Browsing by Author "Hamprecht, F. A."
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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 ... -
Variational bayesian multiple instance learning with gaussian processes
Haussmann, M.; Hamprecht, F. A.; Kandemir, Melih (IEEE, 2017)Gaussian Processes (GPs) are effective Bayesian predictors. We here show for the first time that instance labels of a GP classifier can be inferred in the multiple instance learning (MIL) setting using variational Bayes. ...
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