首页|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)
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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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).
SeattleWashingtonUnited StatesNort h and Central AmericaCyborgsEmerging TechnologiesMachine LearningUnivers ity of Washington
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