{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:75A7QIGFM6TVIQY27WMUOA3R7R","short_pith_number":"pith:75A7QIGF","schema_version":"1.0","canonical_sha256":"ff41f820c567a754431afd99470371fc4037e0187e7dcf8ed153925cb492b35d","source":{"kind":"arxiv","id":"1805.08527","version":4},"attestation_state":"computed","paper":{"title":"Safe Element Screening for Submodular Function Minimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Bin Hong, Lin Ma, Tong Zhang, Wei Liu, Weizhong Zhang","submitted_at":"2018-05-22T11:58:11Z","abstract_excerpt":"Submodular functions are discrete analogs of convex functions, which have applications in various fields, including machine learning and computer vision. However, in large-scale applications, solving Submodular Function Minimization (SFM) problems remains challenging. In this paper, we make the first attempt to extend the emerging technique named screening in large-scale sparse learning to SFM for accelerating its optimization process. We first conduct a careful studying of the relationships between SFM and the corresponding convex proximal problems, as well as the accurate primal optimum esti"},"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":"1805.08527","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-22T11:58:11Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f8caeea05742b88177f5145ef2c376420c9446daec9d44f51704e25d348aaade","abstract_canon_sha256":"1582330968ba2e4e77708e547a2b4ee02e8d5c2be6a1bc1647519f981495d50d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:57.466852Z","signature_b64":"NEDHE/o+Enxeprui9hdVxMXVf+p9GobhD3SPvNVk3Cm6wG9MJpEeErSdCUBkI65wYtSYsyCbd7l4oxaUg12RAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff41f820c567a754431afd99470371fc4037e0187e7dcf8ed153925cb492b35d","last_reissued_at":"2026-05-18T00:13:57.465892Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:57.465892Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Safe Element Screening for Submodular Function Minimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Bin Hong, Lin Ma, Tong Zhang, Wei Liu, Weizhong Zhang","submitted_at":"2018-05-22T11:58:11Z","abstract_excerpt":"Submodular functions are discrete analogs of convex functions, which have applications in various fields, including machine learning and computer vision. However, in large-scale applications, solving Submodular Function Minimization (SFM) problems remains challenging. In this paper, we make the first attempt to extend the emerging technique named screening in large-scale sparse learning to SFM for accelerating its optimization process. We first conduct a careful studying of the relationships between SFM and the corresponding convex proximal problems, as well as the accurate primal optimum esti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.08527","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1805.08527","created_at":"2026-05-18T00:13:57.466061+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.08527v4","created_at":"2026-05-18T00:13:57.466061+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.08527","created_at":"2026-05-18T00:13:57.466061+00:00"},{"alias_kind":"pith_short_12","alias_value":"75A7QIGFM6TV","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_16","alias_value":"75A7QIGFM6TVIQY2","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_8","alias_value":"75A7QIGF","created_at":"2026-05-18T12:32:11.075285+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/75A7QIGFM6TVIQY27WMUOA3R7R","json":"https://pith.science/pith/75A7QIGFM6TVIQY27WMUOA3R7R.json","graph_json":"https://pith.science/api/pith-number/75A7QIGFM6TVIQY27WMUOA3R7R/graph.json","events_json":"https://pith.science/api/pith-number/75A7QIGFM6TVIQY27WMUOA3R7R/events.json","paper":"https://pith.science/paper/75A7QIGF"},"agent_actions":{"view_html":"https://pith.science/pith/75A7QIGFM6TVIQY27WMUOA3R7R","download_json":"https://pith.science/pith/75A7QIGFM6TVIQY27WMUOA3R7R.json","view_paper":"https://pith.science/paper/75A7QIGF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.08527&json=true","fetch_graph":"https://pith.science/api/pith-number/75A7QIGFM6TVIQY27WMUOA3R7R/graph.json","fetch_events":"https://pith.science/api/pith-number/75A7QIGFM6TVIQY27WMUOA3R7R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/75A7QIGFM6TVIQY27WMUOA3R7R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/75A7QIGFM6TVIQY27WMUOA3R7R/action/storage_attestation","attest_author":"https://pith.science/pith/75A7QIGFM6TVIQY27WMUOA3R7R/action/author_attestation","sign_citation":"https://pith.science/pith/75A7QIGFM6TVIQY27WMUOA3R7R/action/citation_signature","submit_replication":"https://pith.science/pith/75A7QIGFM6TVIQY27WMUOA3R7R/action/replication_record"}},"created_at":"2026-05-18T00:13:57.466061+00:00","updated_at":"2026-05-18T00:13:57.466061+00:00"}