{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:64DM6YJUCMP576Y7MJHSNHRNSU","short_pith_number":"pith:64DM6YJU","schema_version":"1.0","canonical_sha256":"f706cf6134131fdffb1f624f269e2d9501cba6a1b3d2cbd97b06a46fcc628c1f","source":{"kind":"arxiv","id":"2401.17269","version":1},"attestation_state":"computed","paper":{"title":"Effect of Weight Quantization on Learning Models by Typical Case Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ayaka Sakata, Masaaki Imaizumi, Shuhei Kashiwamura","submitted_at":"2024-01-30T18:58:46Z","abstract_excerpt":"This paper examines the quantization methods used in large-scale data analysis models and their hyperparameter choices. The recent surge in data analysis scale has significantly increased computational resource requirements. To address this, quantizing model weights has become a prevalent practice in data analysis applications such as deep learning. Quantization is particularly vital for deploying large models on devices with limited computational resources. However, the selection of quantization hyperparameters, like the number of bits and value range for weight quantization, remains an under"},"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":"2401.17269","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-01-30T18:58:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4bf313ce752e5ab5e951a861ac69d1487f4d322edc8610db300e135f950cf976","abstract_canon_sha256":"a0af74e2ef7f5c41c2a972271d970ba613a979321d660d044838610a23dd96b9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:39:22.672070Z","signature_b64":"0PYgC32/XU1tChPSixuZXItlfMcesrhLOtpC84lREB6P8LPyc5t3hCuYybFbZJzVnryXFWD04AJ49H9NYHKDCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f706cf6134131fdffb1f624f269e2d9501cba6a1b3d2cbd97b06a46fcc628c1f","last_reissued_at":"2026-07-05T07:39:22.671613Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:39:22.671613Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Effect of Weight Quantization on Learning Models by Typical Case Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ayaka Sakata, Masaaki Imaizumi, Shuhei Kashiwamura","submitted_at":"2024-01-30T18:58:46Z","abstract_excerpt":"This paper examines the quantization methods used in large-scale data analysis models and their hyperparameter choices. The recent surge in data analysis scale has significantly increased computational resource requirements. To address this, quantizing model weights has become a prevalent practice in data analysis applications such as deep learning. Quantization is particularly vital for deploying large models on devices with limited computational resources. However, the selection of quantization hyperparameters, like the number of bits and value range for weight quantization, remains an under"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.17269","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/2401.17269/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":"2401.17269","created_at":"2026-07-05T07:39:22.671670+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.17269v1","created_at":"2026-07-05T07:39:22.671670+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.17269","created_at":"2026-07-05T07:39:22.671670+00:00"},{"alias_kind":"pith_short_12","alias_value":"64DM6YJUCMP5","created_at":"2026-07-05T07:39:22.671670+00:00"},{"alias_kind":"pith_short_16","alias_value":"64DM6YJUCMP576Y7","created_at":"2026-07-05T07:39:22.671670+00:00"},{"alias_kind":"pith_short_8","alias_value":"64DM6YJU","created_at":"2026-07-05T07:39:22.671670+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/64DM6YJUCMP576Y7MJHSNHRNSU","json":"https://pith.science/pith/64DM6YJUCMP576Y7MJHSNHRNSU.json","graph_json":"https://pith.science/api/pith-number/64DM6YJUCMP576Y7MJHSNHRNSU/graph.json","events_json":"https://pith.science/api/pith-number/64DM6YJUCMP576Y7MJHSNHRNSU/events.json","paper":"https://pith.science/paper/64DM6YJU"},"agent_actions":{"view_html":"https://pith.science/pith/64DM6YJUCMP576Y7MJHSNHRNSU","download_json":"https://pith.science/pith/64DM6YJUCMP576Y7MJHSNHRNSU.json","view_paper":"https://pith.science/paper/64DM6YJU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.17269&json=true","fetch_graph":"https://pith.science/api/pith-number/64DM6YJUCMP576Y7MJHSNHRNSU/graph.json","fetch_events":"https://pith.science/api/pith-number/64DM6YJUCMP576Y7MJHSNHRNSU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/64DM6YJUCMP576Y7MJHSNHRNSU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/64DM6YJUCMP576Y7MJHSNHRNSU/action/storage_attestation","attest_author":"https://pith.science/pith/64DM6YJUCMP576Y7MJHSNHRNSU/action/author_attestation","sign_citation":"https://pith.science/pith/64DM6YJUCMP576Y7MJHSNHRNSU/action/citation_signature","submit_replication":"https://pith.science/pith/64DM6YJUCMP576Y7MJHSNHRNSU/action/replication_record"}},"created_at":"2026-07-05T07:39:22.671670+00:00","updated_at":"2026-07-05T07:39:22.671670+00:00"}