{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:F7ITQ424BQQDB5LSO4JXL7W4EJ","short_pith_number":"pith:F7ITQ424","canonical_record":{"source":{"id":"1908.09791","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-08-26T16:46:23Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"323f5650fec8c210284e942e38ac2dc00d038ed71d76f6fb53a53314e44c2648","abstract_canon_sha256":"659043f764245514f43e1c3c57cc018b37c61dac6bb5a828702cb26db82c65cb"},"schema_version":"1.0"},"canonical_sha256":"2fd138735c0c2030f572771375fedc2261b28059697e110fe18b8776cc9c7750","source":{"kind":"arxiv","id":"1908.09791","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"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:F7ITQ424BQQDB5LSO4JXL7W4EJ","target":"record","payload":{"canonical_record":{"source":{"id":"1908.09791","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2019-08-26T16:46:23Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"323f5650fec8c210284e942e38ac2dc00d038ed71d76f6fb53a53314e44c2648","abstract_canon_sha256":"659043f764245514f43e1c3c57cc018b37c61dac6bb5a828702cb26db82c65cb"},"schema_version":"1.0"},"canonical_sha256":"2fd138735c0c2030f572771375fedc2261b28059697e110fe18b8776cc9c7750","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:59:22.429434Z","signature_b64":"prfJ0vZZBlXo6sDc89pJTbBfmsWeWp5MPtS/oLV2SbSOz0vfpoE6reWcjv4FljGTgT6/tilKUllYqn9rLmvGAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2fd138735c0c2030f572771375fedc2261b28059697e110fe18b8776cc9c7750","last_reissued_at":"2026-07-05T00:59:22.428999Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:59:22.428999Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.09791","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:59:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gcWtJTyHYb53Svx4DZpdZ6unOZGPboEQCfqNFU5TCcMVmjGREl3+NRryZuI+34iKc3g2i9bSe0BInHcpu0qEBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-21T04:12:41.841123Z"},"content_sha256":"aac61ac39cb9c8c58635b33881e954bad5a8e3f9a7939fbded2ef398c0ab9bc8","schema_version":"1.0","event_id":"sha256:aac61ac39cb9c8c58635b33881e954bad5a8e3f9a7939fbded2ef398c0ab9bc8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:F7ITQ424BQQDB5LSO4JXL7W4EJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Once-for-All: Train One Network and Specialize it for Efficient Deployment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chuang Gan, Han Cai, Song Han, Tianzhe Wang, Zhekai Zhang","submitted_at":"2019-08-26T16:46:23Z","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"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.09791","kind":"arxiv","version":5},"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/1908.09791/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:59:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g6xS6QiALhZQ9/wbXQn5/ygc+T/K0K5v3/CMz8jTQ2R1jvo2vCo4DDz5aoxnRZpj3GvrPIcz668jZ3CeXW/WDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-21T04:12:41.841498Z"},"content_sha256":"7d1b912da455b270e63a98b8c0d48308dfa91bd63abf7b452c70b1de8b2d2bbb","schema_version":"1.0","event_id":"sha256:7d1b912da455b270e63a98b8c0d48308dfa91bd63abf7b452c70b1de8b2d2bbb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F7ITQ424BQQDB5LSO4JXL7W4EJ/bundle.json","state_url":"https://pith.science/pith/F7ITQ424BQQDB5LSO4JXL7W4EJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F7ITQ424BQQDB5LSO4JXL7W4EJ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-21T04:12:41Z","links":{"resolver":"https://pith.science/pith/F7ITQ424BQQDB5LSO4JXL7W4EJ","bundle":"https://pith.science/pith/F7ITQ424BQQDB5LSO4JXL7W4EJ/bundle.json","state":"https://pith.science/pith/F7ITQ424BQQDB5LSO4JXL7W4EJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F7ITQ424BQQDB5LSO4JXL7W4EJ/bundle.json"},"state":{"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"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I7zxi2rSkHxt+f3KMbfUPubWQvVAPME/jKkFBkHQtuIRd14oWCELgVxu2wXqyAqCH2uil8wivS679eLnUMPdCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-21T04:12:41.843901Z","bundle_sha256":"29bb18c34aab7ae25d04e38535f46981a07d817f53f4dd4fae576518a6b7824b"}}