Skip to main content

Carlea Holl-Jensen||


Quinn, A., Bederson, B., Yeh, T., Lin, J. (May 2010)
CrowdFlow: Integrating Machine Learning with Mechanical Turk for Speed-Cost-Quality Flexibility

Humans and machines have competing strengths for tasks such as natural language processing and image understanding. Whereas humans do these things naturally with potentially high accuracy, machines offer greater speed and flexibility. CrowdFlow is our toolkit for a model for blending the two in order to attain tighter control over the inherent tradeoffs in speed, cost and quality. With CrowdFlow, humans and machines work together to do a set of tasks at a user-specified point in the tradeoff space. They work symbiotically, with the humans providing training data to the machine while the machine provides first cut results to the humans to save effort in cases where the machine’s answer was already correct. The CrowdFlow toolkit can be considered as a generalization of our other domain-specific efforts aimed at enabling cloud computing services using a variety of computational resources to achieve various tradeoff points.

Community Analysis and Visualization Screenshot

Community Analysis and Visualization
More information

Tech Reports
Video Reports
Annual Symposium

Seminars + Events
HCIL Seminar Series
Annual Symposium
HCIL Service Grants
Events Archives
HCIL Conference Travel Award
Job Openings
For the Press
HCIL Overview
Become a Member
Collaborating Groups + People
Academic Visitors
Join our Mailing List
Contact Us
Visit Us
HCIL Store
Give the HCIL a Hand
HCIL T-shirts for Sale
Our Lighter Side
HCIL Memories Page
Faculty/ Staff
Ph.D. Alumni
Past Members
Research Areas
Design Process
Digital Libraries
Physical Devices
Public Access
Research Histories
Faculty Listed by Research
Project Highlights
Project Screenshots
Publications and TRs
Studying HCI
Masters in HCI
PhD in HCI
Visiting Scholars
Class Websites
Sponsor our Research
Sponsor our Annual Symposium
Active Sponsorship
Industrial Visitors