首页|New Machine Learning Findings from Polytechnic University Torino Outlined (Conse rvative Gaussian Process Models for Uncertainty Quantification and Bayesian Opti mization In Signal Integrity Applications)
New Machine Learning Findings from Polytechnic University Torino Outlined (Conse rvative Gaussian Process Models for Uncertainty Quantification and Bayesian Opti mization In Signal Integrity Applications)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting out of Turin, Italy, by NewsRx edito rs, the research stated, “Surrogate modeling is being increasingly adopted in si gnal and power integrity analysis to assist design exploration, optimization, an d uncertainty quantification (UQ) tasks. In this scenario, machine learning meth ods are attracting an ever-growing interest over alternative and well-consolidated techniques due to their data-driven nature.”
TurinItalyEuropeCyborgsEmerging TechnologiesGaussian ProcessesMachine LearningPolytechnic University Torino
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