{"paper":{"title":"Bayesian Hypernetworks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Aaron Courville, Alexandre Lacoste, Chin-Wei Huang, David Krueger, Riashat Islam, Ryan Turner","submitted_at":"2017-10-13T00:27:57Z","abstract_excerpt":"We study Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks. A Bayesian hypernetwork $\\h$ is a neural network which learns to transform a simple noise distribution, $p(\\vec\\epsilon) = \\N(\\vec 0,\\mat I)$, to a distribution $q(\\pp) := q(h(\\vec\\epsilon))$ over the parameters $\\pp$ of another neural network (the \"primary network\")\\@. We train $q$ with variational inference, using an invertible $\\h$ to enable efficient estimation of the variational lower bound on the posterior $p(\\pp | \\D)$ via sampling. In contrast to most methods for Bayesian deep learning, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.04759","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}