{ "_id": "6a1ad4741d7bb097a09e418f", "Package": "LBBNN", "Title": "Latent Binary Bayesian Neural Networks Using 'torch'", "Version": "0.1.5", "Authors@R": "c(person(\"Lars\", \"Skaaret-Lund\", email = \"lars.skaaret-lund@nmbu.no\", role = c(\"aut\", \"cre\")), person(\"Aliaksandr\", \"Hubin\", email = \"aliaksandr.hubin@nmbu.no\", role = c(\"aut\")), person(\"Eirik\", \"Høyheim\", email = \"eirik.hoyheim@ffi.no\", role = \"aut\"))", "Maintainer": "Lars Skaaret-Lund ", "Description": "Latent binary Bayesian neural networks (LBBNNs) are\nimplemented using 'torch', an R interface to the LibTorch\nbackend. Supports mean-field variational inference as well as\nflexible variational posteriors using normalizing flows. The\nstandard LBBNN implementation follows Hubin and Storvik (2024)\n, using the local reparametrization\ntrick as in Skaaret-Lund et al. (2024)\n. 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