Labs.

M4 Forecasting Competition: Introducing a New Hybrid ES-RNN Model

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.

Introducing the Uber AI Residency

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.

SBNet: Leveraging Activation Block Sparsity for Speeding up Convolutional Neural Networks

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.