{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:IGDQG2EPPBCJNZ2F7CUYDTHF62","short_pith_number":"pith:IGDQG2EP","schema_version":"1.0","canonical_sha256":"418703688f784496e745f8a981cce5f684a4a5e24de8444cb9fa49d9b03be0ba","source":{"kind":"arxiv","id":"2405.18351","version":1},"attestation_state":"computed","paper":{"title":"Evaluating Bayesian deep learning for radio galaxy classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"cs.LG","authors_text":"Anna M. M. Scaife, Devina Mohan","submitted_at":"2024-05-28T16:49:28Z","abstract_excerpt":"The radio astronomy community is rapidly adopting deep learning techniques to deal with the huge data volumes expected from the next generation of radio observatories. Bayesian neural networks (BNNs) provide a principled way to model uncertainty in the predictions made by such deep learning models and will play an important role in extracting well-calibrated uncertainty estimates on their outputs. In this work, we evaluate the performance of different BNNs against the following criteria: predictive performance, uncertainty calibration and distribution-shift detection for the radio galaxy class"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2405.18351","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-28T16:49:28Z","cross_cats_sorted":["astro-ph.IM"],"title_canon_sha256":"5524d1849d9694b10ebd6eed74bc2bd17ae0bc541da8a4990c7ab8e9e413da19","abstract_canon_sha256":"c16cde53ba1226fa77505f5ce327cf4120b05a0d0582806df2cf5a78a1c428fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:24:18.941066Z","signature_b64":"lP9ZoW0daPYJ312nRPfIYBS2MfI57ubvWvaHfEuu4cubaOEZH2RJpSEJz5NE78I/TLsaLXYdle5zKGC6dx9hDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"418703688f784496e745f8a981cce5f684a4a5e24de8444cb9fa49d9b03be0ba","last_reissued_at":"2026-07-05T08:24:18.940672Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:24:18.940672Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating Bayesian deep learning for radio galaxy classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM"],"primary_cat":"cs.LG","authors_text":"Anna M. M. Scaife, Devina Mohan","submitted_at":"2024-05-28T16:49:28Z","abstract_excerpt":"The radio astronomy community is rapidly adopting deep learning techniques to deal with the huge data volumes expected from the next generation of radio observatories. Bayesian neural networks (BNNs) provide a principled way to model uncertainty in the predictions made by such deep learning models and will play an important role in extracting well-calibrated uncertainty estimates on their outputs. In this work, we evaluate the performance of different BNNs against the following criteria: predictive performance, uncertainty calibration and distribution-shift detection for the radio galaxy class"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.18351","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2405.18351/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2405.18351","created_at":"2026-07-05T08:24:18.940732+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.18351v1","created_at":"2026-07-05T08:24:18.940732+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.18351","created_at":"2026-07-05T08:24:18.940732+00:00"},{"alias_kind":"pith_short_12","alias_value":"IGDQG2EPPBCJ","created_at":"2026-07-05T08:24:18.940732+00:00"},{"alias_kind":"pith_short_16","alias_value":"IGDQG2EPPBCJNZ2F","created_at":"2026-07-05T08:24:18.940732+00:00"},{"alias_kind":"pith_short_8","alias_value":"IGDQG2EP","created_at":"2026-07-05T08:24:18.940732+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.04176","citing_title":"Star-forming clump detection in nearby galaxies using Faster R-CNN and $ugrizy$ imaging data from CLAUDS and HSC-SSP","ref_index":285,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IGDQG2EPPBCJNZ2F7CUYDTHF62","json":"https://pith.science/pith/IGDQG2EPPBCJNZ2F7CUYDTHF62.json","graph_json":"https://pith.science/api/pith-number/IGDQG2EPPBCJNZ2F7CUYDTHF62/graph.json","events_json":"https://pith.science/api/pith-number/IGDQG2EPPBCJNZ2F7CUYDTHF62/events.json","paper":"https://pith.science/paper/IGDQG2EP"},"agent_actions":{"view_html":"https://pith.science/pith/IGDQG2EPPBCJNZ2F7CUYDTHF62","download_json":"https://pith.science/pith/IGDQG2EPPBCJNZ2F7CUYDTHF62.json","view_paper":"https://pith.science/paper/IGDQG2EP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.18351&json=true","fetch_graph":"https://pith.science/api/pith-number/IGDQG2EPPBCJNZ2F7CUYDTHF62/graph.json","fetch_events":"https://pith.science/api/pith-number/IGDQG2EPPBCJNZ2F7CUYDTHF62/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IGDQG2EPPBCJNZ2F7CUYDTHF62/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IGDQG2EPPBCJNZ2F7CUYDTHF62/action/storage_attestation","attest_author":"https://pith.science/pith/IGDQG2EPPBCJNZ2F7CUYDTHF62/action/author_attestation","sign_citation":"https://pith.science/pith/IGDQG2EPPBCJNZ2F7CUYDTHF62/action/citation_signature","submit_replication":"https://pith.science/pith/IGDQG2EPPBCJNZ2F7CUYDTHF62/action/replication_record"}},"created_at":"2026-07-05T08:24:18.940732+00:00","updated_at":"2026-07-05T08:24:18.940732+00:00"}