Tag: AI Labs
Zoubin Ghahramani, Head of Uber AI, discusses how we use artificial intelligence techniques to make our platform more efficient for users.
The Plato Research Dialogue System enables experts and non-experts alike to quickly build, train, and deploy conversational AI agents.
Uber builds upon the Lottery Ticket Hypothesis by proposing explanations behind these mechanisms and deriving a surprising by-product: the Supermask.
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.
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.
During an October 2018 meetup, members of our Women in Statistics, Data, Optimization, and Machine Learning (WiSDOM) group presented on their technical work at Uber.
Metropolis-Hastings Generative Adversarial Networks (GANs) leverage the discriminator to pick better samples from the generator after ML model training is done.
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.
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.
Uber Engineering introduces Michelangelo, our machine learning-as-a-service system that enables teams to easily build, deploy, and operate ML solutions at scale.