{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:6MNSMMLS4O6JKKT7AAVK32EZAO","short_pith_number":"pith:6MNSMMLS","schema_version":"1.0","canonical_sha256":"f31b263172e3bc952a7f002aade8990385caa6f326e14cf70c1242f497461d40","source":{"kind":"arxiv","id":"1903.02865","version":1},"attestation_state":"computed","paper":{"title":"Deep Learning for Channel Coding via Neural Mutual Information Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.IT"],"primary_cat":"cs.IT","authors_text":"Gerhard Wunder, Rafael F. Schaefer, Rick Fritschek","submitted_at":"2019-03-07T12:21:27Z","abstract_excerpt":"End-to-end deep learning for communication systems, i.e., systems whose encoder and decoder are learned, has attracted significant interest recently, due to its performance which comes close to well-developed classical encoder-decoder designs. However, one of the drawbacks of current learning approaches is that a differentiable channel model is needed for the training of the underlying neural networks. In real-world scenarios, such a channel model is hardly available and often the channel density is not even known at all. Some works, therefore, focus on a generative approach, i.e., generating "},"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":"1903.02865","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2019-03-07T12:21:27Z","cross_cats_sorted":["cs.LG","math.IT"],"title_canon_sha256":"631df9437076d993ce0a27b2b7194657c8b53bc5e9bcdf2327ee8bbbd96058fe","abstract_canon_sha256":"b9fe7121c0f4c4d3493c7cebebccf41f09fd70227a84edb085c4a88e88ece580"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:38.874546Z","signature_b64":"N/ASbSACO8dCEXYR1nXLZFoVIHRl5pN3QSfcxOGsxL+UIlslMXDgRwWJYmsOdA2nX4dA9ofmafUUoTE8DEgPDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f31b263172e3bc952a7f002aade8990385caa6f326e14cf70c1242f497461d40","last_reissued_at":"2026-05-17T23:51:38.873956Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:38.873956Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning for Channel Coding via Neural Mutual Information Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.IT"],"primary_cat":"cs.IT","authors_text":"Gerhard Wunder, Rafael F. Schaefer, Rick Fritschek","submitted_at":"2019-03-07T12:21:27Z","abstract_excerpt":"End-to-end deep learning for communication systems, i.e., systems whose encoder and decoder are learned, has attracted significant interest recently, due to its performance which comes close to well-developed classical encoder-decoder designs. However, one of the drawbacks of current learning approaches is that a differentiable channel model is needed for the training of the underlying neural networks. In real-world scenarios, such a channel model is hardly available and often the channel density is not even known at all. Some works, therefore, focus on a generative approach, i.e., generating "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.02865","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":"1903.02865","created_at":"2026-05-17T23:51:38.874036+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.02865v1","created_at":"2026-05-17T23:51:38.874036+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.02865","created_at":"2026-05-17T23:51:38.874036+00:00"},{"alias_kind":"pith_short_12","alias_value":"6MNSMMLS4O6J","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"6MNSMMLS4O6JKKT7","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"6MNSMMLS","created_at":"2026-05-18T12:33:10.108867+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/6MNSMMLS4O6JKKT7AAVK32EZAO","json":"https://pith.science/pith/6MNSMMLS4O6JKKT7AAVK32EZAO.json","graph_json":"https://pith.science/api/pith-number/6MNSMMLS4O6JKKT7AAVK32EZAO/graph.json","events_json":"https://pith.science/api/pith-number/6MNSMMLS4O6JKKT7AAVK32EZAO/events.json","paper":"https://pith.science/paper/6MNSMMLS"},"agent_actions":{"view_html":"https://pith.science/pith/6MNSMMLS4O6JKKT7AAVK32EZAO","download_json":"https://pith.science/pith/6MNSMMLS4O6JKKT7AAVK32EZAO.json","view_paper":"https://pith.science/paper/6MNSMMLS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.02865&json=true","fetch_graph":"https://pith.science/api/pith-number/6MNSMMLS4O6JKKT7AAVK32EZAO/graph.json","fetch_events":"https://pith.science/api/pith-number/6MNSMMLS4O6JKKT7AAVK32EZAO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6MNSMMLS4O6JKKT7AAVK32EZAO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6MNSMMLS4O6JKKT7AAVK32EZAO/action/storage_attestation","attest_author":"https://pith.science/pith/6MNSMMLS4O6JKKT7AAVK32EZAO/action/author_attestation","sign_citation":"https://pith.science/pith/6MNSMMLS4O6JKKT7AAVK32EZAO/action/citation_signature","submit_replication":"https://pith.science/pith/6MNSMMLS4O6JKKT7AAVK32EZAO/action/replication_record"}},"created_at":"2026-05-17T23:51:38.874036+00:00","updated_at":"2026-05-17T23:51:38.874036+00:00"}