{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:7Q6VEUD3IAPI3IDMP32MOYBFHH","short_pith_number":"pith:7Q6VEUD3","schema_version":"1.0","canonical_sha256":"fc3d52507b401e8da06c7ef4c7602539e3e2b2c95a2b84ed7e8600daf6a04383","source":{"kind":"arxiv","id":"2009.13003","version":1},"attestation_state":"computed","paper":{"title":"On Efficient Constructions of Checkpoints","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bin Ren, Xin Jin, Yu Chen, Zhenming Liu","submitted_at":"2020-09-28T01:20:15Z","abstract_excerpt":"Efficient construction of checkpoints/snapshots is a critical tool for training and diagnosing deep learning models. In this paper, we propose a lossy compression scheme for checkpoint constructions (called LC-Checkpoint). LC-Checkpoint simultaneously maximizes the compression rate and optimizes the recovery speed, under the assumption that SGD is used to train the model. LC-Checkpointuses quantization and priority promotion to store the most crucial information for SGD to recover, and then uses a Huffman coding to leverage the non-uniform distribution of the gradient scales. Our extensive exp"},"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":"2009.13003","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-28T01:20:15Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"bd474595d919eace0cb12d669a1d4b997aa84aca230ac031bdddea68bb1855af","abstract_canon_sha256":"8c966361a552829b76ff6a8ab155c4b671cd86c3fc7ee0ab2b47288216316e78"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:38:25.612590Z","signature_b64":"YgWXcgMmGp7DjvTPHB2fXApIMedQujdrS6TgAXizmFtMRUvDxvDPCni3gHa2ixg49AO5EZrSJSK/Nif1JHnaBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc3d52507b401e8da06c7ef4c7602539e3e2b2c95a2b84ed7e8600daf6a04383","last_reissued_at":"2026-07-05T01:38:25.612177Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:38:25.612177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On Efficient Constructions of Checkpoints","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bin Ren, Xin Jin, Yu Chen, Zhenming Liu","submitted_at":"2020-09-28T01:20:15Z","abstract_excerpt":"Efficient construction of checkpoints/snapshots is a critical tool for training and diagnosing deep learning models. In this paper, we propose a lossy compression scheme for checkpoint constructions (called LC-Checkpoint). LC-Checkpoint simultaneously maximizes the compression rate and optimizes the recovery speed, under the assumption that SGD is used to train the model. LC-Checkpointuses quantization and priority promotion to store the most crucial information for SGD to recover, and then uses a Huffman coding to leverage the non-uniform distribution of the gradient scales. Our extensive exp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.13003","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/2009.13003/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":"2009.13003","created_at":"2026-07-05T01:38:25.612241+00:00"},{"alias_kind":"arxiv_version","alias_value":"2009.13003v1","created_at":"2026-07-05T01:38:25.612241+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.13003","created_at":"2026-07-05T01:38:25.612241+00:00"},{"alias_kind":"pith_short_12","alias_value":"7Q6VEUD3IAPI","created_at":"2026-07-05T01:38:25.612241+00:00"},{"alias_kind":"pith_short_16","alias_value":"7Q6VEUD3IAPI3IDM","created_at":"2026-07-05T01:38:25.612241+00:00"},{"alias_kind":"pith_short_8","alias_value":"7Q6VEUD3","created_at":"2026-07-05T01:38:25.612241+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.12000","citing_title":"An Efficient Compression of Deep Neural Network Checkpoints Based on Prediction and Context Modeling","ref_index":6,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7Q6VEUD3IAPI3IDMP32MOYBFHH","json":"https://pith.science/pith/7Q6VEUD3IAPI3IDMP32MOYBFHH.json","graph_json":"https://pith.science/api/pith-number/7Q6VEUD3IAPI3IDMP32MOYBFHH/graph.json","events_json":"https://pith.science/api/pith-number/7Q6VEUD3IAPI3IDMP32MOYBFHH/events.json","paper":"https://pith.science/paper/7Q6VEUD3"},"agent_actions":{"view_html":"https://pith.science/pith/7Q6VEUD3IAPI3IDMP32MOYBFHH","download_json":"https://pith.science/pith/7Q6VEUD3IAPI3IDMP32MOYBFHH.json","view_paper":"https://pith.science/paper/7Q6VEUD3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2009.13003&json=true","fetch_graph":"https://pith.science/api/pith-number/7Q6VEUD3IAPI3IDMP32MOYBFHH/graph.json","fetch_events":"https://pith.science/api/pith-number/7Q6VEUD3IAPI3IDMP32MOYBFHH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7Q6VEUD3IAPI3IDMP32MOYBFHH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7Q6VEUD3IAPI3IDMP32MOYBFHH/action/storage_attestation","attest_author":"https://pith.science/pith/7Q6VEUD3IAPI3IDMP32MOYBFHH/action/author_attestation","sign_citation":"https://pith.science/pith/7Q6VEUD3IAPI3IDMP32MOYBFHH/action/citation_signature","submit_replication":"https://pith.science/pith/7Q6VEUD3IAPI3IDMP32MOYBFHH/action/replication_record"}},"created_at":"2026-07-05T01:38:25.612241+00:00","updated_at":"2026-07-05T01:38:25.612241+00:00"}