{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:TAW6KPYSSWC65BTXX5DL37RVN2","short_pith_number":"pith:TAW6KPYS","schema_version":"1.0","canonical_sha256":"982de53f129585ee8677bf46bdfe356ebd6ab41d4119875f028351bf964f7cb3","source":{"kind":"arxiv","id":"1811.06029","version":1},"attestation_state":"computed","paper":{"title":"Verification of Recurrent Neural Networks Through Rule Extraction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"C. Lee Giles, Kaixuan Zhang, Qinglong Wang, Xue Liu","submitted_at":"2018-11-14T19:40:30Z","abstract_excerpt":"The verification problem for neural networks is verifying whether a neural network will suffer from adversarial samples, or approximating the maximal allowed scale of adversarial perturbation that can be endured. While most prior work contributes to verifying feed-forward networks, little has been explored for verifying recurrent networks. This is due to the existence of a more rigorous constraint on the perturbation space for sequential data, and the lack of a proper metric for measuring the perturbation. In this work, we address these challenges by proposing a metric which measures the dista"},"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":"1811.06029","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-14T19:40:30Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"17ce882e8323be7dccd919b32aac21729570270c27f219c02bd7ce9fa84f2dfc","abstract_canon_sha256":"bce9d898505ba471f7f6baa53a915e304e4c99dfbea6605d8676758fb428e8f8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:38.841929Z","signature_b64":"TmG6AWGNT/olwUjjmcizsVZhaFrLFnIJECiV483ZHK6fqbm9IbIgqiLVFfIoY91SnXB79XzcAWowtXTyocJIBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"982de53f129585ee8677bf46bdfe356ebd6ab41d4119875f028351bf964f7cb3","last_reissued_at":"2026-05-18T00:00:38.841415Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:38.841415Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Verification of Recurrent Neural Networks Through Rule Extraction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"C. Lee Giles, Kaixuan Zhang, Qinglong Wang, Xue Liu","submitted_at":"2018-11-14T19:40:30Z","abstract_excerpt":"The verification problem for neural networks is verifying whether a neural network will suffer from adversarial samples, or approximating the maximal allowed scale of adversarial perturbation that can be endured. While most prior work contributes to verifying feed-forward networks, little has been explored for verifying recurrent networks. This is due to the existence of a more rigorous constraint on the perturbation space for sequential data, and the lack of a proper metric for measuring the perturbation. In this work, we address these challenges by proposing a metric which measures the dista"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.06029","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":"1811.06029","created_at":"2026-05-18T00:00:38.841493+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.06029v1","created_at":"2026-05-18T00:00:38.841493+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.06029","created_at":"2026-05-18T00:00:38.841493+00:00"},{"alias_kind":"pith_short_12","alias_value":"TAW6KPYSSWC6","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"TAW6KPYSSWC65BTX","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"TAW6KPYS","created_at":"2026-05-18T12:32:53.628368+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/TAW6KPYSSWC65BTXX5DL37RVN2","json":"https://pith.science/pith/TAW6KPYSSWC65BTXX5DL37RVN2.json","graph_json":"https://pith.science/api/pith-number/TAW6KPYSSWC65BTXX5DL37RVN2/graph.json","events_json":"https://pith.science/api/pith-number/TAW6KPYSSWC65BTXX5DL37RVN2/events.json","paper":"https://pith.science/paper/TAW6KPYS"},"agent_actions":{"view_html":"https://pith.science/pith/TAW6KPYSSWC65BTXX5DL37RVN2","download_json":"https://pith.science/pith/TAW6KPYSSWC65BTXX5DL37RVN2.json","view_paper":"https://pith.science/paper/TAW6KPYS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.06029&json=true","fetch_graph":"https://pith.science/api/pith-number/TAW6KPYSSWC65BTXX5DL37RVN2/graph.json","fetch_events":"https://pith.science/api/pith-number/TAW6KPYSSWC65BTXX5DL37RVN2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TAW6KPYSSWC65BTXX5DL37RVN2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TAW6KPYSSWC65BTXX5DL37RVN2/action/storage_attestation","attest_author":"https://pith.science/pith/TAW6KPYSSWC65BTXX5DL37RVN2/action/author_attestation","sign_citation":"https://pith.science/pith/TAW6KPYSSWC65BTXX5DL37RVN2/action/citation_signature","submit_replication":"https://pith.science/pith/TAW6KPYSSWC65BTXX5DL37RVN2/action/replication_record"}},"created_at":"2026-05-18T00:00:38.841493+00:00","updated_at":"2026-05-18T00:00:38.841493+00:00"}