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cs.LG2024
Debiasing Mini-Batch Quadratics for Applications in Deep Learning
Lukas Tatzel, Bálint Mucsányi, Osane Hackel +1
Quadratic approximations form a fundamental building block of machine learning methods. E.g., second-order optimizers try to find the Newton step into the minimum of a local quadra…
cs.LG2024
Reparameterization invariance in approximate Bayesian inference
Hrittik Roy, Marco Miani, Carl Henrik Ek +4
Current approximate posteriors in Bayesian neural networks (BNNs) exhibit a crucial limitation: they fail to maintain invariance under reparameterization, i.e. BNNs assign differen…