{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:4VJVIA7QVZRMBQQEHRAJXYXPTT","short_pith_number":"pith:4VJVIA7Q","schema_version":"1.0","canonical_sha256":"e5535403f0ae62c0c2043c409be2ef9cdd108895facbc037702c5e4f970cf05e","source":{"kind":"arxiv","id":"1911.09464","version":2},"attestation_state":"computed","paper":{"title":"Quantization Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Bing Deng, Houqiang Li, Jianqiang Huang, Jiwei Yang, Jun Xing, Xiansheng Hua, Xinmei Tian, Xu Shen","submitted_at":"2019-11-21T13:44:03Z","abstract_excerpt":"Although deep neural networks are highly effective, their high computational and memory costs severely challenge their applications on portable devices. As a consequence, low-bit quantization, which converts a full-precision neural network into a low-bitwidth integer version, has been an active and promising research topic. Existing methods formulate the low-bit quantization of networks as an approximation or optimization problem. Approximation-based methods confront the gradient mismatch problem, while optimization-based methods are only suitable for quantizing weights and could introduce hig"},"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":"1911.09464","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-11-21T13:44:03Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"274a6ec5d39d22c5c6f15dd016610b613b53384c6428fa534370330ff19fa490","abstract_canon_sha256":"42c2f1532dc4ef51088268df017b62a3e8253fd9d6f7fde1837688c00909cee2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:22:43.386280Z","signature_b64":"QHWgj554DQ5GBqI6jOUiYgv52kJVW1e2pNgLpNJU1IBEIT5DAu6y8FdYhnN9V46IpZQ4qS57Ca113nu08ZFTBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e5535403f0ae62c0c2043c409be2ef9cdd108895facbc037702c5e4f970cf05e","last_reissued_at":"2026-07-05T00:22:43.385883Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:22:43.385883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantization Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Bing Deng, Houqiang Li, Jianqiang Huang, Jiwei Yang, Jun Xing, Xiansheng Hua, Xinmei Tian, Xu Shen","submitted_at":"2019-11-21T13:44:03Z","abstract_excerpt":"Although deep neural networks are highly effective, their high computational and memory costs severely challenge their applications on portable devices. As a consequence, low-bit quantization, which converts a full-precision neural network into a low-bitwidth integer version, has been an active and promising research topic. Existing methods formulate the low-bit quantization of networks as an approximation or optimization problem. Approximation-based methods confront the gradient mismatch problem, while optimization-based methods are only suitable for quantizing weights and could introduce hig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.09464","kind":"arxiv","version":2},"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/1911.09464/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":"1911.09464","created_at":"2026-07-05T00:22:43.385937+00:00"},{"alias_kind":"arxiv_version","alias_value":"1911.09464v2","created_at":"2026-07-05T00:22:43.385937+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.09464","created_at":"2026-07-05T00:22:43.385937+00:00"},{"alias_kind":"pith_short_12","alias_value":"4VJVIA7QVZRM","created_at":"2026-07-05T00:22:43.385937+00:00"},{"alias_kind":"pith_short_16","alias_value":"4VJVIA7QVZRMBQQE","created_at":"2026-07-05T00:22:43.385937+00:00"},{"alias_kind":"pith_short_8","alias_value":"4VJVIA7Q","created_at":"2026-07-05T00:22:43.385937+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/4VJVIA7QVZRMBQQEHRAJXYXPTT","json":"https://pith.science/pith/4VJVIA7QVZRMBQQEHRAJXYXPTT.json","graph_json":"https://pith.science/api/pith-number/4VJVIA7QVZRMBQQEHRAJXYXPTT/graph.json","events_json":"https://pith.science/api/pith-number/4VJVIA7QVZRMBQQEHRAJXYXPTT/events.json","paper":"https://pith.science/paper/4VJVIA7Q"},"agent_actions":{"view_html":"https://pith.science/pith/4VJVIA7QVZRMBQQEHRAJXYXPTT","download_json":"https://pith.science/pith/4VJVIA7QVZRMBQQEHRAJXYXPTT.json","view_paper":"https://pith.science/paper/4VJVIA7Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1911.09464&json=true","fetch_graph":"https://pith.science/api/pith-number/4VJVIA7QVZRMBQQEHRAJXYXPTT/graph.json","fetch_events":"https://pith.science/api/pith-number/4VJVIA7QVZRMBQQEHRAJXYXPTT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4VJVIA7QVZRMBQQEHRAJXYXPTT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4VJVIA7QVZRMBQQEHRAJXYXPTT/action/storage_attestation","attest_author":"https://pith.science/pith/4VJVIA7QVZRMBQQEHRAJXYXPTT/action/author_attestation","sign_citation":"https://pith.science/pith/4VJVIA7QVZRMBQQEHRAJXYXPTT/action/citation_signature","submit_replication":"https://pith.science/pith/4VJVIA7QVZRMBQQEHRAJXYXPTT/action/replication_record"}},"created_at":"2026-07-05T00:22:43.385937+00:00","updated_at":"2026-07-05T00:22:43.385937+00:00"}