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Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask

Uber builds upon the Lottery Ticket Hypothesis by proposing explanations behind these mechanisms and deriving a surprising by-product: the Supermask.

Introducing the Uber Research Publications Site

Uber's Chief Scientist announces the launch of the Uber Research Publications Site, a portal for showcasing our contributions to the research community.
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Accessible Machine Learning through Data Workflow Management

Uber engineers offer two common use cases showing how we orchestrate machine learning model training in our data workflow engine.

Horovod Adds Support for PySpark and Apache MXNet and Additional Features for Faster Training

Horovod adds support for more frameworks in the latest release and introduces new features to improve versatility and productivity.

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.

First Uber Science Symposium: Discussing the Next Generation of RL, NLP, ConvAI, and DL

The Uber Science Symposium featured talks from members of the broader scientific community about the the latest innovations in RL, NLP, and other fields.

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.

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.

Creating a Zoo of Atari-Playing Agents to Catalyze the Understanding of Deep Reinforcement Learning

Uber AI Labs releases Atari Model Zoo, an open source repository of both trained Atari Learning Environment agents and tools to better understand them.

POET: Endlessly Generating Increasingly Complex and Diverse Learning Environments and their Solutions through the...

Uber AI Labs introduces the Paired Open-Ended Trailblazer (POET), an algorithm that leverages open-endedness to push the bounds of machine learning.

Faster Neural Networks Straight from JPEG

Uber AI Labs introduces a method for making neural networks that process images faster and more accurately by leveraging JPEG representations.

How to Get a Better GAN (Almost) for Free: Introducing the Metropolis-Hastings GAN

Metropolis-Hastings Generative Adversarial Networks (GANs) leverage the discriminator to pick better samples from the generator after ML model training is done.

Montezuma’s Revenge Solved by Go-Explore, a New Algorithm for Hard-Exploration Problems (Sets Records on...

Uber AI Labs introduces Go-Explore, a new reinforcement learning algorithm for solving a variety of challenging problems, especially in robotics.
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.
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Experience in AI: Uber Hires Jan Pedersen

Uber welcomes Jan Pedersen as a Distinguished Scientist to our Uber AI group, where he will bring his extensive experience to our efforts in improving artificial intelligence and machine learning.

NVIDIA: Accelerating Deep Learning with Uber’s Horovod

Horovod, Uber's open source distributed deep learning system, enables NVIDIA to scale model training from one to eight GPUs for their self-driving sensing and perception technologies.

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.

Scaling Machine Learning at Uber with Michelangelo

Uber built Michelangelo, our machine learning platform, in 2015. Three years later, we reflect our journey to scaling ML at Uber and lessons learned along the way.

Michelangelo PyML: Introducing Uber’s Platform for Rapid Python ML Model Development

Uber developed Michelangelo PyML to run identical copies of machine learning models locally in both real time experiments and large-scale offline prediction jobs.
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Applying Customer Feedback: How NLP & Deep Learning Improve Uber’s Maps

To improve our maps, Uber Engineering analyzes customer support tickets with natural language processing and deep learning to identify and correct inaccurate map data.

Improving Driver Communication through One-Click Chat, Uber’s Smart Reply System

One-click chat, the Uber driver app's smart reply system, leverages machine learning to make in-app messaging between driver-partners and riders more seamless.
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Introducing Petastorm: Uber ATG’s Data Access Library for Deep Learning

Uber's Advanced Technologies Group introduces Petastorm, an open source data access library enabling training and evaluation of deep learning models directly from multi-terabyte datasets in Apache Parquet format.
Food Discovery with Uber Eats: Recommending for the Marketplace

Food Discovery with Uber Eats: Recommending for the Marketplace

Uber Eats engineers describe how they surface restaurant recommendations in the app using multi-objective optimization to give eaters the most satisfying experience while maintaining the health of the Uber Eats marketplace.

Forecasting at Uber: An Introduction

In this article, we provide a general overview of how our teams leverage forecasting to build better products and maintain the health of the Uber marketplace.

Scaling Uber’s Customer Support Ticket Assistant (COTA) System with Deep Learning

Uber built the next generation of COTA by leveraging deep learning models, thereby scaling the system to provide more accurate customer support ticket predictions.

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

As powerful and widespread as convolutional neural networks are in deep learning, AI Labs’ latest research reveals both an underappreciated failing and a simple fix.
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Transforming Financial Forecasting with Data Science and Machine Learning at Uber

Uber developed its own financial planning software, relying on data science and machine learning, to deliver on-demand forecasting and optimize strategic and operations decisions.

Measuring the Intrinsic Dimension of Objective Landscapes

Curious about what it is like to traverse the high-dimensional loss landscapes of modern neural networks? Check out Uber AI Labs’ latest research on measuring intrinsic dimension to find out.
Evolution to running

Accelerating Deep Neuroevolution: Train Atari in Hours on a Single Personal Computer

Applying hardware acceleration to deep neuroevolution in what is now an open source project, Uber AI Labs was able to train a neural network to play Atari in just a few hours on a single personal computer, making this type of research accessible to a far greater number of people.
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Differentiable Plasticity: A New Method for Learning to Learn

Differentiable Plasticity is a new machine learning method for training neural networks to change their connection weights adaptively even after training is completed, allowing a form of learning inspired by the lifelong plasticity of biological brains.

VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution

Uber AI Labs introduces Visual Inspector for Neuroevolution (VINE), an open source interactive data visualization tool to help neuroevolution researchers better understand this family of algorithms.

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.

Omphalos, Uber’s Parallel and Language-Extensible Time Series Backtesting Tool

Uber Engineering created Omphalos, our new backtesting framework, to enable efficient and reliable comparison of forecasting models across languages.

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.

COTA: Improving Uber Customer Care with NLP & Machine Learning

In this article, Uber Engineering introduces our Customer Obsession Ticket Assistant (COTA), a new tool that puts machine learning and natural language processing models in the service of customer care to help agents deliver improved support experiences.

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