{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:G635M4ZMVMLES7Y2G47R65I5HH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1a3a50be6f980184bfecab986a6853538902c3ae1fe38d204f5f51fb414fac8b","cross_cats_sorted":["eess.SP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-12-07T04:05:52Z","title_canon_sha256":"41d63ef12dbfd35a9e236b98e9fd455800cce77840bde1c1bbd7b6d8460da4f4"},"schema_version":"1.0","source":{"id":"1912.03433","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.03433","created_at":"2026-07-05T01:25:36Z"},{"alias_kind":"arxiv_version","alias_value":"1912.03433v3","created_at":"2026-07-05T01:25:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.03433","created_at":"2026-07-05T01:25:36Z"},{"alias_kind":"pith_short_12","alias_value":"G635M4ZMVMLE","created_at":"2026-07-05T01:25:36Z"},{"alias_kind":"pith_short_16","alias_value":"G635M4ZMVMLES7Y2","created_at":"2026-07-05T01:25:36Z"},{"alias_kind":"pith_short_8","alias_value":"G635M4ZM","created_at":"2026-07-05T01:25:36Z"}],"graph_snapshots":[{"event_id":"sha256:b0c741df17f945beabadedab6aa0516d434f7be547a628110edec74f79e41a0b","target":"graph","created_at":"2026-07-05T01:25:36Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1912.03433/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Structured low-rank (SLR) algorithms, which exploit annihilation relations between the Fourier samples of a signal resulting from different properties, is a powerful image reconstruction framework in several applications. This scheme relies on low-rank matrix completion to estimate the annihilation relations from the measurements. The main challenge with this strategy is the high computational complexity of matrix completion. We introduce a deep learning (DL) approach to significantly reduce the computational complexity. Specifically, we use a convolutional neural network (CNN)-based filterban","authors_text":"Aniket Pramanik, Hemant Aggarwal, Mathews Jacob","cross_cats":["eess.SP","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-12-07T04:05:52Z","title":"Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.03433","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c996af1825eb69685f54b2d765f18447db1ad01950b3cbb40871edff8ddff7c9","target":"record","created_at":"2026-07-05T01:25:36Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1a3a50be6f980184bfecab986a6853538902c3ae1fe38d204f5f51fb414fac8b","cross_cats_sorted":["eess.SP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-12-07T04:05:52Z","title_canon_sha256":"41d63ef12dbfd35a9e236b98e9fd455800cce77840bde1c1bbd7b6d8460da4f4"},"schema_version":"1.0","source":{"id":"1912.03433","kind":"arxiv","version":3}},"canonical_sha256":"37b7d6732cab16497f1a373f1f751d39f335da9c73b1934ea02917682c6a8f06","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"37b7d6732cab16497f1a373f1f751d39f335da9c73b1934ea02917682c6a8f06","first_computed_at":"2026-07-05T01:25:36.377668Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:25:36.377668Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LnT5xg4czJ5MAUXGQmXGBZRu4hu6mX1RUbWBiRRu5oQSa6wSYCROILpq28V56Vmyroc8cGoJtvJL6fRSiwJfCw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:25:36.378090Z","signed_message":"canonical_sha256_bytes"},"source_id":"1912.03433","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c996af1825eb69685f54b2d765f18447db1ad01950b3cbb40871edff8ddff7c9","sha256:b0c741df17f945beabadedab6aa0516d434f7be547a628110edec74f79e41a0b"],"state_sha256":"af2876c721579955214ee26f6dfb948e0bfde572329b211be77d3dd2da4ce1de"}