{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:VWVAAVWL5UBSBCWMLB7YJFU4VG","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":"67f297169a788cdb62048f3d02f8889169570e915ba08ccb51fa89990f8ddd25","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-05T12:07:36Z","title_canon_sha256":"71e7d068d1a89cf4711be239144da9c31b2a312998c34825ae914485bfc86eb8"},"schema_version":"1.0","source":{"id":"2003.02556","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.02556","created_at":"2026-07-05T00:46:35Z"},{"alias_kind":"arxiv_version","alias_value":"2003.02556v3","created_at":"2026-07-05T00:46:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.02556","created_at":"2026-07-05T00:46:35Z"},{"alias_kind":"pith_short_12","alias_value":"VWVAAVWL5UBS","created_at":"2026-07-05T00:46:35Z"},{"alias_kind":"pith_short_16","alias_value":"VWVAAVWL5UBSBCWM","created_at":"2026-07-05T00:46:35Z"},{"alias_kind":"pith_short_8","alias_value":"VWVAAVWL","created_at":"2026-07-05T00:46:35Z"}],"graph_snapshots":[{"event_id":"sha256:c77583962934ac96b435cdfd0711572dd402983de1b464bb4af8ec4b98d283b2","target":"graph","created_at":"2026-07-05T00:46:35Z","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/2003.02556/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning techniques have been widely applied in Internet companies for various tasks, acting as an essential driving force, and feature engineering has been generally recognized as a crucial tache when constructing machine learning systems. Recently, a growing effort has been made to the development of automatic feature engineering methods, so that the substantial and tedious manual effort can be liberated. However, for industrial tasks, the efficiency and scalability of these methods are still far from satisfactory. In this paper, we proposed a staged method named SAFE (Scalable Autom","authors_text":"Jun Zhou, Longfei Li, Meng Li, Qitao Shi, Xinxing Yang, Ya-Lin Zhang","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-05T12:07:36Z","title":"SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.02556","kind":"arxiv","version":3},"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:d0bd23d7a0b7edb023a3dd293e34978cac08823d623fe4a50b53a7e782735ce6","target":"record","created_at":"2026-07-05T00:46:35Z","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":"67f297169a788cdb62048f3d02f8889169570e915ba08ccb51fa89990f8ddd25","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-05T12:07:36Z","title_canon_sha256":"71e7d068d1a89cf4711be239144da9c31b2a312998c34825ae914485bfc86eb8"},"schema_version":"1.0","source":{"id":"2003.02556","kind":"arxiv","version":3}},"canonical_sha256":"adaa0056cbed03208acc587f84969ca99bbf47530753ca0dfb5426112ca4bf51","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"adaa0056cbed03208acc587f84969ca99bbf47530753ca0dfb5426112ca4bf51","first_computed_at":"2026-07-05T00:46:35.561023Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:46:35.561023Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"htZtnWJ/wgmkBIIzUntU7HgCyOz7ASFqxH/pBjOr5IoqOmSIO9N5joFWfkWw0DQOrHVQwOG60qtVT6Tn+QxRAg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:46:35.561443Z","signed_message":"canonical_sha256_bytes"},"source_id":"2003.02556","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d0bd23d7a0b7edb023a3dd293e34978cac08823d623fe4a50b53a7e782735ce6","sha256:c77583962934ac96b435cdfd0711572dd402983de1b464bb4af8ec4b98d283b2"],"state_sha256":"706f6def24f0526b6c6e9a3e9d8b121585d5356ae4aa260d2663b44c8d905918"}