{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:CODNEDC6VYN7YHCAEQQRBINBJE","short_pith_number":"pith:CODNEDC6","schema_version":"1.0","canonical_sha256":"1386d20c5eae1bfc1c40242110a1a1490dff47a8b8012c15a35db9bf63b28343","source":{"kind":"arxiv","id":"2202.03532","version":2},"attestation_state":"computed","paper":{"title":"MINER: Multiscale Implicit Neural Representations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ashok Veeraraghavan, Guha Balakrishnan, Jasper Tan, Richard G. Baraniuk, Vishwanath Saragadam","submitted_at":"2022-02-07T21:49:33Z","abstract_excerpt":"We introduce a new neural signal model designed for efficient high-resolution representation of large-scale signals. The key innovation in our multiscale implicit neural representation (MINER) is an internal representation via a Laplacian pyramid, which provides a sparse multiscale decomposition of the signal that captures orthogonal parts of the signal across scales. We leverage the advantages of the Laplacian pyramid by representing small disjoint patches of the pyramid at each scale with a small MLP. This enables the capacity of the network to adaptively increase from coarse to fine scales,"},"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":"2202.03532","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-02-07T21:49:33Z","cross_cats_sorted":[],"title_canon_sha256":"30a91bfcf249d7da7081afffa238767c49b5f76b71ef8ea288365ceac4636b2a","abstract_canon_sha256":"c8a14c8ec847004fdca92634a8bcfa1c8978f8031dcb3020123c7576b5e5c15c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:40:50.116479Z","signature_b64":"8Iwy7bzahTwCwvJWoLOhNbmrlTWK1oZ0SQTtQp5mAthPhkn0BOi460EZ6LJHvdjcaqhD84u0tvOcpqktnH+1CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1386d20c5eae1bfc1c40242110a1a1490dff47a8b8012c15a35db9bf63b28343","last_reissued_at":"2026-07-05T04:40:50.116090Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:40:50.116090Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MINER: Multiscale Implicit Neural Representations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ashok Veeraraghavan, Guha Balakrishnan, Jasper Tan, Richard G. Baraniuk, Vishwanath Saragadam","submitted_at":"2022-02-07T21:49:33Z","abstract_excerpt":"We introduce a new neural signal model designed for efficient high-resolution representation of large-scale signals. The key innovation in our multiscale implicit neural representation (MINER) is an internal representation via a Laplacian pyramid, which provides a sparse multiscale decomposition of the signal that captures orthogonal parts of the signal across scales. We leverage the advantages of the Laplacian pyramid by representing small disjoint patches of the pyramid at each scale with a small MLP. This enables the capacity of the network to adaptively increase from coarse to fine scales,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.03532","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/2202.03532/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":"2202.03532","created_at":"2026-07-05T04:40:50.116145+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.03532v2","created_at":"2026-07-05T04:40:50.116145+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.03532","created_at":"2026-07-05T04:40:50.116145+00:00"},{"alias_kind":"pith_short_12","alias_value":"CODNEDC6VYN7","created_at":"2026-07-05T04:40:50.116145+00:00"},{"alias_kind":"pith_short_16","alias_value":"CODNEDC6VYN7YHCA","created_at":"2026-07-05T04:40:50.116145+00:00"},{"alias_kind":"pith_short_8","alias_value":"CODNEDC6","created_at":"2026-07-05T04:40:50.116145+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.20970","citing_title":"From Scalars to Time Series: Rethinking Implicit Neural Representations for Time-Varying Volumetric Data","ref_index":28,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CODNEDC6VYN7YHCAEQQRBINBJE","json":"https://pith.science/pith/CODNEDC6VYN7YHCAEQQRBINBJE.json","graph_json":"https://pith.science/api/pith-number/CODNEDC6VYN7YHCAEQQRBINBJE/graph.json","events_json":"https://pith.science/api/pith-number/CODNEDC6VYN7YHCAEQQRBINBJE/events.json","paper":"https://pith.science/paper/CODNEDC6"},"agent_actions":{"view_html":"https://pith.science/pith/CODNEDC6VYN7YHCAEQQRBINBJE","download_json":"https://pith.science/pith/CODNEDC6VYN7YHCAEQQRBINBJE.json","view_paper":"https://pith.science/paper/CODNEDC6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.03532&json=true","fetch_graph":"https://pith.science/api/pith-number/CODNEDC6VYN7YHCAEQQRBINBJE/graph.json","fetch_events":"https://pith.science/api/pith-number/CODNEDC6VYN7YHCAEQQRBINBJE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CODNEDC6VYN7YHCAEQQRBINBJE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CODNEDC6VYN7YHCAEQQRBINBJE/action/storage_attestation","attest_author":"https://pith.science/pith/CODNEDC6VYN7YHCAEQQRBINBJE/action/author_attestation","sign_citation":"https://pith.science/pith/CODNEDC6VYN7YHCAEQQRBINBJE/action/citation_signature","submit_replication":"https://pith.science/pith/CODNEDC6VYN7YHCAEQQRBINBJE/action/replication_record"}},"created_at":"2026-07-05T04:40:50.116145+00:00","updated_at":"2026-07-05T04:40:50.116145+00:00"}