laplace approximation
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Effective Bayesian Heteroscedastic Regression with Deep Neural Networks Alexander Immer
Further, we emphasize the significance of principled regularization of the network parameters and prediction.
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Riemannian Laplace approximations for Bayesian neural networks
However, practical posteriors are often, even locally, highly non-Gaussian, and empirical performance deteriorates.
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Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Fredrik Lindsten, Jouni Helske, Matti Vihola
Neural Information Processing Systems http://nips.cc/
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Learning Layer-wise Equivariances Automatically using Gradients Tycho F.A. van der Ouderaa
First, it requires efficient and flexible parameterisations of layer-wise equivari-ances.
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Shaving WeightswithOccam'sRazor: BayesianSparsificationforNeuralNetworks usingtheMarginalLikelihood
Whilemuchwork has focused on different weight pruning criteria, the overallsparsifiabilityofthe network, i.e., its capacity to be pruned without quality loss, has often been overlooked.
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