{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:WIKLIHQ42TWM7AYLI226RED233","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":"2ba38a9801800a7a4b3697e4ac687581c816ab7cf28ba5e608d7dd4f698ea77e","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2020-04-17T08:39:58Z","title_canon_sha256":"4296c421175091a5aeec7e04b1f9b8f6a05aaa34e50f93a96bdfd86f55f82c30"},"schema_version":"1.0","source":{"id":"2004.08113","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2004.08113","created_at":"2026-07-05T03:48:36Z"},{"alias_kind":"arxiv_version","alias_value":"2004.08113v3","created_at":"2026-07-05T03:48:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.08113","created_at":"2026-07-05T03:48:36Z"},{"alias_kind":"pith_short_12","alias_value":"WIKLIHQ42TWM","created_at":"2026-07-05T03:48:36Z"},{"alias_kind":"pith_short_16","alias_value":"WIKLIHQ42TWM7AYL","created_at":"2026-07-05T03:48:36Z"},{"alias_kind":"pith_short_8","alias_value":"WIKLIHQ4","created_at":"2026-07-05T03:48:36Z"}],"graph_snapshots":[{"event_id":"sha256:7eaba719cc64ae2ab28cb412cf43dffd358710264eb75c2f26a5e24f00d8d030","target":"graph","created_at":"2026-07-05T03:48:36Z","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/2004.08113/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-label learning deals with the problem that each instance is associated with multiple labels simultaneously. Most of the existing approaches aim to improve the performance of multi-label learning by exploiting label correlations. Although the data augmentation technique is widely used in many machine learning tasks, it is still unclear whether data augmentation is helpful to multi-label learning. In this article, we propose to leverage the data augmentation technique to improve the performance of multi-label learning. Specifically, we first propose a novel data augmentation approach that ","authors_text":"Fengmao Lv, Jun He, Li Li, Senlin Shu, Shuo He, Yan Yan","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2020-04-17T08:39:58Z","title":"Incorporating Multiple Cluster Centers for Multi-Label Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.08113","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":"sha256:8e6e6fe5a75efffc08c2f00d3d99c01d64a143b2ab8fd8f20995d4e4031087ae","target":"record","created_at":"2026-07-05T03:48:36Z","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":"2ba38a9801800a7a4b3697e4ac687581c816ab7cf28ba5e608d7dd4f698ea77e","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2020-04-17T08:39:58Z","title_canon_sha256":"4296c421175091a5aeec7e04b1f9b8f6a05aaa34e50f93a96bdfd86f55f82c30"},"schema_version":"1.0","source":{"id":"2004.08113","kind":"arxiv","version":3}},"canonical_sha256":"b214b41e1cd4eccf830b46b5e8907adeed6991d6dec262be3ea767d0f91a41ed","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b214b41e1cd4eccf830b46b5e8907adeed6991d6dec262be3ea767d0f91a41ed","first_computed_at":"2026-07-05T03:48:36.444776Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:48:36.444776Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Xk14hO2Bp4kiS29kypg/klIUujP/Q4IWKAaBsHHhF2nfNh+cxzFiPpmk7IcwT54TBQroAf4LmWfpjfbFi4EFCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:48:36.445179Z","signed_message":"canonical_sha256_bytes"},"source_id":"2004.08113","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8e6e6fe5a75efffc08c2f00d3d99c01d64a143b2ab8fd8f20995d4e4031087ae","sha256:7eaba719cc64ae2ab28cb412cf43dffd358710264eb75c2f26a5e24f00d8d030"],"state_sha256":"1f1c0554d54a12f33ea0920879dc2861b99bb42a40faee97d0b640bd85822972"}