{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:OZDYZ3PQCGSTYG2F3BQ4RVS2LN","short_pith_number":"pith:OZDYZ3PQ","canonical_record":{"source":{"id":"1804.07759","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-20T08:04:32Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"836a3518e9bbe1a2b4e4ad623f37dcdc30f327b4487cfb60c0111a82d15ddb84","abstract_canon_sha256":"54e5ca96b0fc736a5a3f7df1cf491b481bf63b9cc91220e67040302f032e2b98"},"schema_version":"1.0"},"canonical_sha256":"76478cedf011a53c1b45d861c8d65a5b487aa0f5e956760ddb652de762962b95","source":{"kind":"arxiv","id":"1804.07759","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.07759","created_at":"2026-05-18T00:16:36Z"},{"alias_kind":"arxiv_version","alias_value":"1804.07759v2","created_at":"2026-05-18T00:16:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.07759","created_at":"2026-05-18T00:16:36Z"},{"alias_kind":"pith_short_12","alias_value":"OZDYZ3PQCGST","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OZDYZ3PQCGSTYG2F","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OZDYZ3PQ","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:OZDYZ3PQCGSTYG2F3BQ4RVS2LN","target":"record","payload":{"canonical_record":{"source":{"id":"1804.07759","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-20T08:04:32Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"836a3518e9bbe1a2b4e4ad623f37dcdc30f327b4487cfb60c0111a82d15ddb84","abstract_canon_sha256":"54e5ca96b0fc736a5a3f7df1cf491b481bf63b9cc91220e67040302f032e2b98"},"schema_version":"1.0"},"canonical_sha256":"76478cedf011a53c1b45d861c8d65a5b487aa0f5e956760ddb652de762962b95","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:36.224354Z","signature_b64":"sNf4+wKd6Atv1OJeYZ+PaZr/wi/t/0K4z3TvQ6nkkdOlPvSwM9l5gFufSKKxS0imBBstyjWjJLCMdUrN0D22BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"76478cedf011a53c1b45d861c8d65a5b487aa0f5e956760ddb652de762962b95","last_reissued_at":"2026-05-18T00:16:36.223733Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:36.223733Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1804.07759","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:16:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KAuHQsbTZAJKjB+qEQaeQrr9Qh39GkbAQDVMzoomQkh1YjeFU1Ovt6x183IGsGyLd9CW9IY/44sBcbuetppJBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T00:42:06.693765Z"},"content_sha256":"9fe7dd9c6fecd1f0bfe9ed57902233513454142583025395b68e08a6ced5df02","schema_version":"1.0","event_id":"sha256:9fe7dd9c6fecd1f0bfe9ed57902233513454142583025395b68e08a6ced5df02"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:OZDYZ3PQCGSTYG2F3BQ4RVS2LN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Self-paced Regularization Framework for Partial-Label Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Congyang Lang, Gengyu Lyu, Songhe Feng","submitted_at":"2018-04-20T08:04:32Z","abstract_excerpt":"Partial label learning (PLL) aims to solve the problem where each training instance is associated with a set of candidate labels, one of which is the correct label. Most PLL algorithms try to disambiguate the candidate label set, by either simply treating each candidate label equally or iteratively identifying the true label. Nonetheless, existing algorithms usually treat all labels and instances equally, and the complexities of both labels and instances are not taken into consideration during the learning stage. Inspired by the successful application of self-paced learning strategy in machine"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.07759","kind":"arxiv","version":2},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:16:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zXIiMnaKVcWIskG6SZWhOAEg7b0C2NLSZs7bAwo7+RelF0A5xdIPMpHWp8kTFBisPxXl6ALIOveeSUyo8Lk0Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T00:42:06.694422Z"},"content_sha256":"667fbcab2f703597c9e7d2933e74ef48da1820958ee2e0aa070b9f8a19cdff3f","schema_version":"1.0","event_id":"sha256:667fbcab2f703597c9e7d2933e74ef48da1820958ee2e0aa070b9f8a19cdff3f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OZDYZ3PQCGSTYG2F3BQ4RVS2LN/bundle.json","state_url":"https://pith.science/pith/OZDYZ3PQCGSTYG2F3BQ4RVS2LN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OZDYZ3PQCGSTYG2F3BQ4RVS2LN/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-11T00:42:06Z","links":{"resolver":"https://pith.science/pith/OZDYZ3PQCGSTYG2F3BQ4RVS2LN","bundle":"https://pith.science/pith/OZDYZ3PQCGSTYG2F3BQ4RVS2LN/bundle.json","state":"https://pith.science/pith/OZDYZ3PQCGSTYG2F3BQ4RVS2LN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OZDYZ3PQCGSTYG2F3BQ4RVS2LN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:OZDYZ3PQCGSTYG2F3BQ4RVS2LN","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":"54e5ca96b0fc736a5a3f7df1cf491b481bf63b9cc91220e67040302f032e2b98","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-20T08:04:32Z","title_canon_sha256":"836a3518e9bbe1a2b4e4ad623f37dcdc30f327b4487cfb60c0111a82d15ddb84"},"schema_version":"1.0","source":{"id":"1804.07759","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.07759","created_at":"2026-05-18T00:16:36Z"},{"alias_kind":"arxiv_version","alias_value":"1804.07759v2","created_at":"2026-05-18T00:16:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.07759","created_at":"2026-05-18T00:16:36Z"},{"alias_kind":"pith_short_12","alias_value":"OZDYZ3PQCGST","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OZDYZ3PQCGSTYG2F","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OZDYZ3PQ","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:667fbcab2f703597c9e7d2933e74ef48da1820958ee2e0aa070b9f8a19cdff3f","target":"graph","created_at":"2026-05-18T00:16:36Z","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"},"paper":{"abstract_excerpt":"Partial label learning (PLL) aims to solve the problem where each training instance is associated with a set of candidate labels, one of which is the correct label. Most PLL algorithms try to disambiguate the candidate label set, by either simply treating each candidate label equally or iteratively identifying the true label. Nonetheless, existing algorithms usually treat all labels and instances equally, and the complexities of both labels and instances are not taken into consideration during the learning stage. Inspired by the successful application of self-paced learning strategy in machine","authors_text":"Congyang Lang, Gengyu Lyu, Songhe Feng","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-20T08:04:32Z","title":"A Self-paced Regularization Framework for Partial-Label Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.07759","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":"sha256:9fe7dd9c6fecd1f0bfe9ed57902233513454142583025395b68e08a6ced5df02","target":"record","created_at":"2026-05-18T00:16:36Z","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":"54e5ca96b0fc736a5a3f7df1cf491b481bf63b9cc91220e67040302f032e2b98","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-20T08:04:32Z","title_canon_sha256":"836a3518e9bbe1a2b4e4ad623f37dcdc30f327b4487cfb60c0111a82d15ddb84"},"schema_version":"1.0","source":{"id":"1804.07759","kind":"arxiv","version":2}},"canonical_sha256":"76478cedf011a53c1b45d861c8d65a5b487aa0f5e956760ddb652de762962b95","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"76478cedf011a53c1b45d861c8d65a5b487aa0f5e956760ddb652de762962b95","first_computed_at":"2026-05-18T00:16:36.223733Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:16:36.223733Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sNf4+wKd6Atv1OJeYZ+PaZr/wi/t/0K4z3TvQ6nkkdOlPvSwM9l5gFufSKKxS0imBBstyjWjJLCMdUrN0D22BA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:16:36.224354Z","signed_message":"canonical_sha256_bytes"},"source_id":"1804.07759","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9fe7dd9c6fecd1f0bfe9ed57902233513454142583025395b68e08a6ced5df02","sha256:667fbcab2f703597c9e7d2933e74ef48da1820958ee2e0aa070b9f8a19cdff3f"],"state_sha256":"fd153d74b9c47628f8739d340c376441e5085108a9a3fe7873fe6bd28729c0bf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mh5WIaGj+BNQsoDH1XnORkiwKVxeERaIW2sMhyspyyXqT2NocfGr0NE6AikIXrtv3wYN4nngdi2jh8pNFyQQBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T00:42:06.697959Z","bundle_sha256":"fa7f97e0bf456fa202aa3ddc5beec06b69499bfdbec4ed01f53650d49141f4d9"}}