Langevin-gradient parallel tempering for Bayesian neural learning

11 Nov 2018Rohitash ChandraKonark JainRatneel V. DeoSally Cripps

Bayesian neural learning feature a rigorous approach to estimation and uncertainty quantification via the posterior distribution of weights that represent knowledge of the neural network. This not only provides point estimates of optimal set of weights but also the ability to quantify uncertainty in decision making using the posterior distribution... (read more)

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