Layer-wise Training of a BNN Based on Progressive Gaussian Filtering
Leon Winheim, U. Hanebeck
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Probabilistic Regression via Neural Network Ensembles
DENSE is an ensemble of multi layer perceptrons (MLPs) that is trained to minimize a modified Cram´er–von Mises distance in the joint space of inputs and outputs that enables non-parametric representations of arbitrary densities without (complex) likelihood design.
Leon Winheim, Hayk Amirkhanian, MarkusWalker et al.
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