Skip to main content
U.S. Department of Energy
Office of Scientific and Technical Information

Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO

Journal Article · · IEEE Transactions on Pattern Analysis and Machine Intelligence

Not provided.

Research Organization:
Northwestern Univ., Evanston, IL (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
DOE Contract Number:
NA0002520
OSTI ID:
1980537
Journal Information:
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, Issue 3; ISSN 0162-8828
Publisher:
IEEE
Country of Publication:
United States
Language:
English

References (25)

Variational Inference: A Review for Statisticians journal July 2016
Scalable and efficient learning from crowds with Gaussian processes journal December 2019
Remote Sensing Image Classification With Large-Scale Gaussian Processes journal February 2018
Citizen science, public policy journal July 2018
No PhDs needed: how citizen science is transforming research journal October 2018
Learning from multiple annotators with varying expertise journal October 2013
Learning from crowds with variational Gaussian processes journal April 2019
Characterization of transient noise in Advanced LIGO relevant to gravitational wave signal GW150914 journal June 2016
Improving the data quality of Advanced LIGO based on early engineering run results journal December 2015
Gradient-based learning applied to document recognition journal January 1998
Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science journal February 2017
LIGO: The Laser Interferometer Gravitational-Wave Observatory journal April 1992
Observation of Gravitational Waves from a Binary Black Hole Merger journal February 2016
Einstein's gravitational waves found at last journal February 2016
Get another label? improving data quality and data mining using multiple, noisy labelers
  • Sheng, Victor S.; Provost, Foster; Ipeirotis, Panagiotis G.
  • Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08 https://doi.org/10.1145/1401890.1401965
conference January 2008
Proactive learning conference January 2008
Maximum Likelihood Estimation of Observer Error-Rates Using the EM Algorithm journal January 1979
Quality management on Amazon Mechanical Turk conference July 2010
Amazon's Mechanical Turk: A New Source of Inexpensive, Yet High-Quality, Data? journal January 2011
Cheap and fast---but is it good? conference January 2008
Crowdsourcing biomedical research: leveraging communities as innovation engines journal July 2016
AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images journal May 2016
Learning Supervised Topic Models for Classification and Regression from Crowds journal December 2017
Machine learning for Gravity Spy: Glitch classification and dataset journal May 2018
Clickstream Analysis for Crowd-Based Object Segmentation with Confidence journal December 2018

Similar Records

Related Subjects