首页|New Machine Learning Data Have Been Reported by Investigators at Karlsruhe Insti tute of Technology (KIT) [Easy Uncertainty Quantification (Ea syuq): Generating Predictive Distributions From Single-valued Model Output\ ast]
New Machine Learning Data Have Been Reported by Investigators at Karlsruhe Insti tute of Technology (KIT) [Easy Uncertainty Quantification (Ea syuq): Generating Predictive Distributions From Single-valued Model Output\ ast]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news reporting out of Karlsruhe, Germany, by News Rx editors, research stated, “How can we quantify uncertainty if our favorite co mputational tool—be it a numerical, statistical, or machine learning approach, or just any computer model-provides singlevalued output only? In this article, we introduce the Easy Uncertainty Quantification (EasyUQ) technique, which trans forms real-valued model output into calibrated statistical distributions, based solely on training data of model output–outcome pairs, without any need to acce ss model input. In its basic form, EasyUQ is a special case of the recently intr oduced isotonic distributional regression (IDR) technique that leverages the poo l-adjacent-violators algorithm for nonparametric isotonic regression.” Financial supporters for this research include German Research Foundation (DFG), Swiss National Science Foundation (SNSF), Klaus Tschira Foundation.
KarlsruheGermanyEuropeCyborgsEme rging TechnologiesMachine LearningKarlsruhe Institute of Technology (KIT)
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