{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:I2Y3DZP3VKEWXPZEZJBV4XCOJB","short_pith_number":"pith:I2Y3DZP3","canonical_record":{"source":{"id":"1701.07738","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-01-26T15:24:57Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"50f239d1efebe450cfd18f243b669e61bae726252c3c01f9d3622e1398a36928","abstract_canon_sha256":"955a19c5f2e43f289469c9b29a827ca641fc0c9239b6c100d781b47b23eba8c4"},"schema_version":"1.0"},"canonical_sha256":"46b1b1e5fbaa896bbf24ca435e5c4e4879b7aecabc6e59f64df4906bab326c5d","source":{"kind":"arxiv","id":"1701.07738","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.07738","created_at":"2026-05-18T00:52:02Z"},{"alias_kind":"arxiv_version","alias_value":"1701.07738v1","created_at":"2026-05-18T00:52:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.07738","created_at":"2026-05-18T00:52:02Z"},{"alias_kind":"pith_short_12","alias_value":"I2Y3DZP3VKEW","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"I2Y3DZP3VKEWXPZE","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"I2Y3DZP3","created_at":"2026-05-18T12:31:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:I2Y3DZP3VKEWXPZEZJBV4XCOJB","target":"record","payload":{"canonical_record":{"source":{"id":"1701.07738","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-01-26T15:24:57Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"50f239d1efebe450cfd18f243b669e61bae726252c3c01f9d3622e1398a36928","abstract_canon_sha256":"955a19c5f2e43f289469c9b29a827ca641fc0c9239b6c100d781b47b23eba8c4"},"schema_version":"1.0"},"canonical_sha256":"46b1b1e5fbaa896bbf24ca435e5c4e4879b7aecabc6e59f64df4906bab326c5d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:52:02.722387Z","signature_b64":"5TxkRQKAaW4+SmVuEayTVyEQQORtm+U4V48ktehZxC04o+dikm6QSOxlyQaNcBlL/5iUZAhJ9SmWv7aO1TyiBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46b1b1e5fbaa896bbf24ca435e5c4e4879b7aecabc6e59f64df4906bab326c5d","last_reissued_at":"2026-05-18T00:52:02.721881Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:52:02.721881Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1701.07738","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:52:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IUIvcA+F8yTj8+VDC2aztUgoOzKjrSKq0uz/hds1waMILKXvAR10rEBIvrf1Imgc1ql0ombamXmoXBUtoNN9AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T22:34:59.870052Z"},"content_sha256":"2f7e644e17a1b2482f82a498f724a385bf8fc8475e07719a84c068d06f63b46d","schema_version":"1.0","event_id":"sha256:2f7e644e17a1b2482f82a498f724a385bf8fc8475e07719a84c068d06f63b46d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:I2Y3DZP3VKEWXPZEZJBV4XCOJB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On Deep Learning-Based Channel Decoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Jakob Hoydis, Sebastian Cammerer, Stephan ten Brink, Tobias Gruber","submitted_at":"2017-01-26T15:24:57Z","abstract_excerpt":"We revisit the idea of using deep neural networks for one-shot decoding of random and structured codes, such as polar codes. Although it is possible to achieve maximum a posteriori (MAP) bit error rate (BER) performance for both code families and for short codeword lengths, we observe that (i) structured codes are easier to learn and (ii) the neural network is able to generalize to codewords that it has never seen during training for structured, but not for random codes. These results provide some evidence that neural networks can learn a form of decoding algorithm, rather than only a simple c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.07738","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:52:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bPKUMLGRn/6Y8p8mYfOEEeSYppcCssbMpHQssZFF0NFAve9xR/Pb8yT4B+m2XFif00NCugm0/36sqQEfTWLbBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T22:34:59.870749Z"},"content_sha256":"37e1cfb9b615bee99db4ca4f25aeb5ed1d951c4c66a721f9f2cf357d12af5559","schema_version":"1.0","event_id":"sha256:37e1cfb9b615bee99db4ca4f25aeb5ed1d951c4c66a721f9f2cf357d12af5559"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I2Y3DZP3VKEWXPZEZJBV4XCOJB/bundle.json","state_url":"https://pith.science/pith/I2Y3DZP3VKEWXPZEZJBV4XCOJB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I2Y3DZP3VKEWXPZEZJBV4XCOJB/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-25T22:34:59Z","links":{"resolver":"https://pith.science/pith/I2Y3DZP3VKEWXPZEZJBV4XCOJB","bundle":"https://pith.science/pith/I2Y3DZP3VKEWXPZEZJBV4XCOJB/bundle.json","state":"https://pith.science/pith/I2Y3DZP3VKEWXPZEZJBV4XCOJB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I2Y3DZP3VKEWXPZEZJBV4XCOJB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:I2Y3DZP3VKEWXPZEZJBV4XCOJB","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"955a19c5f2e43f289469c9b29a827ca641fc0c9239b6c100d781b47b23eba8c4","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-01-26T15:24:57Z","title_canon_sha256":"50f239d1efebe450cfd18f243b669e61bae726252c3c01f9d3622e1398a36928"},"schema_version":"1.0","source":{"id":"1701.07738","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.07738","created_at":"2026-05-18T00:52:02Z"},{"alias_kind":"arxiv_version","alias_value":"1701.07738v1","created_at":"2026-05-18T00:52:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.07738","created_at":"2026-05-18T00:52:02Z"},{"alias_kind":"pith_short_12","alias_value":"I2Y3DZP3VKEW","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"I2Y3DZP3VKEWXPZE","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"I2Y3DZP3","created_at":"2026-05-18T12:31:21Z"}],"graph_snapshots":[{"event_id":"sha256:37e1cfb9b615bee99db4ca4f25aeb5ed1d951c4c66a721f9f2cf357d12af5559","target":"graph","created_at":"2026-05-18T00:52:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We revisit the idea of using deep neural networks for one-shot decoding of random and structured codes, such as polar codes. Although it is possible to achieve maximum a posteriori (MAP) bit error rate (BER) performance for both code families and for short codeword lengths, we observe that (i) structured codes are easier to learn and (ii) the neural network is able to generalize to codewords that it has never seen during training for structured, but not for random codes. These results provide some evidence that neural networks can learn a form of decoding algorithm, rather than only a simple c","authors_text":"Jakob Hoydis, Sebastian Cammerer, Stephan ten Brink, Tobias Gruber","cross_cats":["math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-01-26T15:24:57Z","title":"On Deep Learning-Based Channel Decoding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.07738","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2f7e644e17a1b2482f82a498f724a385bf8fc8475e07719a84c068d06f63b46d","target":"record","created_at":"2026-05-18T00:52:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"955a19c5f2e43f289469c9b29a827ca641fc0c9239b6c100d781b47b23eba8c4","cross_cats_sorted":["math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-01-26T15:24:57Z","title_canon_sha256":"50f239d1efebe450cfd18f243b669e61bae726252c3c01f9d3622e1398a36928"},"schema_version":"1.0","source":{"id":"1701.07738","kind":"arxiv","version":1}},"canonical_sha256":"46b1b1e5fbaa896bbf24ca435e5c4e4879b7aecabc6e59f64df4906bab326c5d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"46b1b1e5fbaa896bbf24ca435e5c4e4879b7aecabc6e59f64df4906bab326c5d","first_computed_at":"2026-05-18T00:52:02.721881Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:52:02.721881Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5TxkRQKAaW4+SmVuEayTVyEQQORtm+U4V48ktehZxC04o+dikm6QSOxlyQaNcBlL/5iUZAhJ9SmWv7aO1TyiBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:52:02.722387Z","signed_message":"canonical_sha256_bytes"},"source_id":"1701.07738","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2f7e644e17a1b2482f82a498f724a385bf8fc8475e07719a84c068d06f63b46d","sha256:37e1cfb9b615bee99db4ca4f25aeb5ed1d951c4c66a721f9f2cf357d12af5559"],"state_sha256":"fec7e1031cf30ec764939ef943be844f9ba58fa7c1406460872018429e384569"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vDGnYMREaZWmXEm2lEnsS3dyPPusTmFrMJEHCCrQ2wT1Uq2cY1IRrCOr9GW9j84qsrr9IIbbUIULLA4jQ6K2Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T22:34:59.874458Z","bundle_sha256":"15d250b71492cd1cfbf979933cab44da9b8a8bf24f3bc1d98c6acd3dc47cfe66"}}