{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:L2PG2FSND4UNFSALGVG4Y2KBNO","short_pith_number":"pith:L2PG2FSN","schema_version":"1.0","canonical_sha256":"5e9e6d164d1f28d2c80b354dcc69416b8e74499e51a17118943a88fd7d66ec91","source":{"kind":"arxiv","id":"2404.14674","version":1},"attestation_state":"computed","paper":{"title":"HOIN: High-Order Implicit Neural Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.MM"],"primary_cat":"cs.LG","authors_text":"Ce Zhu, Ruituo Wu, Yang Chen, Yipeng Liu","submitted_at":"2024-04-23T02:00:58Z","abstract_excerpt":"Implicit neural representations (INR) suffer from worsening spectral bias, which results in overly smooth solutions to the inverse problem. To deal with this problem, we propose a universal framework for processing inverse problems called \\textbf{High-Order Implicit Neural Representations (HOIN)}. By refining the traditional cascade structure to foster high-order interactions among features, HOIN enhances the model's expressive power and mitigates spectral bias through its neural tangent kernel's (NTK) strong diagonal properties, accelerating and optimizing inverse problem resolution. By analy"},"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":"2404.14674","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-04-23T02:00:58Z","cross_cats_sorted":["cs.AI","cs.CV","cs.MM"],"title_canon_sha256":"4aef29c86a71f818b6450b3aae5bf1263a3ec555ab0f51a8b071679c174e0e7a","abstract_canon_sha256":"32a74c0a751073670e18baf1634c6eaa45a50949668102ae78353b5c6bb596c5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:11:08.530432Z","signature_b64":"zxf9gCd7V3w9sSGx6pfOLwQIhIJZigF8MrU55XdFz6OGSdTaJn3Wgwy0uUkq0aIdLC2PZsouX2tzIFxuto/eBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e9e6d164d1f28d2c80b354dcc69416b8e74499e51a17118943a88fd7d66ec91","last_reissued_at":"2026-07-05T08:11:08.530021Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:11:08.530021Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HOIN: High-Order Implicit Neural Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.MM"],"primary_cat":"cs.LG","authors_text":"Ce Zhu, Ruituo Wu, Yang Chen, Yipeng Liu","submitted_at":"2024-04-23T02:00:58Z","abstract_excerpt":"Implicit neural representations (INR) suffer from worsening spectral bias, which results in overly smooth solutions to the inverse problem. To deal with this problem, we propose a universal framework for processing inverse problems called \\textbf{High-Order Implicit Neural Representations (HOIN)}. By refining the traditional cascade structure to foster high-order interactions among features, HOIN enhances the model's expressive power and mitigates spectral bias through its neural tangent kernel's (NTK) strong diagonal properties, accelerating and optimizing inverse problem resolution. By analy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.14674","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/2404.14674/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":"2404.14674","created_at":"2026-07-05T08:11:08.530075+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.14674v1","created_at":"2026-07-05T08:11:08.530075+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.14674","created_at":"2026-07-05T08:11:08.530075+00:00"},{"alias_kind":"pith_short_12","alias_value":"L2PG2FSND4UN","created_at":"2026-07-05T08:11:08.530075+00:00"},{"alias_kind":"pith_short_16","alias_value":"L2PG2FSND4UNFSAL","created_at":"2026-07-05T08:11:08.530075+00:00"},{"alias_kind":"pith_short_8","alias_value":"L2PG2FSN","created_at":"2026-07-05T08:11:08.530075+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.01412","citing_title":"Vis-CoT: A Human-in-the-Loop Framework for Interactive Visualization and Intervention in LLM Chain-of-Thought Reasoning","ref_index":40,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L2PG2FSND4UNFSALGVG4Y2KBNO","json":"https://pith.science/pith/L2PG2FSND4UNFSALGVG4Y2KBNO.json","graph_json":"https://pith.science/api/pith-number/L2PG2FSND4UNFSALGVG4Y2KBNO/graph.json","events_json":"https://pith.science/api/pith-number/L2PG2FSND4UNFSALGVG4Y2KBNO/events.json","paper":"https://pith.science/paper/L2PG2FSN"},"agent_actions":{"view_html":"https://pith.science/pith/L2PG2FSND4UNFSALGVG4Y2KBNO","download_json":"https://pith.science/pith/L2PG2FSND4UNFSALGVG4Y2KBNO.json","view_paper":"https://pith.science/paper/L2PG2FSN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.14674&json=true","fetch_graph":"https://pith.science/api/pith-number/L2PG2FSND4UNFSALGVG4Y2KBNO/graph.json","fetch_events":"https://pith.science/api/pith-number/L2PG2FSND4UNFSALGVG4Y2KBNO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L2PG2FSND4UNFSALGVG4Y2KBNO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L2PG2FSND4UNFSALGVG4Y2KBNO/action/storage_attestation","attest_author":"https://pith.science/pith/L2PG2FSND4UNFSALGVG4Y2KBNO/action/author_attestation","sign_citation":"https://pith.science/pith/L2PG2FSND4UNFSALGVG4Y2KBNO/action/citation_signature","submit_replication":"https://pith.science/pith/L2PG2FSND4UNFSALGVG4Y2KBNO/action/replication_record"}},"created_at":"2026-07-05T08:11:08.530075+00:00","updated_at":"2026-07-05T08:11:08.530075+00:00"}