{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:4O3NJHGIZN4W66UGPUS2HGEOOB","short_pith_number":"pith:4O3NJHGI","canonical_record":{"source":{"id":"2211.14227","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-11-25T16:40:49Z","cross_cats_sorted":["cs.DS","stat.ML"],"title_canon_sha256":"d0512feb1db40b8852d75dac1cae47ca81e4234022adf4f7c1d043cc45063ba3","abstract_canon_sha256":"e52d8806a7e2d1260e3a172b3f778e53e79e5998b09700baafd2c6463234a519"},"schema_version":"1.0"},"canonical_sha256":"e3b6d49cc8cb796f7a867d25a3988e706bb6624d4fe8082ccdba22cab94555c8","source":{"kind":"arxiv","id":"2211.14227","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.14227","created_at":"2026-07-05T05:19:30Z"},{"alias_kind":"arxiv_version","alias_value":"2211.14227v1","created_at":"2026-07-05T05:19:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.14227","created_at":"2026-07-05T05:19:30Z"},{"alias_kind":"pith_short_12","alias_value":"4O3NJHGIZN4W","created_at":"2026-07-05T05:19:30Z"},{"alias_kind":"pith_short_16","alias_value":"4O3NJHGIZN4W66UG","created_at":"2026-07-05T05:19:30Z"},{"alias_kind":"pith_short_8","alias_value":"4O3NJHGI","created_at":"2026-07-05T05:19:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:4O3NJHGIZN4W66UGPUS2HGEOOB","target":"record","payload":{"canonical_record":{"source":{"id":"2211.14227","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-11-25T16:40:49Z","cross_cats_sorted":["cs.DS","stat.ML"],"title_canon_sha256":"d0512feb1db40b8852d75dac1cae47ca81e4234022adf4f7c1d043cc45063ba3","abstract_canon_sha256":"e52d8806a7e2d1260e3a172b3f778e53e79e5998b09700baafd2c6463234a519"},"schema_version":"1.0"},"canonical_sha256":"e3b6d49cc8cb796f7a867d25a3988e706bb6624d4fe8082ccdba22cab94555c8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:19:30.922084Z","signature_b64":"CZXISvKn1Kem+BoOvPiOOuFLxnB/ef3QeVYMUPtmAYgcUdsFNBsh6nCK6z4JEXm9vjFeML9xk3x2Y9GKaBy8CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e3b6d49cc8cb796f7a867d25a3988e706bb6624d4fe8082ccdba22cab94555c8","last_reissued_at":"2026-07-05T05:19:30.921606Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:19:30.921606Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2211.14227","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:19:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gdQqJm2KD2eBjrzKqO6pe8m0XiyYJIWKcUFK58qLp7QHII5uUmY+JvcpV9j3unitxQpRI2jOJQ7umLq3BdRFDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T09:38:52.335853Z"},"content_sha256":"f1363824412aa2d70d274d799645281703eade9e2043b1afce2635e3bf26dacb","schema_version":"1.0","event_id":"sha256:f1363824412aa2d70d274d799645281703eade9e2043b1afce2635e3bf26dacb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:4O3NJHGIZN4W66UGPUS2HGEOOB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bypass Exponential Time Preprocessing: Fast Neural Network Training via Weight-Data Correlation Preprocessing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DS","stat.ML"],"primary_cat":"cs.LG","authors_text":"Danyang Zhuo, Jiehao Liang, Josh Alman, Ruizhe Zhang, Zhao Song","submitted_at":"2022-11-25T16:40:49Z","abstract_excerpt":"Over the last decade, deep neural networks have transformed our society, and they are already widely applied in various machine learning applications. State-of-art deep neural networks are becoming larger in size every year to deliver increasing model accuracy, and as a result, model training consumes substantial computing resources and will only consume more in the future. Using current training methods, in each iteration, to process a data point $x \\in \\mathbb{R}^d$ in a layer, we need to spend $\\Theta(md)$ time to evaluate all the $m$ neurons in the layer. This means processing the entire l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.14227","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/2211.14227/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":"2026-07-05T05:19:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YkghkcsQnqJFj584o4ol2Fve0veHzkHHXqgGGyWLYD8BSC8ItduBPNRwA8I+T46wqpiI4EGxmFlWvZf3YdWiDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T09:38:52.336353Z"},"content_sha256":"ef3983dc0602c17dd5cf7c7cc22e6bce5618c70e982d52ae563b7ca542ed15d9","schema_version":"1.0","event_id":"sha256:ef3983dc0602c17dd5cf7c7cc22e6bce5618c70e982d52ae563b7ca542ed15d9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4O3NJHGIZN4W66UGPUS2HGEOOB/bundle.json","state_url":"https://pith.science/pith/4O3NJHGIZN4W66UGPUS2HGEOOB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4O3NJHGIZN4W66UGPUS2HGEOOB/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":"2026-08-08T09:38:52Z","links":{"resolver":"https://pith.science/pith/4O3NJHGIZN4W66UGPUS2HGEOOB","bundle":"https://pith.science/pith/4O3NJHGIZN4W66UGPUS2HGEOOB/bundle.json","state":"https://pith.science/pith/4O3NJHGIZN4W66UGPUS2HGEOOB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4O3NJHGIZN4W66UGPUS2HGEOOB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:4O3NJHGIZN4W66UGPUS2HGEOOB","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":"e52d8806a7e2d1260e3a172b3f778e53e79e5998b09700baafd2c6463234a519","cross_cats_sorted":["cs.DS","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-11-25T16:40:49Z","title_canon_sha256":"d0512feb1db40b8852d75dac1cae47ca81e4234022adf4f7c1d043cc45063ba3"},"schema_version":"1.0","source":{"id":"2211.14227","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.14227","created_at":"2026-07-05T05:19:30Z"},{"alias_kind":"arxiv_version","alias_value":"2211.14227v1","created_at":"2026-07-05T05:19:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.14227","created_at":"2026-07-05T05:19:30Z"},{"alias_kind":"pith_short_12","alias_value":"4O3NJHGIZN4W","created_at":"2026-07-05T05:19:30Z"},{"alias_kind":"pith_short_16","alias_value":"4O3NJHGIZN4W66UG","created_at":"2026-07-05T05:19:30Z"},{"alias_kind":"pith_short_8","alias_value":"4O3NJHGI","created_at":"2026-07-05T05:19:30Z"}],"graph_snapshots":[{"event_id":"sha256:ef3983dc0602c17dd5cf7c7cc22e6bce5618c70e982d52ae563b7ca542ed15d9","target":"graph","created_at":"2026-07-05T05:19:30Z","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/2211.14227/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Over the last decade, deep neural networks have transformed our society, and they are already widely applied in various machine learning applications. State-of-art deep neural networks are becoming larger in size every year to deliver increasing model accuracy, and as a result, model training consumes substantial computing resources and will only consume more in the future. Using current training methods, in each iteration, to process a data point $x \\in \\mathbb{R}^d$ in a layer, we need to spend $\\Theta(md)$ time to evaluate all the $m$ neurons in the layer. This means processing the entire l","authors_text":"Danyang Zhuo, Jiehao Liang, Josh Alman, Ruizhe Zhang, Zhao Song","cross_cats":["cs.DS","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-11-25T16:40:49Z","title":"Bypass Exponential Time Preprocessing: Fast Neural Network Training via Weight-Data Correlation Preprocessing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.14227","kind":"arxiv","version":1},"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:f1363824412aa2d70d274d799645281703eade9e2043b1afce2635e3bf26dacb","target":"record","created_at":"2026-07-05T05:19:30Z","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":"e52d8806a7e2d1260e3a172b3f778e53e79e5998b09700baafd2c6463234a519","cross_cats_sorted":["cs.DS","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-11-25T16:40:49Z","title_canon_sha256":"d0512feb1db40b8852d75dac1cae47ca81e4234022adf4f7c1d043cc45063ba3"},"schema_version":"1.0","source":{"id":"2211.14227","kind":"arxiv","version":1}},"canonical_sha256":"e3b6d49cc8cb796f7a867d25a3988e706bb6624d4fe8082ccdba22cab94555c8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e3b6d49cc8cb796f7a867d25a3988e706bb6624d4fe8082ccdba22cab94555c8","first_computed_at":"2026-07-05T05:19:30.921606Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:19:30.921606Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CZXISvKn1Kem+BoOvPiOOuFLxnB/ef3QeVYMUPtmAYgcUdsFNBsh6nCK6z4JEXm9vjFeML9xk3x2Y9GKaBy8CA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:19:30.922084Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.14227","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f1363824412aa2d70d274d799645281703eade9e2043b1afce2635e3bf26dacb","sha256:ef3983dc0602c17dd5cf7c7cc22e6bce5618c70e982d52ae563b7ca542ed15d9"],"state_sha256":"d769ecfdf2c26b3345371833f5cf9fccb7a8b3bb57f2392fa7f9e81194d4b162"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KfWEpZpszZjfslvrU846A3NeU4OBpj/9/Tffr5rzRFfng0UEMXpkJnyM9U0vAvoTrrPLd43vsHG1B5ncN8iQAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T09:38:52.340043Z","bundle_sha256":"faeaf5031d9b367a341cdab1b207cf3434fc701e49dce1a40a229c561f4fbe46"}}