{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UWQL6HKOC4N62RFVV3RBYH2M6M","short_pith_number":"pith:UWQL6HKO","schema_version":"1.0","canonical_sha256":"a5a0bf1d4e171bed44b5aee21c1f4cf300c63c7caeac13bd901c1df6f70baa4f","source":{"kind":"arxiv","id":"2407.17834","version":1},"attestation_state":"computed","paper":{"title":"Towards the Spectral bias Alleviation by Normalizations in Coordinate Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hao Zhu, Qiu Shen, Xinran Wang, Xun Cao, Zhicheng Cai","submitted_at":"2024-07-25T07:45:28Z","abstract_excerpt":"Representing signals using coordinate networks dominates the area of inverse problems recently, and is widely applied in various scientific computing tasks. Still, there exists an issue of spectral bias in coordinate networks, limiting the capacity to learn high-frequency components. This problem is caused by the pathological distribution of the neural tangent kernel's (NTK's) eigenvalues of coordinate networks. We find that, this pathological distribution could be improved using classical normalization techniques (batch normalization and layer normalization), which are commonly used in convol"},"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":"2407.17834","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-07-25T07:45:28Z","cross_cats_sorted":[],"title_canon_sha256":"7aa09d208329aa90eeab1074200a10cec7a861ebde2f1987820d13a172a661fd","abstract_canon_sha256":"f7e9316ab43a9e13f8efbee6f24490f4e386ca8cbb7cf8ec0104b828f56615a4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:48:22.283294Z","signature_b64":"e1yUs6MAi3vl41EwdC9CKg/jgxdwm+OKBx6guq27Cun34QwbYVBV3o0QTG9acdOhoEvrkT2xtri3uLv//PnSDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5a0bf1d4e171bed44b5aee21c1f4cf300c63c7caeac13bd901c1df6f70baa4f","last_reissued_at":"2026-07-05T08:48:22.282840Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:48:22.282840Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards the Spectral bias Alleviation by Normalizations in Coordinate Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hao Zhu, Qiu Shen, Xinran Wang, Xun Cao, Zhicheng Cai","submitted_at":"2024-07-25T07:45:28Z","abstract_excerpt":"Representing signals using coordinate networks dominates the area of inverse problems recently, and is widely applied in various scientific computing tasks. Still, there exists an issue of spectral bias in coordinate networks, limiting the capacity to learn high-frequency components. This problem is caused by the pathological distribution of the neural tangent kernel's (NTK's) eigenvalues of coordinate networks. We find that, this pathological distribution could be improved using classical normalization techniques (batch normalization and layer normalization), which are commonly used in convol"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.17834","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/2407.17834/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":"2407.17834","created_at":"2026-07-05T08:48:22.282894+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.17834v1","created_at":"2026-07-05T08:48:22.282894+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.17834","created_at":"2026-07-05T08:48:22.282894+00:00"},{"alias_kind":"pith_short_12","alias_value":"UWQL6HKOC4N6","created_at":"2026-07-05T08:48:22.282894+00:00"},{"alias_kind":"pith_short_16","alias_value":"UWQL6HKOC4N62RFV","created_at":"2026-07-05T08:48:22.282894+00:00"},{"alias_kind":"pith_short_8","alias_value":"UWQL6HKO","created_at":"2026-07-05T08:48:22.282894+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2511.18384","citing_title":"NSTR: Neural Spectral Transport Representation for Space-Varying Frequency Fields","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2511.18387","citing_title":"Scaling Implicit Fields via Hypernetwork-Driven Multiscale Coordinate Transformations","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UWQL6HKOC4N62RFVV3RBYH2M6M","json":"https://pith.science/pith/UWQL6HKOC4N62RFVV3RBYH2M6M.json","graph_json":"https://pith.science/api/pith-number/UWQL6HKOC4N62RFVV3RBYH2M6M/graph.json","events_json":"https://pith.science/api/pith-number/UWQL6HKOC4N62RFVV3RBYH2M6M/events.json","paper":"https://pith.science/paper/UWQL6HKO"},"agent_actions":{"view_html":"https://pith.science/pith/UWQL6HKOC4N62RFVV3RBYH2M6M","download_json":"https://pith.science/pith/UWQL6HKOC4N62RFVV3RBYH2M6M.json","view_paper":"https://pith.science/paper/UWQL6HKO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.17834&json=true","fetch_graph":"https://pith.science/api/pith-number/UWQL6HKOC4N62RFVV3RBYH2M6M/graph.json","fetch_events":"https://pith.science/api/pith-number/UWQL6HKOC4N62RFVV3RBYH2M6M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UWQL6HKOC4N62RFVV3RBYH2M6M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UWQL6HKOC4N62RFVV3RBYH2M6M/action/storage_attestation","attest_author":"https://pith.science/pith/UWQL6HKOC4N62RFVV3RBYH2M6M/action/author_attestation","sign_citation":"https://pith.science/pith/UWQL6HKOC4N62RFVV3RBYH2M6M/action/citation_signature","submit_replication":"https://pith.science/pith/UWQL6HKOC4N62RFVV3RBYH2M6M/action/replication_record"}},"created_at":"2026-07-05T08:48:22.282894+00:00","updated_at":"2026-07-05T08:48:22.282894+00:00"}