首页|Reports Outline Machine Learning Study Results from Johns Hopkins University (He teroscedastic Gaussian Process Regression for Material Structure-property Relati onship Modeling)
Reports Outline Machine Learning Study Results from Johns Hopkins University (He teroscedastic Gaussian Process Regression for Material Structure-property Relati onship Modeling)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting out of Baltimore, Maryland, by NewsRx editors, research stated, “Uncertainty quantification is a critical a spect of machine learning models for material property predictions. Gaussian Pro cess Regression (GPR) is a popular technique for capturing uncertainties, but mo st existing models assume homoscedastic aleatoric uncertainty (noise), which may not adequately represent the heteroscedastic behavior observed in real-world da tasets.”
BaltimoreMarylandUnited StatesNort h and Central AmericaCyborgsEmerging TechnologiesGaussian ProcessesMachi ne LearningJohns Hopkins University
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