{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:7ZP5FPVRASBLFIM5E66Z5M72RV","short_pith_number":"pith:7ZP5FPVR","schema_version":"1.0","canonical_sha256":"fe5fd2beb10482b2a19d27bd9eb3fa8d76bf609bb7161c33597003ce7a951367","source":{"kind":"arxiv","id":"2301.09231","version":1},"attestation_state":"computed","paper":{"title":"GP-NAS-ensemble: a model for NAS Performance Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Kunjin Chen, Kunlong Chen, Liu Yang, Lujun Li, Yidan Xu, Yitian Chen","submitted_at":"2023-01-23T00:17:52Z","abstract_excerpt":"It is of great significance to estimate the performance of a given model architecture without training in the application of Neural Architecture Search (NAS) as it may take a lot of time to evaluate the performance of an architecture. In this paper, a novel NAS framework called GP-NAS-ensemble is proposed to predict the performance of a neural network architecture with a small training dataset. We make several improvements on the GP-NAS model to make it share the advantage of ensemble learning methods. Our method ranks second in the CVPR2022 second lightweight NAS challenge performance predict"},"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":"2301.09231","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-23T00:17:52Z","cross_cats_sorted":["stat.AP","stat.ML"],"title_canon_sha256":"3709617470f5f292927b662741b52ce1c159a2cd5fd91c50b6b768f8de2cdb32","abstract_canon_sha256":"dd14d966ea533da0e23fe02b69c9c5daf46ac99f04f89001a004b11f80a5acc1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:34:58.481756Z","signature_b64":"BbQ1ldLbp1z0ZStU2jJ6Wv7tgNQmrtAGhryK1PWyRsRlOsGmI/+6u5A70tYxj4+mfMIozo56iwDDCimoTdXMBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe5fd2beb10482b2a19d27bd9eb3fa8d76bf609bb7161c33597003ce7a951367","last_reissued_at":"2026-07-05T05:34:58.481258Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:34:58.481258Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GP-NAS-ensemble: a model for NAS Performance Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Kunjin Chen, Kunlong Chen, Liu Yang, Lujun Li, Yidan Xu, Yitian Chen","submitted_at":"2023-01-23T00:17:52Z","abstract_excerpt":"It is of great significance to estimate the performance of a given model architecture without training in the application of Neural Architecture Search (NAS) as it may take a lot of time to evaluate the performance of an architecture. In this paper, a novel NAS framework called GP-NAS-ensemble is proposed to predict the performance of a neural network architecture with a small training dataset. We make several improvements on the GP-NAS model to make it share the advantage of ensemble learning methods. Our method ranks second in the CVPR2022 second lightweight NAS challenge performance predict"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.09231","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2301.09231/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2301.09231","created_at":"2026-07-05T05:34:58.481321+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.09231v1","created_at":"2026-07-05T05:34:58.481321+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.09231","created_at":"2026-07-05T05:34:58.481321+00:00"},{"alias_kind":"pith_short_12","alias_value":"7ZP5FPVRASBL","created_at":"2026-07-05T05:34:58.481321+00:00"},{"alias_kind":"pith_short_16","alias_value":"7ZP5FPVRASBLFIM5","created_at":"2026-07-05T05:34:58.481321+00:00"},{"alias_kind":"pith_short_8","alias_value":"7ZP5FPVR","created_at":"2026-07-05T05:34:58.481321+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/7ZP5FPVRASBLFIM5E66Z5M72RV","json":"https://pith.science/pith/7ZP5FPVRASBLFIM5E66Z5M72RV.json","graph_json":"https://pith.science/api/pith-number/7ZP5FPVRASBLFIM5E66Z5M72RV/graph.json","events_json":"https://pith.science/api/pith-number/7ZP5FPVRASBLFIM5E66Z5M72RV/events.json","paper":"https://pith.science/paper/7ZP5FPVR"},"agent_actions":{"view_html":"https://pith.science/pith/7ZP5FPVRASBLFIM5E66Z5M72RV","download_json":"https://pith.science/pith/7ZP5FPVRASBLFIM5E66Z5M72RV.json","view_paper":"https://pith.science/paper/7ZP5FPVR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.09231&json=true","fetch_graph":"https://pith.science/api/pith-number/7ZP5FPVRASBLFIM5E66Z5M72RV/graph.json","fetch_events":"https://pith.science/api/pith-number/7ZP5FPVRASBLFIM5E66Z5M72RV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7ZP5FPVRASBLFIM5E66Z5M72RV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7ZP5FPVRASBLFIM5E66Z5M72RV/action/storage_attestation","attest_author":"https://pith.science/pith/7ZP5FPVRASBLFIM5E66Z5M72RV/action/author_attestation","sign_citation":"https://pith.science/pith/7ZP5FPVRASBLFIM5E66Z5M72RV/action/citation_signature","submit_replication":"https://pith.science/pith/7ZP5FPVRASBLFIM5E66Z5M72RV/action/replication_record"}},"created_at":"2026-07-05T05:34:58.481321+00:00","updated_at":"2026-07-05T05:34:58.481321+00:00"}