{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NMAULY3S45PFEDBUJA7PZWSTAW","short_pith_number":"pith:NMAULY3S","schema_version":"1.0","canonical_sha256":"6b0145e372e75e520c34483efcda53059ed1e54743ddd06a9483b3be91b1c787","source":{"kind":"arxiv","id":"2506.06322","version":1},"attestation_state":"computed","paper":{"title":"Neural networks with image recognition by pairs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NE","authors_text":"Polad Geidarov","submitted_at":"2025-05-29T15:20:14Z","abstract_excerpt":"Neural networks based on metric recognition methods have a strictly determined architecture. Number of neurons, connections, as well as weights and thresholds values are calculated analytically, based on the initial conditions of tasks: number of recognizable classes, number of samples, metric expressions used. This paper discusses the possibility of transforming these networks in order to apply classical learning algorithms to them without using analytical expressions that calculate weight values. In the received network, training is carried out by recognizing images in pairs. This approach s"},"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":"2506.06322","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2025-05-29T15:20:14Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9ab8bed6622414979d19931629faa725b55336908b5cc09f6aaed44e1fd9881e","abstract_canon_sha256":"fa895c73b138596e09c968a9bb176b1697e107590a36e53ac41b2cac0a16f667"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:47.990624Z","signature_b64":"iKCH65XhVReTMjI1Kf3I/WXh4fucnyPJX07Y7FM999b7JGsCV8Y0a36Ekx1XOcCUfh9AE/M0EkN3abXxjVY2Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6b0145e372e75e520c34483efcda53059ed1e54743ddd06a9483b3be91b1c787","last_reissued_at":"2026-07-05T11:17:47.990145Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:47.990145Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural networks with image recognition by pairs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NE","authors_text":"Polad Geidarov","submitted_at":"2025-05-29T15:20:14Z","abstract_excerpt":"Neural networks based on metric recognition methods have a strictly determined architecture. Number of neurons, connections, as well as weights and thresholds values are calculated analytically, based on the initial conditions of tasks: number of recognizable classes, number of samples, metric expressions used. This paper discusses the possibility of transforming these networks in order to apply classical learning algorithms to them without using analytical expressions that calculate weight values. In the received network, training is carried out by recognizing images in pairs. This approach s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06322","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/2506.06322/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":"2506.06322","created_at":"2026-07-05T11:17:47.990208+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.06322v1","created_at":"2026-07-05T11:17:47.990208+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06322","created_at":"2026-07-05T11:17:47.990208+00:00"},{"alias_kind":"pith_short_12","alias_value":"NMAULY3S45PF","created_at":"2026-07-05T11:17:47.990208+00:00"},{"alias_kind":"pith_short_16","alias_value":"NMAULY3S45PFEDBU","created_at":"2026-07-05T11:17:47.990208+00:00"},{"alias_kind":"pith_short_8","alias_value":"NMAULY3S","created_at":"2026-07-05T11:17:47.990208+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/NMAULY3S45PFEDBUJA7PZWSTAW","json":"https://pith.science/pith/NMAULY3S45PFEDBUJA7PZWSTAW.json","graph_json":"https://pith.science/api/pith-number/NMAULY3S45PFEDBUJA7PZWSTAW/graph.json","events_json":"https://pith.science/api/pith-number/NMAULY3S45PFEDBUJA7PZWSTAW/events.json","paper":"https://pith.science/paper/NMAULY3S"},"agent_actions":{"view_html":"https://pith.science/pith/NMAULY3S45PFEDBUJA7PZWSTAW","download_json":"https://pith.science/pith/NMAULY3S45PFEDBUJA7PZWSTAW.json","view_paper":"https://pith.science/paper/NMAULY3S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.06322&json=true","fetch_graph":"https://pith.science/api/pith-number/NMAULY3S45PFEDBUJA7PZWSTAW/graph.json","fetch_events":"https://pith.science/api/pith-number/NMAULY3S45PFEDBUJA7PZWSTAW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NMAULY3S45PFEDBUJA7PZWSTAW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NMAULY3S45PFEDBUJA7PZWSTAW/action/storage_attestation","attest_author":"https://pith.science/pith/NMAULY3S45PFEDBUJA7PZWSTAW/action/author_attestation","sign_citation":"https://pith.science/pith/NMAULY3S45PFEDBUJA7PZWSTAW/action/citation_signature","submit_replication":"https://pith.science/pith/NMAULY3S45PFEDBUJA7PZWSTAW/action/replication_record"}},"created_at":"2026-07-05T11:17:47.990208+00:00","updated_at":"2026-07-05T11:17:47.990208+00:00"}