{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:D54UJ5CD4KTPRQY4L55K533OTJ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"02855f4ff5a12f5ed052d20cb7f3ab276af32ba5ad20830856fdef8a4b93612d","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2020-12-11T00:10:38Z","title_canon_sha256":"5e75f95680dd3c20fab685c81ead7ff787da5840bbda39d68b17fca7cde9e4e4"},"schema_version":"1.0","source":{"id":"2012.06046","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.06046","created_at":"2026-07-05T02:09:54Z"},{"alias_kind":"arxiv_version","alias_value":"2012.06046v2","created_at":"2026-07-05T02:09:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.06046","created_at":"2026-07-05T02:09:54Z"},{"alias_kind":"pith_short_12","alias_value":"D54UJ5CD4KTP","created_at":"2026-07-05T02:09:54Z"},{"alias_kind":"pith_short_16","alias_value":"D54UJ5CD4KTPRQY4","created_at":"2026-07-05T02:09:54Z"},{"alias_kind":"pith_short_8","alias_value":"D54UJ5CD","created_at":"2026-07-05T02:09:54Z"}],"graph_snapshots":[{"event_id":"sha256:619ded016744e26caa3e7a75ab5648d4d77ae4bf2c9e9b8f0787151e4047625a","target":"graph","created_at":"2026-07-05T02:09:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2012.06046/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Obtaining large annotated datasets is critical for training successful machine learning models and it is often a bottleneck in practice. Weak supervision offers a promising alternative for producing labeled datasets without ground truth annotations by generating probabilistic labels using multiple noisy heuristics. This process can scale to large datasets and has demonstrated state of the art performance in diverse domains such as healthcare and e-commerce. One practical issue with learning from user-generated heuristics is that their creation requires creativity, foresight, and domain experti","authors_text":"Artur Dubrawski, Benedikt Boecking, Eric Xing, Willie Neiswanger","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2020-12-11T00:10:38Z","title":"Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.06046","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1cd11df3f9ae009903035a2112a05d0cb588735b973fa88b80e54dc6923fc20b","target":"record","created_at":"2026-07-05T02:09:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"02855f4ff5a12f5ed052d20cb7f3ab276af32ba5ad20830856fdef8a4b93612d","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2020-12-11T00:10:38Z","title_canon_sha256":"5e75f95680dd3c20fab685c81ead7ff787da5840bbda39d68b17fca7cde9e4e4"},"schema_version":"1.0","source":{"id":"2012.06046","kind":"arxiv","version":2}},"canonical_sha256":"1f7944f443e2a6f8c31c5f7aaeef6e9a5ba2a1147d1f9280764c95e2e2b19be9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1f7944f443e2a6f8c31c5f7aaeef6e9a5ba2a1147d1f9280764c95e2e2b19be9","first_computed_at":"2026-07-05T02:09:54.786822Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:09:54.786822Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ALlq+hW5LTn1b/5vGOdZH42mvez/5iLqQq+o5jCfNLVEzCFQ78+jyjywaotkmXH72Fg7/0KiZeNTN4Ng4MszDg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:09:54.787287Z","signed_message":"canonical_sha256_bytes"},"source_id":"2012.06046","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1cd11df3f9ae009903035a2112a05d0cb588735b973fa88b80e54dc6923fc20b","sha256:619ded016744e26caa3e7a75ab5648d4d77ae4bf2c9e9b8f0787151e4047625a"],"state_sha256":"2bda9445dfd273c7b6173062f17b1bf63ec1331c703e486a777f7d5fdaf2db78"}