{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:3GGVW2RLGEFPZ4G2U4REAQZIYI","short_pith_number":"pith:3GGVW2RL","schema_version":"1.0","canonical_sha256":"d98d5b6a2b310afcf0daa722404328c21ed40f15d3209534be62da0290cb3bd6","source":{"kind":"arxiv","id":"1809.04184","version":1},"attestation_state":"computed","paper":{"title":"Searching for Efficient Multi-Scale Architectures for Dense Image Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Barret Zoph, Florian Schroff, George Papandreou, Hartwig Adam, Jonathon Shlens, Liang-Chieh Chen, Maxwell D. Collins, Yukun Zhu","submitted_at":"2018-09-11T22:36:01Z","abstract_excerpt":"The design of neural network architectures is an important component for achieving state-of-the-art performance with machine learning systems across a broad array of tasks. Much work has endeavored to design and build architectures automatically through clever construction of a search space paired with simple learning algorithms. Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks. An open question is the degree to which such methods may generalize to new domains. In this work we explore the constructio"},"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":"1809.04184","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-11T22:36:01Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"f7e483a4116fa349c8b24f898af9945583b12b152c45a247810ef84246725423","abstract_canon_sha256":"deb8c9311f0761b0e734c3390ae6dc8a3bbfc8ab95d915c83dd3d05239a7bfef"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:55.252286Z","signature_b64":"PMNOwjJIkzYrmJ4hNcLSa6kUYkwdLG5a/VSj/WTHvLEFqKob6rloyidlmRljaPRGFx9UHXLGdpL9bmDOJmupDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d98d5b6a2b310afcf0daa722404328c21ed40f15d3209534be62da0290cb3bd6","last_reissued_at":"2026-05-18T00:05:55.251559Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:55.251559Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Searching for Efficient Multi-Scale Architectures for Dense Image Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Barret Zoph, Florian Schroff, George Papandreou, Hartwig Adam, Jonathon Shlens, Liang-Chieh Chen, Maxwell D. Collins, Yukun Zhu","submitted_at":"2018-09-11T22:36:01Z","abstract_excerpt":"The design of neural network architectures is an important component for achieving state-of-the-art performance with machine learning systems across a broad array of tasks. Much work has endeavored to design and build architectures automatically through clever construction of a search space paired with simple learning algorithms. Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks. An open question is the degree to which such methods may generalize to new domains. In this work we explore the constructio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.04184","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":"1809.04184","created_at":"2026-05-18T00:05:55.251669+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.04184v1","created_at":"2026-05-18T00:05:55.251669+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.04184","created_at":"2026-05-18T00:05:55.251669+00:00"},{"alias_kind":"pith_short_12","alias_value":"3GGVW2RLGEFP","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"3GGVW2RLGEFPZ4G2","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"3GGVW2RL","created_at":"2026-05-18T12:32:02.567920+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/3GGVW2RLGEFPZ4G2U4REAQZIYI","json":"https://pith.science/pith/3GGVW2RLGEFPZ4G2U4REAQZIYI.json","graph_json":"https://pith.science/api/pith-number/3GGVW2RLGEFPZ4G2U4REAQZIYI/graph.json","events_json":"https://pith.science/api/pith-number/3GGVW2RLGEFPZ4G2U4REAQZIYI/events.json","paper":"https://pith.science/paper/3GGVW2RL"},"agent_actions":{"view_html":"https://pith.science/pith/3GGVW2RLGEFPZ4G2U4REAQZIYI","download_json":"https://pith.science/pith/3GGVW2RLGEFPZ4G2U4REAQZIYI.json","view_paper":"https://pith.science/paper/3GGVW2RL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.04184&json=true","fetch_graph":"https://pith.science/api/pith-number/3GGVW2RLGEFPZ4G2U4REAQZIYI/graph.json","fetch_events":"https://pith.science/api/pith-number/3GGVW2RLGEFPZ4G2U4REAQZIYI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3GGVW2RLGEFPZ4G2U4REAQZIYI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3GGVW2RLGEFPZ4G2U4REAQZIYI/action/storage_attestation","attest_author":"https://pith.science/pith/3GGVW2RLGEFPZ4G2U4REAQZIYI/action/author_attestation","sign_citation":"https://pith.science/pith/3GGVW2RLGEFPZ4G2U4REAQZIYI/action/citation_signature","submit_replication":"https://pith.science/pith/3GGVW2RLGEFPZ4G2U4REAQZIYI/action/replication_record"}},"created_at":"2026-05-18T00:05:55.251669+00:00","updated_at":"2026-05-18T00:05:55.251669+00:00"}