{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:D3PEAUYCG2ASB7ONRFDAMK6BBW","short_pith_number":"pith:D3PEAUYC","schema_version":"1.0","canonical_sha256":"1ede405302368120fdcd8946062bc10d81a05eff384b95b749593b9329c91b30","source":{"kind":"arxiv","id":"1612.00745","version":1},"attestation_state":"computed","paper":{"title":"Cognitive Deep Machine Can Train Itself","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.NE"],"primary_cat":"cs.LG","authors_text":"Andr\\'as L\\H{o}rincz, Andr\\'as S\\'ark\\'any, \\'Aron F\\'othi, M\\'at\\'e Cs\\'akv\\'ari, Zolt\\'an \\'Ad\\'am Milacski, Zolt\\'an T\\H{o}s\\'er","submitted_at":"2016-12-02T16:49:07Z","abstract_excerpt":"Machine learning is making substantial progress in diverse applications. The success is mostly due to advances in deep learning. However, deep learning can make mistakes and its generalization abilities to new tasks are questionable. We ask when and how one can combine network outputs, when (i) details of the observations are evaluated by learned deep components and (ii) facts and confirmation rules are available in knowledge based systems. We show that in limited contexts the required number of training samples can be low and self-improvement of pre-trained networks in more general context is"},"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":"1612.00745","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-12-02T16:49:07Z","cross_cats_sorted":["cs.AI","cs.NE"],"title_canon_sha256":"f141f4a2e761c090d19e71b0cd1784ef018a8f911ccd3eede605fe7ffeb4e774","abstract_canon_sha256":"6ffefb4268e9f76b0f454b71b8c68a3fc639acf7a4dbedb13262241b835c6811"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:55:59.137106Z","signature_b64":"iV92ewphDHLCetQFotblL6YReS/LwOcqGUtvQdnaJfysT6OaiFNQt7OB1b2bh11q3QOgdSgvC9bAElIPK01kCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ede405302368120fdcd8946062bc10d81a05eff384b95b749593b9329c91b30","last_reissued_at":"2026-05-18T00:55:59.136355Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:55:59.136355Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cognitive Deep Machine Can Train Itself","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.NE"],"primary_cat":"cs.LG","authors_text":"Andr\\'as L\\H{o}rincz, Andr\\'as S\\'ark\\'any, \\'Aron F\\'othi, M\\'at\\'e Cs\\'akv\\'ari, Zolt\\'an \\'Ad\\'am Milacski, Zolt\\'an T\\H{o}s\\'er","submitted_at":"2016-12-02T16:49:07Z","abstract_excerpt":"Machine learning is making substantial progress in diverse applications. The success is mostly due to advances in deep learning. However, deep learning can make mistakes and its generalization abilities to new tasks are questionable. We ask when and how one can combine network outputs, when (i) details of the observations are evaluated by learned deep components and (ii) facts and confirmation rules are available in knowledge based systems. We show that in limited contexts the required number of training samples can be low and self-improvement of pre-trained networks in more general context is"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.00745","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":""},"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":"1612.00745","created_at":"2026-05-18T00:55:59.136483+00:00"},{"alias_kind":"arxiv_version","alias_value":"1612.00745v1","created_at":"2026-05-18T00:55:59.136483+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.00745","created_at":"2026-05-18T00:55:59.136483+00:00"},{"alias_kind":"pith_short_12","alias_value":"D3PEAUYCG2AS","created_at":"2026-05-18T12:30:09.641336+00:00"},{"alias_kind":"pith_short_16","alias_value":"D3PEAUYCG2ASB7ON","created_at":"2026-05-18T12:30:09.641336+00:00"},{"alias_kind":"pith_short_8","alias_value":"D3PEAUYC","created_at":"2026-05-18T12:30:09.641336+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/D3PEAUYCG2ASB7ONRFDAMK6BBW","json":"https://pith.science/pith/D3PEAUYCG2ASB7ONRFDAMK6BBW.json","graph_json":"https://pith.science/api/pith-number/D3PEAUYCG2ASB7ONRFDAMK6BBW/graph.json","events_json":"https://pith.science/api/pith-number/D3PEAUYCG2ASB7ONRFDAMK6BBW/events.json","paper":"https://pith.science/paper/D3PEAUYC"},"agent_actions":{"view_html":"https://pith.science/pith/D3PEAUYCG2ASB7ONRFDAMK6BBW","download_json":"https://pith.science/pith/D3PEAUYCG2ASB7ONRFDAMK6BBW.json","view_paper":"https://pith.science/paper/D3PEAUYC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1612.00745&json=true","fetch_graph":"https://pith.science/api/pith-number/D3PEAUYCG2ASB7ONRFDAMK6BBW/graph.json","fetch_events":"https://pith.science/api/pith-number/D3PEAUYCG2ASB7ONRFDAMK6BBW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D3PEAUYCG2ASB7ONRFDAMK6BBW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D3PEAUYCG2ASB7ONRFDAMK6BBW/action/storage_attestation","attest_author":"https://pith.science/pith/D3PEAUYCG2ASB7ONRFDAMK6BBW/action/author_attestation","sign_citation":"https://pith.science/pith/D3PEAUYCG2ASB7ONRFDAMK6BBW/action/citation_signature","submit_replication":"https://pith.science/pith/D3PEAUYCG2ASB7ONRFDAMK6BBW/action/replication_record"}},"created_at":"2026-05-18T00:55:59.136483+00:00","updated_at":"2026-05-18T00:55:59.136483+00:00"}