{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OXUGKS4XVFTC2XSP3L3JPCBNWC","short_pith_number":"pith:OXUGKS4X","schema_version":"1.0","canonical_sha256":"75e8654b97a9662d5e4fdaf697882db0bab88701a26db7403a5ccc9f1033579e","source":{"kind":"arxiv","id":"2401.11243","version":1},"attestation_state":"computed","paper":{"title":"LRP-QViT: Mixed-Precision Vision Transformer Quantization via Layer-wise Relevance Propagation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andreas Savakis, Navin Ranjan","submitted_at":"2024-01-20T14:53:19Z","abstract_excerpt":"Vision transformers (ViTs) have demonstrated remarkable performance across various visual tasks. However, ViT models suffer from substantial computational and memory requirements, making it challenging to deploy them on resource-constrained platforms. Quantization is a popular approach for reducing model size, but most studies mainly focus on equal bit-width quantization for the entire network, resulting in sub-optimal solutions. While there are few works on mixed precision quantization (MPQ) for ViTs, they typically rely on search space-based methods or employ mixed precision arbitrarily. In "},"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":"2401.11243","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-01-20T14:53:19Z","cross_cats_sorted":[],"title_canon_sha256":"c67590cbd18d9d4ac09155123e5fcddee1ded7696b67abe07aba12ee16b1104e","abstract_canon_sha256":"f01546b70b50934228657abcfa52306706c55c23a5e8d5af1b3535ddacdf5b53"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:35:47.870161Z","signature_b64":"N+e59yJlezcUpzSuj7L/wk9R15OjdaGkyroSIwT9JmZiV+rdpT1C9mSPvK8jBkTF/bFTs82ulhkuFYkJh3UfAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"75e8654b97a9662d5e4fdaf697882db0bab88701a26db7403a5ccc9f1033579e","last_reissued_at":"2026-07-05T07:35:47.869746Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:35:47.869746Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LRP-QViT: Mixed-Precision Vision Transformer Quantization via Layer-wise Relevance Propagation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andreas Savakis, Navin Ranjan","submitted_at":"2024-01-20T14:53:19Z","abstract_excerpt":"Vision transformers (ViTs) have demonstrated remarkable performance across various visual tasks. However, ViT models suffer from substantial computational and memory requirements, making it challenging to deploy them on resource-constrained platforms. Quantization is a popular approach for reducing model size, but most studies mainly focus on equal bit-width quantization for the entire network, resulting in sub-optimal solutions. While there are few works on mixed precision quantization (MPQ) for ViTs, they typically rely on search space-based methods or employ mixed precision arbitrarily. In "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.11243","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/2401.11243/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":"2401.11243","created_at":"2026-07-05T07:35:47.869803+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.11243v1","created_at":"2026-07-05T07:35:47.869803+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.11243","created_at":"2026-07-05T07:35:47.869803+00:00"},{"alias_kind":"pith_short_12","alias_value":"OXUGKS4XVFTC","created_at":"2026-07-05T07:35:47.869803+00:00"},{"alias_kind":"pith_short_16","alias_value":"OXUGKS4XVFTC2XSP","created_at":"2026-07-05T07:35:47.869803+00:00"},{"alias_kind":"pith_short_8","alias_value":"OXUGKS4X","created_at":"2026-07-05T07:35:47.869803+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/OXUGKS4XVFTC2XSP3L3JPCBNWC","json":"https://pith.science/pith/OXUGKS4XVFTC2XSP3L3JPCBNWC.json","graph_json":"https://pith.science/api/pith-number/OXUGKS4XVFTC2XSP3L3JPCBNWC/graph.json","events_json":"https://pith.science/api/pith-number/OXUGKS4XVFTC2XSP3L3JPCBNWC/events.json","paper":"https://pith.science/paper/OXUGKS4X"},"agent_actions":{"view_html":"https://pith.science/pith/OXUGKS4XVFTC2XSP3L3JPCBNWC","download_json":"https://pith.science/pith/OXUGKS4XVFTC2XSP3L3JPCBNWC.json","view_paper":"https://pith.science/paper/OXUGKS4X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.11243&json=true","fetch_graph":"https://pith.science/api/pith-number/OXUGKS4XVFTC2XSP3L3JPCBNWC/graph.json","fetch_events":"https://pith.science/api/pith-number/OXUGKS4XVFTC2XSP3L3JPCBNWC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OXUGKS4XVFTC2XSP3L3JPCBNWC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OXUGKS4XVFTC2XSP3L3JPCBNWC/action/storage_attestation","attest_author":"https://pith.science/pith/OXUGKS4XVFTC2XSP3L3JPCBNWC/action/author_attestation","sign_citation":"https://pith.science/pith/OXUGKS4XVFTC2XSP3L3JPCBNWC/action/citation_signature","submit_replication":"https://pith.science/pith/OXUGKS4XVFTC2XSP3L3JPCBNWC/action/replication_record"}},"created_at":"2026-07-05T07:35:47.869803+00:00","updated_at":"2026-07-05T07:35:47.869803+00:00"}