{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:YWVOOL5JJXTNYKZIB5G4TK4QPE","short_pith_number":"pith:YWVOOL5J","schema_version":"1.0","canonical_sha256":"c5aae72fa94de6dc2b280f4dc9ab9079179eef9e9734fc973767c07126761c33","source":{"kind":"arxiv","id":"2108.03167","version":1},"attestation_state":"computed","paper":{"title":"Generalized Tensor Summation Compressive Sensing Network (GTSNET): An Easy to Learn Compressive Sensing Operation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.IT"],"primary_cat":"eess.SP","authors_text":"Erdem Sahin, Mehmet Yamac, Moncef Gabbouj, Serkan Kiranyaz, Ugur Akpinar","submitted_at":"2021-08-04T13:13:50Z","abstract_excerpt":"In CS literature, the efforts can be divided into two groups: finding a measurement matrix that preserves the compressed information at the maximum level, and finding a reconstruction algorithm for the compressed information. In the traditional CS setup, the measurement matrices are selected as random matrices, and optimization-based iterative solutions are used to recover the signals. However, when we handle large signals, using random matrices become cumbersome especially when it comes to iterative optimization-based solutions. Even though recent deep learning-based solutions boost the recon"},"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":"2108.03167","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.SP","submitted_at":"2021-08-04T13:13:50Z","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"title_canon_sha256":"b61f8563ea1882ec72a73363592dd66c37ec305e87441df5a65c206323aae750","abstract_canon_sha256":"d890e96b5975418549ae1f5dc6da2b2f3d9396cc5e3084a3d00b2aabad6a6358"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:03:46.998761Z","signature_b64":"+4FYr413vI43OYqQcpca2B5caVj3VkZPS7JmmV2AGN530lzaY98Y9oRt4CnNmwczyf9QQ4trfFXmWnB5/R7QDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5aae72fa94de6dc2b280f4dc9ab9079179eef9e9734fc973767c07126761c33","last_reissued_at":"2026-07-05T03:03:46.998269Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:03:46.998269Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generalized Tensor Summation Compressive Sensing Network (GTSNET): An Easy to Learn Compressive Sensing Operation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.IT"],"primary_cat":"eess.SP","authors_text":"Erdem Sahin, Mehmet Yamac, Moncef Gabbouj, Serkan Kiranyaz, Ugur Akpinar","submitted_at":"2021-08-04T13:13:50Z","abstract_excerpt":"In CS literature, the efforts can be divided into two groups: finding a measurement matrix that preserves the compressed information at the maximum level, and finding a reconstruction algorithm for the compressed information. In the traditional CS setup, the measurement matrices are selected as random matrices, and optimization-based iterative solutions are used to recover the signals. However, when we handle large signals, using random matrices become cumbersome especially when it comes to iterative optimization-based solutions. Even though recent deep learning-based solutions boost the recon"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.03167","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/2108.03167/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":"2108.03167","created_at":"2026-07-05T03:03:46.998336+00:00"},{"alias_kind":"arxiv_version","alias_value":"2108.03167v1","created_at":"2026-07-05T03:03:46.998336+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.03167","created_at":"2026-07-05T03:03:46.998336+00:00"},{"alias_kind":"pith_short_12","alias_value":"YWVOOL5JJXTN","created_at":"2026-07-05T03:03:46.998336+00:00"},{"alias_kind":"pith_short_16","alias_value":"YWVOOL5JJXTNYKZI","created_at":"2026-07-05T03:03:46.998336+00:00"},{"alias_kind":"pith_short_8","alias_value":"YWVOOL5J","created_at":"2026-07-05T03:03:46.998336+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/YWVOOL5JJXTNYKZIB5G4TK4QPE","json":"https://pith.science/pith/YWVOOL5JJXTNYKZIB5G4TK4QPE.json","graph_json":"https://pith.science/api/pith-number/YWVOOL5JJXTNYKZIB5G4TK4QPE/graph.json","events_json":"https://pith.science/api/pith-number/YWVOOL5JJXTNYKZIB5G4TK4QPE/events.json","paper":"https://pith.science/paper/YWVOOL5J"},"agent_actions":{"view_html":"https://pith.science/pith/YWVOOL5JJXTNYKZIB5G4TK4QPE","download_json":"https://pith.science/pith/YWVOOL5JJXTNYKZIB5G4TK4QPE.json","view_paper":"https://pith.science/paper/YWVOOL5J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2108.03167&json=true","fetch_graph":"https://pith.science/api/pith-number/YWVOOL5JJXTNYKZIB5G4TK4QPE/graph.json","fetch_events":"https://pith.science/api/pith-number/YWVOOL5JJXTNYKZIB5G4TK4QPE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YWVOOL5JJXTNYKZIB5G4TK4QPE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YWVOOL5JJXTNYKZIB5G4TK4QPE/action/storage_attestation","attest_author":"https://pith.science/pith/YWVOOL5JJXTNYKZIB5G4TK4QPE/action/author_attestation","sign_citation":"https://pith.science/pith/YWVOOL5JJXTNYKZIB5G4TK4QPE/action/citation_signature","submit_replication":"https://pith.science/pith/YWVOOL5JJXTNYKZIB5G4TK4QPE/action/replication_record"}},"created_at":"2026-07-05T03:03:46.998336+00:00","updated_at":"2026-07-05T03:03:46.998336+00:00"}