Robotics & Machine Learning Daily News2024,Issue(Sep.19) :78-78.

New Findings from University of Washington Describe Advances in Machine Learning (Uncertainty Quantification In the Machinelearning Inference From Neutron Star Probability Distribution To the Equation of State)

Robotics & Machine Learning Daily News2024,Issue(Sep.19) :78-78.

New Findings from University of Washington Describe Advances in Machine Learning (Uncertainty Quantification In the Machinelearning Inference From Neutron Star Probability Distribution To the Equation of State)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Machine Learning are discussed in a new report. According to news originating from Seattle, Washi ngton, by NewsRx correspondents, research stated, “We discuss the machine-learni ng inference and uncertainty quantification for the equation of state (EOS) of t he neutron star matter directly using the NS probability distribution from the o bservations. We previously proposed a prescription for uncertainty quantificatio n based on ensemble learning by evaluating output variance from independently tr ained models.” Financial supporters for this research include Japan Society for the Promotion o f Science, United States Department of Energy (DOE), Grants-in-Aid for Scientifi c Research (KAKENHI).

Key words

Seattle/Washington/United States/Nort h and Central America/Cyborgs/Emerging Technologies/Machine Learning/Univers ity of Washington

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
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