{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:EUQZRC2SY4KHVWJ7AO3NKJGATY","short_pith_number":"pith:EUQZRC2S","canonical_record":{"source":{"id":"1412.3555","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2014-12-11T06:46:53Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0c032736fa12c22aaf520c75d594fbc8c56d62a8800c47bfba63dac59da5afb6","abstract_canon_sha256":"2ab93a7d3d0441672f73a3514d79891e14d1b6e26abf276e5aa0c7feb46e4a51"},"schema_version":"1.0"},"canonical_sha256":"2521988b52c7147ad93f03b6d524c09e1b7e44a99a9abddc6e2c2f3b66672a00","source":{"kind":"arxiv","id":"1412.3555","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1412.3555","created_at":"2026-07-04T19:15:29Z"},{"alias_kind":"arxiv_version","alias_value":"1412.3555v1","created_at":"2026-07-04T19:15:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.3555","created_at":"2026-07-04T19:15:29Z"},{"alias_kind":"pith_short_12","alias_value":"EUQZRC2SY4KH","created_at":"2026-07-04T19:15:29Z"},{"alias_kind":"pith_short_16","alias_value":"EUQZRC2SY4KHVWJ7","created_at":"2026-07-04T19:15:29Z"},{"alias_kind":"pith_short_8","alias_value":"EUQZRC2S","created_at":"2026-07-04T19:15:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:EUQZRC2SY4KHVWJ7AO3NKJGATY","target":"record","payload":{"canonical_record":{"source":{"id":"1412.3555","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2014-12-11T06:46:53Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0c032736fa12c22aaf520c75d594fbc8c56d62a8800c47bfba63dac59da5afb6","abstract_canon_sha256":"2ab93a7d3d0441672f73a3514d79891e14d1b6e26abf276e5aa0c7feb46e4a51"},"schema_version":"1.0"},"canonical_sha256":"2521988b52c7147ad93f03b6d524c09e1b7e44a99a9abddc6e2c2f3b66672a00","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T19:15:29.200774Z","signature_b64":"WqsIX1nvV8zBtthiiPJETEX+0vRFiM06g1EyDXGqEEJsd9yehGLTMG8P7zxq1uJ0W0EN0Nno4+zIuYCF7Jl4Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2521988b52c7147ad93f03b6d524c09e1b7e44a99a9abddc6e2c2f3b66672a00","last_reissued_at":"2026-07-04T19:15:29.200269Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T19:15:29.200269Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1412.3555","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":"2026-07-04T19:15:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nBaW84hMhipWE4WX2zHbohu8doDRiNicQsxSORbMItmNgDt57Y60XPSaOuU3BkSDe5+ZYALC+T92GE/fjtjaDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-14T20:33:34.372025Z"},"content_sha256":"75ac951a5ab3aad8a362f77a7f74a388a9e2bd4bba4b2fbb4ecdb256b2ac1dcd","schema_version":"1.0","event_id":"sha256:75ac951a5ab3aad8a362f77a7f74a388a9e2bd4bba4b2fbb4ecdb256b2ac1dcd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:EUQZRC2SY4KHVWJ7AO3NKJGATY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Gated recurrent units like LSTM and GRU outperform traditional tanh units on sequence modeling tasks.","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Caglar Gulcehre, Junyoung Chung, Kyunghyun Cho, Yoshua Bengio","submitted_at":"2014-12-11T06:46:53Z","abstract_excerpt":"In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). We evaluate these recurrent units on the tasks of polyphonic music modeling and speech signal modeling. Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM."},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That differences in performance are attributable to the choice of recurrent unit rather than differences in hyperparameter tuning, initialization, or optimization details across the compared models.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Gated recurrent units outperform traditional tanh units on polyphonic music and speech sequence modeling, with GRU performing comparably to LSTM.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Gated recurrent units like LSTM and GRU outperform traditional tanh units on sequence modeling tasks.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"b7718eaa60aa05fbce4ad33ddec91241ab17d43a8f92f0e5b27f4595178ff3ea"},"source":{"id":"1412.3555","kind":"arxiv","version":1},"verdict":{"id":"041f2bd4-03fb-457c-b856-9fe4c79935f2","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-11T17:30:25.621481Z","strongest_claim":"Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM.","one_line_summary":"Gated recurrent units outperform traditional tanh units on polyphonic music and speech sequence modeling, with GRU performing comparably to LSTM.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That differences in performance are attributable to the choice of recurrent unit rather than differences in hyperparameter tuning, initialization, or optimization details across the compared models.","pith_extraction_headline":"Gated recurrent units like LSTM and GRU outperform traditional tanh units on sequence modeling tasks."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1412.3555/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"9658bbab69aec77f5a3106897b15b4e8b7cc3ed69ad6fe7d49f280b403265818"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"041f2bd4-03fb-457c-b856-9fe4c79935f2"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-04T19:15:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RUkqqQKBOnTiU3bE0aqTcmMT+K9zW3JhnTyF600Csy04P0GQ377BQh9i627x2kD11+A8xxe4QaCOPKLJtV+mBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-14T20:33:34.372494Z"},"content_sha256":"2b95f337a79e5ea8a09b0c537f2de52fb10621307125fc9bd6272c43c2e51479","schema_version":"1.0","event_id":"sha256:2b95f337a79e5ea8a09b0c537f2de52fb10621307125fc9bd6272c43c2e51479"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EUQZRC2SY4KHVWJ7AO3NKJGATY/bundle.json","state_url":"https://pith.science/pith/EUQZRC2SY4KHVWJ7AO3NKJGATY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EUQZRC2SY4KHVWJ7AO3NKJGATY/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":"2026-07-14T20:33:34Z","links":{"resolver":"https://pith.science/pith/EUQZRC2SY4KHVWJ7AO3NKJGATY","bundle":"https://pith.science/pith/EUQZRC2SY4KHVWJ7AO3NKJGATY/bundle.json","state":"https://pith.science/pith/EUQZRC2SY4KHVWJ7AO3NKJGATY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EUQZRC2SY4KHVWJ7AO3NKJGATY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:EUQZRC2SY4KHVWJ7AO3NKJGATY","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":"2ab93a7d3d0441672f73a3514d79891e14d1b6e26abf276e5aa0c7feb46e4a51","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2014-12-11T06:46:53Z","title_canon_sha256":"0c032736fa12c22aaf520c75d594fbc8c56d62a8800c47bfba63dac59da5afb6"},"schema_version":"1.0","source":{"id":"1412.3555","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1412.3555","created_at":"2026-07-04T19:15:29Z"},{"alias_kind":"arxiv_version","alias_value":"1412.3555v1","created_at":"2026-07-04T19:15:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1412.3555","created_at":"2026-07-04T19:15:29Z"},{"alias_kind":"pith_short_12","alias_value":"EUQZRC2SY4KH","created_at":"2026-07-04T19:15:29Z"},{"alias_kind":"pith_short_16","alias_value":"EUQZRC2SY4KHVWJ7","created_at":"2026-07-04T19:15:29Z"},{"alias_kind":"pith_short_8","alias_value":"EUQZRC2S","created_at":"2026-07-04T19:15:29Z"}],"graph_snapshots":[{"event_id":"sha256:2b95f337a79e5ea8a09b0c537f2de52fb10621307125fc9bd6272c43c2e51479","target":"graph","created_at":"2026-07-04T19:15:29Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That differences in performance are attributable to the choice of recurrent unit rather than differences in hyperparameter tuning, initialization, or optimization details across the compared models."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Gated recurrent units outperform traditional tanh units on polyphonic music and speech sequence modeling, with GRU performing comparably to LSTM."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Gated recurrent units like LSTM and GRU outperform traditional tanh units on sequence modeling tasks."}],"snapshot_sha256":"b7718eaa60aa05fbce4ad33ddec91241ab17d43a8f92f0e5b27f4595178ff3ea"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"9658bbab69aec77f5a3106897b15b4e8b7cc3ed69ad6fe7d49f280b403265818"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1412.3555/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). We evaluate these recurrent units on the tasks of polyphonic music modeling and speech signal modeling. Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM.","authors_text":"Caglar Gulcehre, Junyoung Chung, Kyunghyun Cho, Yoshua Bengio","cross_cats":["cs.LG"],"headline":"Gated recurrent units like LSTM and GRU outperform traditional tanh units on sequence modeling tasks.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2014-12-11T06:46:53Z","title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1412.3555","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-11T17:30:25.621481Z","id":"041f2bd4-03fb-457c-b856-9fe4c79935f2","model_set":{"reader":"grok-4.3"},"one_line_summary":"Gated recurrent units outperform traditional tanh units on polyphonic music and speech sequence modeling, with GRU performing comparably to LSTM.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Gated recurrent units like LSTM and GRU outperform traditional tanh units on sequence modeling tasks.","strongest_claim":"Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM.","weakest_assumption":"That differences in performance are attributable to the choice of recurrent unit rather than differences in hyperparameter tuning, initialization, or optimization details across the compared models."}},"verdict_id":"041f2bd4-03fb-457c-b856-9fe4c79935f2"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:75ac951a5ab3aad8a362f77a7f74a388a9e2bd4bba4b2fbb4ecdb256b2ac1dcd","target":"record","created_at":"2026-07-04T19:15:29Z","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":"2ab93a7d3d0441672f73a3514d79891e14d1b6e26abf276e5aa0c7feb46e4a51","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2014-12-11T06:46:53Z","title_canon_sha256":"0c032736fa12c22aaf520c75d594fbc8c56d62a8800c47bfba63dac59da5afb6"},"schema_version":"1.0","source":{"id":"1412.3555","kind":"arxiv","version":1}},"canonical_sha256":"2521988b52c7147ad93f03b6d524c09e1b7e44a99a9abddc6e2c2f3b66672a00","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2521988b52c7147ad93f03b6d524c09e1b7e44a99a9abddc6e2c2f3b66672a00","first_computed_at":"2026-07-04T19:15:29.200269Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T19:15:29.200269Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WqsIX1nvV8zBtthiiPJETEX+0vRFiM06g1EyDXGqEEJsd9yehGLTMG8P7zxq1uJ0W0EN0Nno4+zIuYCF7Jl4Cg==","signature_status":"signed_v1","signed_at":"2026-07-04T19:15:29.200774Z","signed_message":"canonical_sha256_bytes"},"source_id":"1412.3555","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:75ac951a5ab3aad8a362f77a7f74a388a9e2bd4bba4b2fbb4ecdb256b2ac1dcd","sha256:2b95f337a79e5ea8a09b0c537f2de52fb10621307125fc9bd6272c43c2e51479"],"state_sha256":"837a346afd695db4da4470348f25d9d004af2beddf572576936c96d73af5649e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A867ibYjTk//YMI8BohDvG2eyKGBqZ+6ureVN1jsnRpTntbqTAHPvb1PKnKmLvwMNACn+CCbfdZosgjWtBO8AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-14T20:33:34.374996Z","bundle_sha256":"5e4c4a5e6273eaf024125969c4a283b873f5eb1dbd113f19929ddcd1331908cf"}}