{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/implicit-weight-uncertainty-in-neural","title":"Implicit Weight Uncertainty in Neural Networks","arxiv_id":"1711.01297","date":"2017-11-03","proceeding":null,"authors":["Nick Pawlowski","Andrew Brock","Matthew C. H. Lee","Martin Rajchl","Ben Glocker"],"abstract":"Modern neural networks tend to be overconfident on unseen, noisy or\nincorrectly labelled data and do not produce meaningful uncertainty measures.\nBayesian deep learning aims to address this shortcoming with variational\napproximations (such as Bayes by Backprop or Multiplicative Normalising Flows).\nHowever, current approaches have limitations regarding flexibility and\nscalability. We introduce Bayes by Hypernet (BbH), a new method of variational\napproximation that interprets hypernetworks as implicit distributions. It\nnaturally uses neural networks to model arbitrarily complex distributions and\nscales to modern deep learning architectures. In our experiments, we\ndemonstrate that our method achieves competitive accuracies and predictive\nuncertainties on MNIST and a CIFAR5 task, while being the most robust against\nadversarial attacks.","url_abs":"http://arxiv.org/abs/1711.01297v2","url_pdf":"http://arxiv.org/pdf/1711.01297v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"implicit-weight-uncertainty-in-neural","repo_url":"https://github.com/pawni/BayesByHypernet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"implicit-weight-uncertainty-in-neural","repo_url":"https://github.com/beauCoker/bayesian_neural_networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"normalising-flows","task_name":"Normalising Flows"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1711.01297","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.01297"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/pawni/BayesByHypernet","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/beauCoker/bayesian_neural_networks","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"fe8746a4dd93bc88","entry":"get_activation","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"fe8746a4dd93bc88"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}