Accurate Uncertainties for Deep Learning Using Calibrated Regression

ICML 2018 Volodymyr KuleshovNathan FennerStefano Ermon

Methods for reasoning under uncertainty are a key building block of accurate and reliable machine learning systems. Bayesian methods provide a general framework to quantify uncertainty... (read more)

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