{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:L3U35QGXB2553ODKCNXEMGXFVA","short_pith_number":"pith:L3U35QGX","schema_version":"1.0","canonical_sha256":"5ee9bec0d70ebbddb86a136e461ae5a81cb37fc553098544bed9c00e240f3266","source":{"kind":"arxiv","id":"1702.07560","version":1},"attestation_state":"computed","paper":{"title":"RNN Decoding of Linear Block Codes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE","math.IT"],"primary_cat":"cs.IT","authors_text":"David Burshtein, Elad Marciano, Eliya Nachmani, Yair Be'ery","submitted_at":"2017-02-24T12:49:29Z","abstract_excerpt":"Designing a practical, low complexity, close to optimal, channel decoder for powerful algebraic codes with short to moderate block length is an open research problem. Recently it has been shown that a feed-forward neural network architecture can improve on standard belief propagation decoding, despite the large example space. In this paper we introduce a recurrent neural network architecture for decoding linear block codes. Our method shows comparable bit error rate results compared to the feed-forward neural network with significantly less parameters. We also demonstrate improved performance "},"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":"1702.07560","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-02-24T12:49:29Z","cross_cats_sorted":["cs.LG","cs.NE","math.IT"],"title_canon_sha256":"03263e64de3bb5b564ac2c2d48c1b4f0557697f53e3f73c8f6be425e57f500e0","abstract_canon_sha256":"68927c4aad9bdde40c9e3e5bfffd220b9b9e7c58e20ae32f9e7060a48706d50e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:50:02.903595Z","signature_b64":"WHwKJZuzACGgqzbm0nZuF/SKRRHuTFQQ/NG8QLAZ1g7mCNi8yT8ewjS3BQSBD+iBqMMYaiLJ5zV4d8qh3dPlAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ee9bec0d70ebbddb86a136e461ae5a81cb37fc553098544bed9c00e240f3266","last_reissued_at":"2026-05-18T00:50:02.902910Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:50:02.902910Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RNN Decoding of Linear Block Codes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE","math.IT"],"primary_cat":"cs.IT","authors_text":"David Burshtein, Elad Marciano, Eliya Nachmani, Yair Be'ery","submitted_at":"2017-02-24T12:49:29Z","abstract_excerpt":"Designing a practical, low complexity, close to optimal, channel decoder for powerful algebraic codes with short to moderate block length is an open research problem. Recently it has been shown that a feed-forward neural network architecture can improve on standard belief propagation decoding, despite the large example space. In this paper we introduce a recurrent neural network architecture for decoding linear block codes. Our method shows comparable bit error rate results compared to the feed-forward neural network with significantly less parameters. We also demonstrate improved performance "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.07560","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":"1702.07560","created_at":"2026-05-18T00:50:02.903020+00:00"},{"alias_kind":"arxiv_version","alias_value":"1702.07560v1","created_at":"2026-05-18T00:50:02.903020+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.07560","created_at":"2026-05-18T00:50:02.903020+00:00"},{"alias_kind":"pith_short_12","alias_value":"L3U35QGXB255","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"L3U35QGXB2553ODK","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"L3U35QGX","created_at":"2026-05-18T12:31:28.150371+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/L3U35QGXB2553ODKCNXEMGXFVA","json":"https://pith.science/pith/L3U35QGXB2553ODKCNXEMGXFVA.json","graph_json":"https://pith.science/api/pith-number/L3U35QGXB2553ODKCNXEMGXFVA/graph.json","events_json":"https://pith.science/api/pith-number/L3U35QGXB2553ODKCNXEMGXFVA/events.json","paper":"https://pith.science/paper/L3U35QGX"},"agent_actions":{"view_html":"https://pith.science/pith/L3U35QGXB2553ODKCNXEMGXFVA","download_json":"https://pith.science/pith/L3U35QGXB2553ODKCNXEMGXFVA.json","view_paper":"https://pith.science/paper/L3U35QGX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1702.07560&json=true","fetch_graph":"https://pith.science/api/pith-number/L3U35QGXB2553ODKCNXEMGXFVA/graph.json","fetch_events":"https://pith.science/api/pith-number/L3U35QGXB2553ODKCNXEMGXFVA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L3U35QGXB2553ODKCNXEMGXFVA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L3U35QGXB2553ODKCNXEMGXFVA/action/storage_attestation","attest_author":"https://pith.science/pith/L3U35QGXB2553ODKCNXEMGXFVA/action/author_attestation","sign_citation":"https://pith.science/pith/L3U35QGXB2553ODKCNXEMGXFVA/action/citation_signature","submit_replication":"https://pith.science/pith/L3U35QGXB2553ODKCNXEMGXFVA/action/replication_record"}},"created_at":"2026-05-18T00:50:02.903020+00:00","updated_at":"2026-05-18T00:50:02.903020+00:00"}