Browsing Computer Science by Author "Haußmann, M."
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Sampling-free variational inference of bayesian neural networks by variance backpropagation
Haußmann, M.; Hamprecht, F. A.; Kandemir, Melih (ML Research Press, 2020)We propose a new Bayesian Neural Net formulation that affords variational inference for which the evidence lower bound is analytically tractable subject to a tight approximation. We achieve this tractability by (i) decomposing ... -
Sampling-free variational inference of Bayesian neural networks by variance backpropagation
Haußmann, M.; Hamprecht, F. A.; Kandemir, Melih (Association For Uncertainty in Artificial Intelligence (AUAI), 2019)We propose a new Bayesian Neural Net formulation that affords variational inference for which the evidence lower bound is analytically tractable subject to a tight approximation. We achieve this tractability by (i) decomposing ...
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