{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:6T7HNX2JFFNELMAXGSTGRQLNIP","short_pith_number":"pith:6T7HNX2J","canonical_record":{"source":{"id":"1804.11188","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-23T09:17:35Z","cross_cats_sorted":["cs.NE","stat.ML"],"title_canon_sha256":"ed0389c379dd6608df486bd801488ccabbd82f832143afa0ac7b92aacf9a943a","abstract_canon_sha256":"2ba97eacb30566a57f4046384307f575e5968d421ee8f4198f8c388fba0a44ae"},"schema_version":"1.0"},"canonical_sha256":"f4fe76df49295a45b01734a668c16d43d9bf5a434ff655f36ecdb8eeaf7659bc","source":{"kind":"arxiv","id":"1804.11188","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.11188","created_at":"2026-05-18T00:17:14Z"},{"alias_kind":"arxiv_version","alias_value":"1804.11188v1","created_at":"2026-05-18T00:17:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.11188","created_at":"2026-05-18T00:17:14Z"},{"alias_kind":"pith_short_12","alias_value":"6T7HNX2JFFNE","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"6T7HNX2JFFNELMAX","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"6T7HNX2J","created_at":"2026-05-18T12:32:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:6T7HNX2JFFNELMAXGSTGRQLNIP","target":"record","payload":{"canonical_record":{"source":{"id":"1804.11188","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-23T09:17:35Z","cross_cats_sorted":["cs.NE","stat.ML"],"title_canon_sha256":"ed0389c379dd6608df486bd801488ccabbd82f832143afa0ac7b92aacf9a943a","abstract_canon_sha256":"2ba97eacb30566a57f4046384307f575e5968d421ee8f4198f8c388fba0a44ae"},"schema_version":"1.0"},"canonical_sha256":"f4fe76df49295a45b01734a668c16d43d9bf5a434ff655f36ecdb8eeaf7659bc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:17:14.349433Z","signature_b64":"U7Z4kjgtocUZmQYvG+iWI8YDKF5Tc3ESSaneTq8yns/olxCEmtQpVU176yjrDOdlxbFKZLYOYj718USGUDWnDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4fe76df49295a45b01734a668c16d43d9bf5a434ff655f36ecdb8eeaf7659bc","last_reissued_at":"2026-05-18T00:17:14.348614Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:17:14.348614Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1804.11188","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":"2026-05-18T00:17:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0tTZNfOSR8FS6unj2NVFECz3HMRq+bjU031TPfkMQG7aJZk3N2asLsfHl7XVMK8nGSdRW7o3BbwDx5myG7B/CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T06:33:33.622875Z"},"content_sha256":"396e3394924d3b011a688fc070aa3ced218e3cf3cda0419cd5e86a0501be78f8","schema_version":"1.0","event_id":"sha256:396e3394924d3b011a688fc070aa3ced218e3cf3cda0419cd5e86a0501be78f8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:6T7HNX2JFFNELMAXGSTGRQLNIP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Can recurrent neural networks warp time?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE","stat.ML"],"primary_cat":"cs.LG","authors_text":"Corentin Tallec, Yann Ollivier","submitted_at":"2018-03-23T09:17:35Z","abstract_excerpt":"Successful recurrent models such as long short-term memories (LSTMs) and gated recurrent units (GRUs) use ad hoc gating mechanisms. Empirically these models have been found to improve the learning of medium to long term temporal dependencies and to help with vanishing gradient issues. We prove that learnable gates in a recurrent model formally provide quasi- invariance to general time transformations in the input data. We recover part of the LSTM architecture from a simple axiomatic approach. This result leads to a new way of initializing gate biases in LSTMs and GRUs. Ex- perimentally, this n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.11188","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":"2026-05-18T00:17:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bFMa0IpkGSys0eqOOKq07hz2gQ5q8bFDsjB8x1Jqe8cCw55tkKwOyfYcc0pY59Dw25psfLXXo1BPcjW3N16ZDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T06:33:33.623475Z"},"content_sha256":"23eb3333798e39629f1a919d970bb81c6afee3de5ef778bb8a2ad3a023e34e75","schema_version":"1.0","event_id":"sha256:23eb3333798e39629f1a919d970bb81c6afee3de5ef778bb8a2ad3a023e34e75"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6T7HNX2JFFNELMAXGSTGRQLNIP/bundle.json","state_url":"https://pith.science/pith/6T7HNX2JFFNELMAXGSTGRQLNIP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6T7HNX2JFFNELMAXGSTGRQLNIP/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":"2026-05-26T06:33:33Z","links":{"resolver":"https://pith.science/pith/6T7HNX2JFFNELMAXGSTGRQLNIP","bundle":"https://pith.science/pith/6T7HNX2JFFNELMAXGSTGRQLNIP/bundle.json","state":"https://pith.science/pith/6T7HNX2JFFNELMAXGSTGRQLNIP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6T7HNX2JFFNELMAXGSTGRQLNIP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:6T7HNX2JFFNELMAXGSTGRQLNIP","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":"2ba97eacb30566a57f4046384307f575e5968d421ee8f4198f8c388fba0a44ae","cross_cats_sorted":["cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-23T09:17:35Z","title_canon_sha256":"ed0389c379dd6608df486bd801488ccabbd82f832143afa0ac7b92aacf9a943a"},"schema_version":"1.0","source":{"id":"1804.11188","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.11188","created_at":"2026-05-18T00:17:14Z"},{"alias_kind":"arxiv_version","alias_value":"1804.11188v1","created_at":"2026-05-18T00:17:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.11188","created_at":"2026-05-18T00:17:14Z"},{"alias_kind":"pith_short_12","alias_value":"6T7HNX2JFFNE","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"6T7HNX2JFFNELMAX","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"6T7HNX2J","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:23eb3333798e39629f1a919d970bb81c6afee3de5ef778bb8a2ad3a023e34e75","target":"graph","created_at":"2026-05-18T00:17:14Z","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":"Successful recurrent models such as long short-term memories (LSTMs) and gated recurrent units (GRUs) use ad hoc gating mechanisms. Empirically these models have been found to improve the learning of medium to long term temporal dependencies and to help with vanishing gradient issues. We prove that learnable gates in a recurrent model formally provide quasi- invariance to general time transformations in the input data. We recover part of the LSTM architecture from a simple axiomatic approach. This result leads to a new way of initializing gate biases in LSTMs and GRUs. Ex- perimentally, this n","authors_text":"Corentin Tallec, Yann Ollivier","cross_cats":["cs.NE","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-23T09:17:35Z","title":"Can recurrent neural networks warp time?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.11188","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":"sha256:396e3394924d3b011a688fc070aa3ced218e3cf3cda0419cd5e86a0501be78f8","target":"record","created_at":"2026-05-18T00:17:14Z","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":"2ba97eacb30566a57f4046384307f575e5968d421ee8f4198f8c388fba0a44ae","cross_cats_sorted":["cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-03-23T09:17:35Z","title_canon_sha256":"ed0389c379dd6608df486bd801488ccabbd82f832143afa0ac7b92aacf9a943a"},"schema_version":"1.0","source":{"id":"1804.11188","kind":"arxiv","version":1}},"canonical_sha256":"f4fe76df49295a45b01734a668c16d43d9bf5a434ff655f36ecdb8eeaf7659bc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f4fe76df49295a45b01734a668c16d43d9bf5a434ff655f36ecdb8eeaf7659bc","first_computed_at":"2026-05-18T00:17:14.348614Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:17:14.348614Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"U7Z4kjgtocUZmQYvG+iWI8YDKF5Tc3ESSaneTq8yns/olxCEmtQpVU176yjrDOdlxbFKZLYOYj718USGUDWnDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:17:14.349433Z","signed_message":"canonical_sha256_bytes"},"source_id":"1804.11188","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:396e3394924d3b011a688fc070aa3ced218e3cf3cda0419cd5e86a0501be78f8","sha256:23eb3333798e39629f1a919d970bb81c6afee3de5ef778bb8a2ad3a023e34e75"],"state_sha256":"64665d6c13c92af7b3d1e0e38dafb78da0c92d3d5429e6e232c3eb3b069305fd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"evlECCdJHGh89ZrD7uV1nLH5IKzhqA4EdNaxQwCEWEBImuVpsQ3lQ5vFK6BPy3ezVSV8J4WC3wYcCtTvctMWDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T06:33:33.626643Z","bundle_sha256":"0eee42b4a3bbf4d4d9128fd403678b6c7cae1d6d6c1fe3ce4f1efa276a4919da"}}