{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:FJ6HTWE35TAXLLO2MTJJHL6ZEQ","short_pith_number":"pith:FJ6HTWE3","schema_version":"1.0","canonical_sha256":"2a7c79d89becc175adda64d293afd9243eb5a7cc28937cd41c55e1514e5745f0","source":{"kind":"arxiv","id":"2111.07722","version":4},"attestation_state":"computed","paper":{"title":"Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"C.L.Philip Chen, Dongbin Zhao, Nannan Li, Yaran Chen, Zixiang Ding","submitted_at":"2021-11-15T12:49:27Z","abstract_excerpt":"Different from other deep scalable architecture-based NAS approaches, Broad Neural Architecture Search (BNAS) proposes a broad scalable architecture which consists of convolution and enhancement blocks, dubbed Broad Convolutional Neural Network (BCNN), as the search space for amazing efficiency improvement. BCNN reuses the topologies of cells in the convolution block so that BNAS can employ few cells for efficient search. Moreover, multi-scale feature fusion and knowledge embedding are proposed to improve the performance of BCNN with shallow topology. However, BNAS suffers some drawbacks: 1) i"},"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":"2111.07722","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-11-15T12:49:27Z","cross_cats_sorted":[],"title_canon_sha256":"75fd0078215a5fedce8beaff62357ebf743079c9cd54e9cc43a3b84636b7ccab","abstract_canon_sha256":"66f61b138c79197a4c152f3d6888b26411c15d258df414250f87c0081453eaf5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:44:59.561902Z","signature_b64":"/qj0LLzDPph7tOr1vwIhw/11qfAaS+2rV1PWclQoWcIqVp3KfNsS+1VvKd0gDbs5LKvUeoNB0HcawwXidqzvCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2a7c79d89becc175adda64d293afd9243eb5a7cc28937cd41c55e1514e5745f0","last_reissued_at":"2026-07-05T04:44:59.561406Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:44:59.561406Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"C.L.Philip Chen, Dongbin Zhao, Nannan Li, Yaran Chen, Zixiang Ding","submitted_at":"2021-11-15T12:49:27Z","abstract_excerpt":"Different from other deep scalable architecture-based NAS approaches, Broad Neural Architecture Search (BNAS) proposes a broad scalable architecture which consists of convolution and enhancement blocks, dubbed Broad Convolutional Neural Network (BCNN), as the search space for amazing efficiency improvement. BCNN reuses the topologies of cells in the convolution block so that BNAS can employ few cells for efficient search. Moreover, multi-scale feature fusion and knowledge embedding are proposed to improve the performance of BCNN with shallow topology. However, BNAS suffers some drawbacks: 1) i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.07722","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2111.07722/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":"2111.07722","created_at":"2026-07-05T04:44:59.561460+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.07722v4","created_at":"2026-07-05T04:44:59.561460+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.07722","created_at":"2026-07-05T04:44:59.561460+00:00"},{"alias_kind":"pith_short_12","alias_value":"FJ6HTWE35TAX","created_at":"2026-07-05T04:44:59.561460+00:00"},{"alias_kind":"pith_short_16","alias_value":"FJ6HTWE35TAXLLO2","created_at":"2026-07-05T04:44:59.561460+00:00"},{"alias_kind":"pith_short_8","alias_value":"FJ6HTWE3","created_at":"2026-07-05T04:44:59.561460+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/FJ6HTWE35TAXLLO2MTJJHL6ZEQ","json":"https://pith.science/pith/FJ6HTWE35TAXLLO2MTJJHL6ZEQ.json","graph_json":"https://pith.science/api/pith-number/FJ6HTWE35TAXLLO2MTJJHL6ZEQ/graph.json","events_json":"https://pith.science/api/pith-number/FJ6HTWE35TAXLLO2MTJJHL6ZEQ/events.json","paper":"https://pith.science/paper/FJ6HTWE3"},"agent_actions":{"view_html":"https://pith.science/pith/FJ6HTWE35TAXLLO2MTJJHL6ZEQ","download_json":"https://pith.science/pith/FJ6HTWE35TAXLLO2MTJJHL6ZEQ.json","view_paper":"https://pith.science/paper/FJ6HTWE3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.07722&json=true","fetch_graph":"https://pith.science/api/pith-number/FJ6HTWE35TAXLLO2MTJJHL6ZEQ/graph.json","fetch_events":"https://pith.science/api/pith-number/FJ6HTWE35TAXLLO2MTJJHL6ZEQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FJ6HTWE35TAXLLO2MTJJHL6ZEQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FJ6HTWE35TAXLLO2MTJJHL6ZEQ/action/storage_attestation","attest_author":"https://pith.science/pith/FJ6HTWE35TAXLLO2MTJJHL6ZEQ/action/author_attestation","sign_citation":"https://pith.science/pith/FJ6HTWE35TAXLLO2MTJJHL6ZEQ/action/citation_signature","submit_replication":"https://pith.science/pith/FJ6HTWE35TAXLLO2MTJJHL6ZEQ/action/replication_record"}},"created_at":"2026-07-05T04:44:59.561460+00:00","updated_at":"2026-07-05T04:44:59.561460+00:00"}