2287 results for "earn" across all locations
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.
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.
Heterogeneous Causal Learning for Effectiveness Optimization in User Marketing
W. Y. Zou, S. Du, J. Lee, J. Pedersen
User marketing is a key focus of consumer-based internet companies. Learning algorithms are effective to optimize marketing campaigns which increase user engagement, and facilitates cross-marketing to related products. By attracting users with rewards, marketing methods are effective to boost user activity in the desired products. Rewards incur significant cost that can be off-set by increase in future revenue. […] [PDF]
2020
The Mirage of Action-Dependent Baselines in Reinforcement Learning
G. Tucker, S. Bhupatiraju, S. Gu, R. Turner, Z. Ghahramani, S. Levine
Policy gradient methods are a widely used class of model-free reinforcement learning algorithms where a state-dependent baseline is used to reduce gradient estimator variance. Several recent papers extend the baseline to depend on both the state and action and suggest that this significantly reduces variance and improves sample efficiency without introducing bias into the gradient estimates. […] [PDF]
International Conference on Machine Learning (ICML), 2018
Uber’s Journey to Ray on Kubernetes: Resource Management
Learn about the novel enhancements Uber made to Kubernetes® to run Ray®-based jobs with optimal resource management. This is the second blog in our series about Ray on Kubernetes.
Migrating a Trillion Entries of Uber’s Ledger Data from DynamoDB to LedgerStore
Migrating money data with peace of mind. Learn how Uber moved its Money related data spanning trillion of rows & petabytes in size flawlessly.
Russell launched his Engineering career as an UberSTAR intern
Learn about Russell’s engineering internship at UberSTAR, our program enabling students with real-work skills, experience, and professional network to succeed in tech.
Meet Uber Engineering Seattle
In this article we take a look inside the Uber Engineering Seattle office, meet a few of the leaders, and learn about the culture and technologies the teams are working on.
Open Sourcing Manifold, a Visual Debugging Tool for Machine Learning
First introduced by Uber Engineering in January 2019, Manifold is a visual debugging tool that enables users to quickly identify performance issues in machine learning models.
Uber Elevate – Meet the Team
We met with Tom Prevot, Director, Airspace Systems to learn about the team’s plans for a shared air transportation service planned for 2023.