{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:F7ITQ424BQQDB5LSO4JXL7W4EJ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"659043f764245514f43e1c3c57cc018b37c61dac6bb5a828702cb26db82c65cb","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-08-26T16:46:23Z","title_canon_sha256":"323f5650fec8c210284e942e38ac2dc00d038ed71d76f6fb53a53314e44c2648"},"schema_version":"1.0","source":{"id":"1908.09791","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.09791","created_at":"2026-07-05T00:59:22Z"},{"alias_kind":"arxiv_version","alias_value":"1908.09791v5","created_at":"2026-07-05T00:59:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.09791","created_at":"2026-07-05T00:59:22Z"},{"alias_kind":"pith_short_12","alias_value":"F7ITQ424BQQD","created_at":"2026-07-05T00:59:22Z"},{"alias_kind":"pith_short_16","alias_value":"F7ITQ424BQQDB5LS","created_at":"2026-07-05T00:59:22Z"},{"alias_kind":"pith_short_8","alias_value":"F7ITQ424","created_at":"2026-07-05T00:59:22Z"}],"graph_snapshots":[{"event_id":"sha256:7d1b912da455b270e63a98b8c0d48308dfa91bd63abf7b452c70b1de8b2d2bbb","target":"graph","created_at":"2026-07-05T00:59:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1908.09791/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We address the challenging problem of efficient inference across many devices and resource constraints, especially on edge devices. Conventional approaches either manually design or use neural architecture search (NAS) to find a specialized neural network and train it from scratch for each case, which is computationally prohibitive (causing $CO_2$ emission as much as 5 cars' lifetime) thus unscalable. In this work, we propose to train a once-for-all (OFA) network that supports diverse architectural settings by decoupling training and search, to reduce the cost. We can quickly get a specialized","authors_text":"Chuang Gan, Han Cai, Song Han, Tianzhe Wang, Zhekai Zhang","cross_cats":["cs.CV","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-08-26T16:46:23Z","title":"Once-for-All: Train One Network and Specialize it for Efficient Deployment"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.09791","kind":"arxiv","version":5},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:aac61ac39cb9c8c58635b33881e954bad5a8e3f9a7939fbded2ef398c0ab9bc8","target":"record","created_at":"2026-07-05T00:59:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"659043f764245514f43e1c3c57cc018b37c61dac6bb5a828702cb26db82c65cb","cross_cats_sorted":["cs.CV","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-08-26T16:46:23Z","title_canon_sha256":"323f5650fec8c210284e942e38ac2dc00d038ed71d76f6fb53a53314e44c2648"},"schema_version":"1.0","source":{"id":"1908.09791","kind":"arxiv","version":5}},"canonical_sha256":"2fd138735c0c2030f572771375fedc2261b28059697e110fe18b8776cc9c7750","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2fd138735c0c2030f572771375fedc2261b28059697e110fe18b8776cc9c7750","first_computed_at":"2026-07-05T00:59:22.428999Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:59:22.428999Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"prfJ0vZZBlXo6sDc89pJTbBfmsWeWp5MPtS/oLV2SbSOz0vfpoE6reWcjv4FljGTgT6/tilKUllYqn9rLmvGAw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:59:22.429434Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.09791","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:aac61ac39cb9c8c58635b33881e954bad5a8e3f9a7939fbded2ef398c0ab9bc8","sha256:7d1b912da455b270e63a98b8c0d48308dfa91bd63abf7b452c70b1de8b2d2bbb"],"state_sha256":"0c4b7f1585d734801b1253c6e4d9f564da3c4929c8d5e781c6c96f882686654f"}