{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:GMK2XGYXQUVGU6JGDD2MFK7DAO","short_pith_number":"pith:GMK2XGYX","canonical_record":{"source":{"id":"1805.09317","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-23T17:58:37Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1a89781af718dd78b70a3025035f88d6d0c9280f8599b84d5fc936b651652521","abstract_canon_sha256":"0db317cc76189210a71e53828917da7522977ec140e6c595aa23f7b738a85049"},"schema_version":"1.0"},"canonical_sha256":"3315ab9b17852a6a792618f4c2abe30392a11f5ae90f1ee1ad6d0b1602eabeb2","source":{"kind":"arxiv","id":"1805.09317","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.09317","created_at":"2026-05-18T00:15:05Z"},{"alias_kind":"arxiv_version","alias_value":"1805.09317v1","created_at":"2026-05-18T00:15:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.09317","created_at":"2026-05-18T00:15:05Z"},{"alias_kind":"pith_short_12","alias_value":"GMK2XGYXQUVG","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"GMK2XGYXQUVGU6JG","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"GMK2XGYX","created_at":"2026-05-18T12:32:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:GMK2XGYXQUVGU6JGDD2MFK7DAO","target":"record","payload":{"canonical_record":{"source":{"id":"1805.09317","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-23T17:58:37Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1a89781af718dd78b70a3025035f88d6d0c9280f8599b84d5fc936b651652521","abstract_canon_sha256":"0db317cc76189210a71e53828917da7522977ec140e6c595aa23f7b738a85049"},"schema_version":"1.0"},"canonical_sha256":"3315ab9b17852a6a792618f4c2abe30392a11f5ae90f1ee1ad6d0b1602eabeb2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:05.980858Z","signature_b64":"QbA712INXg79mUwMoVBRv/hPF3N8bNpxBvgBoNlGADLaj2S0ISBCxDpPwdiGz492L3W4GuyAKPHfWDmZRyhiDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3315ab9b17852a6a792618f4c2abe30392a11f5ae90f1ee1ad6d0b1602eabeb2","last_reissued_at":"2026-05-18T00:15:05.980213Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:05.980213Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.09317","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":"2026-05-18T00:15:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zwYPlTuAcyg9lX3yEDYjxjVvpIs2341WCCdNWRpuwQFOHEZEDE/wRNK2ndnlKBOY/ugZpuuKtvzAX7fI+LosDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T06:22:56.730223Z"},"content_sha256":"d0b8e742d7e7846e98c57b616d40fab177410a4472f8e5bdb9f55b0ad60a863e","schema_version":"1.0","event_id":"sha256:d0b8e742d7e7846e98c57b616d40fab177410a4472f8e5bdb9f55b0ad60a863e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:GMK2XGYXQUVGU6JGDD2MFK7DAO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Communication Algorithms via Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Hyeji Kim, Pramod Viswanath, Ranvir Rana, Sewoong Oh, Sreeram Kannan, Yihan Jiang","submitted_at":"2018-05-23T17:58:37Z","abstract_excerpt":"Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age. Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century. In this paper we study whether it is possible to automate the discovery of decoding algorithms via deep learning. We study a family of sequential codes parameterized by recurrent neural network (RNN) architectures. We show that creatively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.09317","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":"2026-05-18T00:15:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cw5JIVnTbVuRSbxd2GuWwSt4ok8B2jY0ItgomoTUqYTzyryiUiVePrz9I/ymyfVdwCmWfHbMaoQu3KzVJHCODw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T06:22:56.730942Z"},"content_sha256":"87b08e81b443f1343eb9a0c0784f64638eff7b2f47eeb3ef3b0ec8bb08f3251b","schema_version":"1.0","event_id":"sha256:87b08e81b443f1343eb9a0c0784f64638eff7b2f47eeb3ef3b0ec8bb08f3251b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GMK2XGYXQUVGU6JGDD2MFK7DAO/bundle.json","state_url":"https://pith.science/pith/GMK2XGYXQUVGU6JGDD2MFK7DAO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GMK2XGYXQUVGU6JGDD2MFK7DAO/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":"2026-06-06T06:22:56Z","links":{"resolver":"https://pith.science/pith/GMK2XGYXQUVGU6JGDD2MFK7DAO","bundle":"https://pith.science/pith/GMK2XGYXQUVGU6JGDD2MFK7DAO/bundle.json","state":"https://pith.science/pith/GMK2XGYXQUVGU6JGDD2MFK7DAO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GMK2XGYXQUVGU6JGDD2MFK7DAO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:GMK2XGYXQUVGU6JGDD2MFK7DAO","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":"0db317cc76189210a71e53828917da7522977ec140e6c595aa23f7b738a85049","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-23T17:58:37Z","title_canon_sha256":"1a89781af718dd78b70a3025035f88d6d0c9280f8599b84d5fc936b651652521"},"schema_version":"1.0","source":{"id":"1805.09317","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.09317","created_at":"2026-05-18T00:15:05Z"},{"alias_kind":"arxiv_version","alias_value":"1805.09317v1","created_at":"2026-05-18T00:15:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.09317","created_at":"2026-05-18T00:15:05Z"},{"alias_kind":"pith_short_12","alias_value":"GMK2XGYXQUVG","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"GMK2XGYXQUVGU6JG","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"GMK2XGYX","created_at":"2026-05-18T12:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:87b08e81b443f1343eb9a0c0784f64638eff7b2f47eeb3ef3b0ec8bb08f3251b","target":"graph","created_at":"2026-05-18T00:15:05Z","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":"Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age. Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century. In this paper we study whether it is possible to automate the discovery of decoding algorithms via deep learning. We study a family of sequential codes parameterized by recurrent neural network (RNN) architectures. We show that creatively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and ","authors_text":"Hyeji Kim, Pramod Viswanath, Ranvir Rana, Sewoong Oh, Sreeram Kannan, Yihan Jiang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-23T17:58:37Z","title":"Communication Algorithms via Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.09317","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:d0b8e742d7e7846e98c57b616d40fab177410a4472f8e5bdb9f55b0ad60a863e","target":"record","created_at":"2026-05-18T00:15:05Z","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":"0db317cc76189210a71e53828917da7522977ec140e6c595aa23f7b738a85049","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-23T17:58:37Z","title_canon_sha256":"1a89781af718dd78b70a3025035f88d6d0c9280f8599b84d5fc936b651652521"},"schema_version":"1.0","source":{"id":"1805.09317","kind":"arxiv","version":1}},"canonical_sha256":"3315ab9b17852a6a792618f4c2abe30392a11f5ae90f1ee1ad6d0b1602eabeb2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3315ab9b17852a6a792618f4c2abe30392a11f5ae90f1ee1ad6d0b1602eabeb2","first_computed_at":"2026-05-18T00:15:05.980213Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:05.980213Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QbA712INXg79mUwMoVBRv/hPF3N8bNpxBvgBoNlGADLaj2S0ISBCxDpPwdiGz492L3W4GuyAKPHfWDmZRyhiDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:05.980858Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.09317","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d0b8e742d7e7846e98c57b616d40fab177410a4472f8e5bdb9f55b0ad60a863e","sha256:87b08e81b443f1343eb9a0c0784f64638eff7b2f47eeb3ef3b0ec8bb08f3251b"],"state_sha256":"990b9926e35636784ec66e9568c1e1ac132deb03070fa2ca6604c8ea15ad6a67"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wbt3UxpSvMM3ZLrSxev/ocoXVp7gaNvBTweBwGfCcSJ+gnvQX/60BVz1bx3KrYaYHzlu7dG8eUsxfgfVbYMWBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T06:22:56.734705Z","bundle_sha256":"1bf2ca67928b3bfda9a8bcd458ce5ee19e9deae136e877127e866c27fef911c3"}}