{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JCP77S3AKSIISWT7RMQE4VUOQD","short_pith_number":"pith:JCP77S3A","schema_version":"1.0","canonical_sha256":"489fffcb605490895a7f8b204e568e80e667ce144f5324809d51d9411a179b9c","source":{"kind":"arxiv","id":"2502.09669","version":1},"attestation_state":"computed","paper":{"title":"Meta-INR: Efficient Encoding of Volumetric Data via Meta-Learning Implicit Neural Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.GR"],"primary_cat":"cs.CV","authors_text":"Chaoli Wang, Kaiyuan Tang, Maizhe Yang","submitted_at":"2025-02-12T21:54:22Z","abstract_excerpt":"Implicit neural representation (INR) has emerged as a promising solution for encoding volumetric data, offering continuous representations and seamless compatibility with the volume rendering pipeline. However, optimizing an INR network from randomly initialized parameters for each new volume is computationally inefficient, especially for large-scale time-varying or ensemble volumetric datasets where volumes share similar structural patterns but require independent training. To close this gap, we propose Meta-INR, a pretraining strategy adapted from meta-learning algorithms to learn initial 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":"2502.09669","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-12T21:54:22Z","cross_cats_sorted":["cs.AI","cs.GR"],"title_canon_sha256":"394a15bb1afeab3ec581cdb82a203820495534550c76483e9b647dcfd44752cb","abstract_canon_sha256":"5971e3a0f865a58b3d1213883452133560bd8234886cb260c1e6d4060b2f0151"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:14:10.296398Z","signature_b64":"otuDVoN0HXMDydfMdGI9eJ/7hMt+VPrBCQ2zhW58O7fph1wmzehoWYFoaRLJtA7RKzfGuVDfakpEDXfooh77Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"489fffcb605490895a7f8b204e568e80e667ce144f5324809d51d9411a179b9c","last_reissued_at":"2026-07-05T10:14:10.295879Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:14:10.295879Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Meta-INR: Efficient Encoding of Volumetric Data via Meta-Learning Implicit Neural Representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.GR"],"primary_cat":"cs.CV","authors_text":"Chaoli Wang, Kaiyuan Tang, Maizhe Yang","submitted_at":"2025-02-12T21:54:22Z","abstract_excerpt":"Implicit neural representation (INR) has emerged as a promising solution for encoding volumetric data, offering continuous representations and seamless compatibility with the volume rendering pipeline. However, optimizing an INR network from randomly initialized parameters for each new volume is computationally inefficient, especially for large-scale time-varying or ensemble volumetric datasets where volumes share similar structural patterns but require independent training. To close this gap, we propose Meta-INR, a pretraining strategy adapted from meta-learning algorithms to learn initial IN"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.09669","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/2502.09669/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":"2502.09669","created_at":"2026-07-05T10:14:10.295968+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.09669v1","created_at":"2026-07-05T10:14:10.295968+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.09669","created_at":"2026-07-05T10:14:10.295968+00:00"},{"alias_kind":"pith_short_12","alias_value":"JCP77S3AKSII","created_at":"2026-07-05T10:14:10.295968+00:00"},{"alias_kind":"pith_short_16","alias_value":"JCP77S3AKSIISWT7","created_at":"2026-07-05T10:14:10.295968+00:00"},{"alias_kind":"pith_short_8","alias_value":"JCP77S3A","created_at":"2026-07-05T10:14:10.295968+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.03836","citing_title":"F-Hash: Feature-Based Hash Design for Time-Varying Volume Visualization via Multi-Resolution Tesseract Encoding","ref_index":46,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JCP77S3AKSIISWT7RMQE4VUOQD","json":"https://pith.science/pith/JCP77S3AKSIISWT7RMQE4VUOQD.json","graph_json":"https://pith.science/api/pith-number/JCP77S3AKSIISWT7RMQE4VUOQD/graph.json","events_json":"https://pith.science/api/pith-number/JCP77S3AKSIISWT7RMQE4VUOQD/events.json","paper":"https://pith.science/paper/JCP77S3A"},"agent_actions":{"view_html":"https://pith.science/pith/JCP77S3AKSIISWT7RMQE4VUOQD","download_json":"https://pith.science/pith/JCP77S3AKSIISWT7RMQE4VUOQD.json","view_paper":"https://pith.science/paper/JCP77S3A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.09669&json=true","fetch_graph":"https://pith.science/api/pith-number/JCP77S3AKSIISWT7RMQE4VUOQD/graph.json","fetch_events":"https://pith.science/api/pith-number/JCP77S3AKSIISWT7RMQE4VUOQD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JCP77S3AKSIISWT7RMQE4VUOQD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JCP77S3AKSIISWT7RMQE4VUOQD/action/storage_attestation","attest_author":"https://pith.science/pith/JCP77S3AKSIISWT7RMQE4VUOQD/action/author_attestation","sign_citation":"https://pith.science/pith/JCP77S3AKSIISWT7RMQE4VUOQD/action/citation_signature","submit_replication":"https://pith.science/pith/JCP77S3AKSIISWT7RMQE4VUOQD/action/replication_record"}},"created_at":"2026-07-05T10:14:10.295968+00:00","updated_at":"2026-07-05T10:14:10.295968+00:00"}