{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:QPMUFRCLBUDWM67CRROSONTPOI","short_pith_number":"pith:QPMUFRCL","schema_version":"1.0","canonical_sha256":"83d942c44b0d07667be28c5d27366f7220786f393ddf94832afe35ffa4d74c1e","source":{"kind":"arxiv","id":"2310.11478","version":1},"attestation_state":"computed","paper":{"title":"ASP: Automatic Selection of Proxy dataset for efficient AutoML","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Bin Chen, Chao Liao, Chengru Song, Di Zhang, Jianchao Tan, Jiyuan Jia, Peng Yao","submitted_at":"2023-10-17T09:36:22Z","abstract_excerpt":"Deep neural networks have gained great success due to the increasing amounts of data, and diverse effective neural network designs. However, it also brings a heavy computing burden as the amount of training data is proportional to the training time. In addition, a well-behaved model requires repeated trials of different structure designs and hyper-parameters, which may take a large amount of time even with state-of-the-art (SOTA) hyper-parameter optimization (HPO) algorithms and neural architecture search (NAS) algorithms. In this paper, we propose an Automatic Selection of Proxy dataset frame"},"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":"2310.11478","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-17T09:36:22Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"756d29d2944738c21cf3f7edc3d03a78963940cfd1d851737a94c1e84c273b4a","abstract_canon_sha256":"bb7e5d841764aea40d1def2b9d21c313cc0d76d585fa020d32a0b3ba871e6aad"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:02:07.711736Z","signature_b64":"c/yEt/qFTyHwFCsk5N1GYR1GwxreW4E0VMiLpk/HWjwa2U32SM8ocVpIwciiBFLpC1ne6MdiCIbmiCiUvx3kCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"83d942c44b0d07667be28c5d27366f7220786f393ddf94832afe35ffa4d74c1e","last_reissued_at":"2026-07-05T07:02:07.711247Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:02:07.711247Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ASP: Automatic Selection of Proxy dataset for efficient AutoML","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Bin Chen, Chao Liao, Chengru Song, Di Zhang, Jianchao Tan, Jiyuan Jia, Peng Yao","submitted_at":"2023-10-17T09:36:22Z","abstract_excerpt":"Deep neural networks have gained great success due to the increasing amounts of data, and diverse effective neural network designs. However, it also brings a heavy computing burden as the amount of training data is proportional to the training time. In addition, a well-behaved model requires repeated trials of different structure designs and hyper-parameters, which may take a large amount of time even with state-of-the-art (SOTA) hyper-parameter optimization (HPO) algorithms and neural architecture search (NAS) algorithms. In this paper, we propose an Automatic Selection of Proxy dataset frame"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.11478","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/2310.11478/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":"2310.11478","created_at":"2026-07-05T07:02:07.711302+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.11478v1","created_at":"2026-07-05T07:02:07.711302+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.11478","created_at":"2026-07-05T07:02:07.711302+00:00"},{"alias_kind":"pith_short_12","alias_value":"QPMUFRCLBUDW","created_at":"2026-07-05T07:02:07.711302+00:00"},{"alias_kind":"pith_short_16","alias_value":"QPMUFRCLBUDWM67C","created_at":"2026-07-05T07:02:07.711302+00:00"},{"alias_kind":"pith_short_8","alias_value":"QPMUFRCL","created_at":"2026-07-05T07:02:07.711302+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/QPMUFRCLBUDWM67CRROSONTPOI","json":"https://pith.science/pith/QPMUFRCLBUDWM67CRROSONTPOI.json","graph_json":"https://pith.science/api/pith-number/QPMUFRCLBUDWM67CRROSONTPOI/graph.json","events_json":"https://pith.science/api/pith-number/QPMUFRCLBUDWM67CRROSONTPOI/events.json","paper":"https://pith.science/paper/QPMUFRCL"},"agent_actions":{"view_html":"https://pith.science/pith/QPMUFRCLBUDWM67CRROSONTPOI","download_json":"https://pith.science/pith/QPMUFRCLBUDWM67CRROSONTPOI.json","view_paper":"https://pith.science/paper/QPMUFRCL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.11478&json=true","fetch_graph":"https://pith.science/api/pith-number/QPMUFRCLBUDWM67CRROSONTPOI/graph.json","fetch_events":"https://pith.science/api/pith-number/QPMUFRCLBUDWM67CRROSONTPOI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QPMUFRCLBUDWM67CRROSONTPOI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QPMUFRCLBUDWM67CRROSONTPOI/action/storage_attestation","attest_author":"https://pith.science/pith/QPMUFRCLBUDWM67CRROSONTPOI/action/author_attestation","sign_citation":"https://pith.science/pith/QPMUFRCLBUDWM67CRROSONTPOI/action/citation_signature","submit_replication":"https://pith.science/pith/QPMUFRCLBUDWM67CRROSONTPOI/action/replication_record"}},"created_at":"2026-07-05T07:02:07.711302+00:00","updated_at":"2026-07-05T07:02:07.711302+00:00"}