Marginalized Neural Network Mixtures for Large-Scale Regression

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Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, background): “Recently, such basis functions have also been learned from data by training deep belief networks [71], deep neural networks [93], [144], or by factoring the empirical covariance matrix of historical data [146], [72].”
Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors (2018, methodology): “We use a weight prior for only the output layer (Lázaro-Gredilla and Figueiras-Vidal, 2010; Calandra et al., 2014) that predicts the mean, resulting in the model We do not model uncertainty about the noise estimate, as this is not required for the approximation for the Gaussian expected information…”
Noise Contrastive Priors for Functional Uncertainty (2018, methodology): “We apply NCP to posit epistemic uncertainty on the output of the mean μ, and we infer the induced weight posterior for only the output layer (Lázaro-Gredilla and Figueiras-Vidal, 2010; Calandra et al., 2014) that predicts the mean.”