2217 results for "earn" across all locations
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.
Uber partners with A21 to fight Human Trafficking in SA
In honour of Human Trafficking Awareness week in South Africa, Uber is making a commitment to raise awareness around this heinous crime by developing new educational materials with guidance from A21, and sending them to drivers, and Uber Eats delivery-partners across South Africa, so they can learn how human trafficking works, how it may present, and how they can report or reach out for help.
Samsara makes virtual events special with Vouchers for Uber Eats
Samsara makes virtual events special with Vouchers for Uber Eats. Given that Samsara’s internal sales kick-off lunch was just a one-day need, Vouchers for Uber Eats made more sense than an ongoing corporate meal program. Since the success of the SKO, the Samsara team has already started using Vouchers for Uber Eats to connect with employees, customers, and prospects during lunch-and-learns and other virtual events.
Ringing in 2017 in India
After a several New Year’s Eves under our belt, we’ve learned that millions of people in cities around the world rely on Uber for a safe, reliable ride to and from their celebrations. This year, we wanted to spotlight how riders in India used Uber when ringing in the New Year. Check out these 5 fun facts!
Samsara makes virtual events special with Vouchers for Uber Eats
Samsara makes virtual events special with Vouchers for Uber Eats. Given that Samsara’s internal sales kick-off lunch was just a one-day need, Vouchers for Uber Eats made more sense than an ongoing corporate meal program. Since the success of the SKO, the Samsara team has already started using Vouchers for Uber Eats to connect with employees, customers, and prospects during lunch-and-learns and other virtual events.
Robustness to out-of-distribution inputs via taskaware generative uncertainty
R. McAllister, G. Kahn, J. Clune, S. Levine
Deep learning provides a powerful tool for machine perception when the observations resemble the training data. However, real-world robotic systems must react intelligently to their observations even in unexpected circumstances. This requires a system to reason about its own uncertainty given unfamiliar, out-of-distribution observations. […] [PDF]
International Conference on Robotics and Automation (ICRA), 2019
Leveraging Constraint Logic Programming for Neural Guided Program Synthesis
L. Zhang, G. Rosenblatt, E. Fetaya, R. Liao, W. Byrd, R. Urtasun, R. Zemel
We present a method for solving Programming by Example (PBE) problems that tightly integrates a neural network with a constraint logic programming system called miniKanren. Internally, miniKanren searches for a program that satisfies the recursive constraints imposed by the provided examples. […] [PDF]
International Conference on Machine Learning (ICLR), 2018
Graph Partition Neural Networks for Semi-Supervised Classification
R. Liao, M. Brockschmidt, D. Tarlow, A. Gaunt, R. Urtasun, R. Zemel
We present graph partition neural networks (GPNN), an extension of graph neural networks (GNNs) able to handle extremely large graphs. GPNNs alternate between locally propagating information between nodes in small subgraphs and globally propagating information between the subgraphs. […] [PDF]
Workshop @ International Conference on Machine Learning (ICLR), 2018
Magnetic Hamiltonian Monte Carlo
N. Tripuraneni, M. Rowland, Z. Ghahramani, R. Turner
Hamiltonian Monte Carlo (HMC) exploits Hamiltonian dynamics to construct efficient proposals for Markov chain Monte Carlo (MCMC). In this paper, we present a generalization of HMC which exploits textit{non-canonical} Hamiltonian dynamics. […] [PDF]
International Conference on Machine Learning (ICML), 2017
A birth-death process for feature allocation
K. Palla, D. Knowles, Z. Ghahramani
We propose a Bayesian nonparametric prior over feature allocations for sequential data, the birthdeath feature allocation process (BDFP). The BDFP models the evolution of the feature allocation of a set of N objects across a covariate (e.g. time) by creating and deleting features. […] [PDF]
International Conference on Machine Learning (ICML), 2017