{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3KNC5IZTJ6VL4P5LUH3VJJGL2V","short_pith_number":"pith:3KNC5IZT","schema_version":"1.0","canonical_sha256":"da9a2ea3334faabe3faba1f754a4cbd542ded21ad04ffd0ab8a3f2c73a74facd","source":{"kind":"arxiv","id":"2406.04332","version":1},"attestation_state":"computed","paper":{"title":"Coarse-To-Fine Tensor Trains for Compact Visual Representations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Christian Leth-Espensen, Dan Wang, Michael J. Kastoryano, Sagie Benaim, Sebastian Loeschcke, Serge Belongie","submitted_at":"2024-06-06T17:59:23Z","abstract_excerpt":"The ability to learn compact, high-quality, and easy-to-optimize representations for visual data is paramount to many applications such as novel view synthesis and 3D reconstruction. Recent work has shown substantial success in using tensor networks to design such compact and high-quality representations. However, the ability to optimize tensor-based representations, and in particular, the highly compact tensor train representation, is still lacking. This has prevented practitioners from deploying the full potential of tensor networks for visual data. To this end, we propose 'Prolongation Upsa"},"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":"2406.04332","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-06T17:59:23Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9b0ad399d2d4993a5edc39060824c3a4e735f9dd643bb2a246057e095c409586","abstract_canon_sha256":"ef3178e630a8e6f0fece588e168c3790dfb8a70ece1cf81c9858f3298754c387"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:28:29.521285Z","signature_b64":"vjjZh5x1jF660dpKRF92y4bsT/WWZMza74d4Sa0VBE3YRQoeeVA2prB6aBBPtc5eFPJ4ONu8EVvOOPSvRU4YAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da9a2ea3334faabe3faba1f754a4cbd542ded21ad04ffd0ab8a3f2c73a74facd","last_reissued_at":"2026-07-05T08:28:29.520883Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:28:29.520883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Coarse-To-Fine Tensor Trains for Compact Visual Representations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Christian Leth-Espensen, Dan Wang, Michael J. Kastoryano, Sagie Benaim, Sebastian Loeschcke, Serge Belongie","submitted_at":"2024-06-06T17:59:23Z","abstract_excerpt":"The ability to learn compact, high-quality, and easy-to-optimize representations for visual data is paramount to many applications such as novel view synthesis and 3D reconstruction. Recent work has shown substantial success in using tensor networks to design such compact and high-quality representations. However, the ability to optimize tensor-based representations, and in particular, the highly compact tensor train representation, is still lacking. This has prevented practitioners from deploying the full potential of tensor networks for visual data. To this end, we propose 'Prolongation Upsa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.04332","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/2406.04332/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":"2406.04332","created_at":"2026-07-05T08:28:29.520938+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.04332v1","created_at":"2026-07-05T08:28:29.520938+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.04332","created_at":"2026-07-05T08:28:29.520938+00:00"},{"alias_kind":"pith_short_12","alias_value":"3KNC5IZTJ6VL","created_at":"2026-07-05T08:28:29.520938+00:00"},{"alias_kind":"pith_short_16","alias_value":"3KNC5IZTJ6VL4P5L","created_at":"2026-07-05T08:28:29.520938+00:00"},{"alias_kind":"pith_short_8","alias_value":"3KNC5IZT","created_at":"2026-07-05T08:28:29.520938+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/3KNC5IZTJ6VL4P5LUH3VJJGL2V","json":"https://pith.science/pith/3KNC5IZTJ6VL4P5LUH3VJJGL2V.json","graph_json":"https://pith.science/api/pith-number/3KNC5IZTJ6VL4P5LUH3VJJGL2V/graph.json","events_json":"https://pith.science/api/pith-number/3KNC5IZTJ6VL4P5LUH3VJJGL2V/events.json","paper":"https://pith.science/paper/3KNC5IZT"},"agent_actions":{"view_html":"https://pith.science/pith/3KNC5IZTJ6VL4P5LUH3VJJGL2V","download_json":"https://pith.science/pith/3KNC5IZTJ6VL4P5LUH3VJJGL2V.json","view_paper":"https://pith.science/paper/3KNC5IZT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.04332&json=true","fetch_graph":"https://pith.science/api/pith-number/3KNC5IZTJ6VL4P5LUH3VJJGL2V/graph.json","fetch_events":"https://pith.science/api/pith-number/3KNC5IZTJ6VL4P5LUH3VJJGL2V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3KNC5IZTJ6VL4P5LUH3VJJGL2V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3KNC5IZTJ6VL4P5LUH3VJJGL2V/action/storage_attestation","attest_author":"https://pith.science/pith/3KNC5IZTJ6VL4P5LUH3VJJGL2V/action/author_attestation","sign_citation":"https://pith.science/pith/3KNC5IZTJ6VL4P5LUH3VJJGL2V/action/citation_signature","submit_replication":"https://pith.science/pith/3KNC5IZTJ6VL4P5LUH3VJJGL2V/action/replication_record"}},"created_at":"2026-07-05T08:28:29.520938+00:00","updated_at":"2026-07-05T08:28:29.520938+00:00"}