{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PHJ6BIG6YWJWHQLALEMYJRRCCJ","short_pith_number":"pith:PHJ6BIG6","schema_version":"1.0","canonical_sha256":"79d3e0a0dec59363c160591984c62212540b63e4d9022772ca68a000ce8bad0a","source":{"kind":"arxiv","id":"1903.05153","version":1},"attestation_state":"computed","paper":{"title":"A Sequential Set Generation Method for Predicting Set-Valued Outputs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jie Chen, Michael Witbrock, Tian Gao, Vijil Chenthamarakshan","submitted_at":"2019-03-12T19:06:18Z","abstract_excerpt":"Consider a general machine learning setting where the output is a set of labels or sequences. This output set is unordered and its size varies with the input. Whereas multi-label classification methods seem a natural first resort, they are not readily applicable to set-valued outputs because of the growth rate of the output space; and because conventional sequence generation doesn't reflect sets' order-free nature. In this paper, we propose a unified framework--sequential set generation (SSG)--that can handle output sets of labels and sequences. SSG is a meta-algorithm that leverages any proba"},"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":"1903.05153","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-12T19:06:18Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"b830d2a3c8b5ba14bd119741087e5511eb1b2f67bf05c939b5bc292f6880e194","abstract_canon_sha256":"08c0845af794b0862125f70855e720ab1d31209d56285283f5902a0f22aa1c1d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:25.298553Z","signature_b64":"SgHih/nrmYK+luNyVGa2D4WR0tMha7Q4djQ877rpMeQe4xlQZKqzxVPv5fOVNylZXCyn0Kx5Iep3ZAoX+seGBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79d3e0a0dec59363c160591984c62212540b63e4d9022772ca68a000ce8bad0a","last_reissued_at":"2026-05-17T23:51:25.298024Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:25.298024Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Sequential Set Generation Method for Predicting Set-Valued Outputs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jie Chen, Michael Witbrock, Tian Gao, Vijil Chenthamarakshan","submitted_at":"2019-03-12T19:06:18Z","abstract_excerpt":"Consider a general machine learning setting where the output is a set of labels or sequences. This output set is unordered and its size varies with the input. Whereas multi-label classification methods seem a natural first resort, they are not readily applicable to set-valued outputs because of the growth rate of the output space; and because conventional sequence generation doesn't reflect sets' order-free nature. In this paper, we propose a unified framework--sequential set generation (SSG)--that can handle output sets of labels and sequences. SSG is a meta-algorithm that leverages any proba"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.05153","kind":"arxiv","version":1},"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":"1903.05153","created_at":"2026-05-17T23:51:25.298107+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.05153v1","created_at":"2026-05-17T23:51:25.298107+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.05153","created_at":"2026-05-17T23:51:25.298107+00:00"},{"alias_kind":"pith_short_12","alias_value":"PHJ6BIG6YWJW","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"PHJ6BIG6YWJWHQLA","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"PHJ6BIG6","created_at":"2026-05-18T12:33:24.271573+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/PHJ6BIG6YWJWHQLALEMYJRRCCJ","json":"https://pith.science/pith/PHJ6BIG6YWJWHQLALEMYJRRCCJ.json","graph_json":"https://pith.science/api/pith-number/PHJ6BIG6YWJWHQLALEMYJRRCCJ/graph.json","events_json":"https://pith.science/api/pith-number/PHJ6BIG6YWJWHQLALEMYJRRCCJ/events.json","paper":"https://pith.science/paper/PHJ6BIG6"},"agent_actions":{"view_html":"https://pith.science/pith/PHJ6BIG6YWJWHQLALEMYJRRCCJ","download_json":"https://pith.science/pith/PHJ6BIG6YWJWHQLALEMYJRRCCJ.json","view_paper":"https://pith.science/paper/PHJ6BIG6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.05153&json=true","fetch_graph":"https://pith.science/api/pith-number/PHJ6BIG6YWJWHQLALEMYJRRCCJ/graph.json","fetch_events":"https://pith.science/api/pith-number/PHJ6BIG6YWJWHQLALEMYJRRCCJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PHJ6BIG6YWJWHQLALEMYJRRCCJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PHJ6BIG6YWJWHQLALEMYJRRCCJ/action/storage_attestation","attest_author":"https://pith.science/pith/PHJ6BIG6YWJWHQLALEMYJRRCCJ/action/author_attestation","sign_citation":"https://pith.science/pith/PHJ6BIG6YWJWHQLALEMYJRRCCJ/action/citation_signature","submit_replication":"https://pith.science/pith/PHJ6BIG6YWJWHQLALEMYJRRCCJ/action/replication_record"}},"created_at":"2026-05-17T23:51:25.298107+00:00","updated_at":"2026-05-17T23:51:25.298107+00:00"}