Robustly representing uncertainty in deep neural networks through sampling

5 Nov 2016Patrick McClureNikolaus Kriegeskorte

As deep neural networks (DNNs) are applied to increasingly challenging problems, they will need to be able to represent their own uncertainty. Modeling uncertainty is one of the key features of Bayesian methods... (read more)

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