{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:GGNQFF7JMS2I2FECLAYMAPVMI5","short_pith_number":"pith:GGNQFF7J","canonical_record":{"source":{"id":"1808.09522","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-28T20:13:18Z","cross_cats_sorted":[],"title_canon_sha256":"ccf550e736e9787a73725a0638be40c2e4be620b240e5ce18e694f8e45004710","abstract_canon_sha256":"f472d2a0be97561e6b6aae291e184dc2f7c9e4cad12e6f91f95a53a9fc67cd13"},"schema_version":"1.0"},"canonical_sha256":"319b0297e964b48d14825830c03eac474ce259b1bca1de5f1505907e61ad121e","source":{"kind":"arxiv","id":"1808.09522","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.09522","created_at":"2026-05-18T00:06:55Z"},{"alias_kind":"arxiv_version","alias_value":"1808.09522v1","created_at":"2026-05-18T00:06:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.09522","created_at":"2026-05-18T00:06:55Z"},{"alias_kind":"pith_short_12","alias_value":"GGNQFF7JMS2I","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"GGNQFF7JMS2I2FEC","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"GGNQFF7J","created_at":"2026-05-18T12:32:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:GGNQFF7JMS2I2FECLAYMAPVMI5","target":"record","payload":{"canonical_record":{"source":{"id":"1808.09522","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-28T20:13:18Z","cross_cats_sorted":[],"title_canon_sha256":"ccf550e736e9787a73725a0638be40c2e4be620b240e5ce18e694f8e45004710","abstract_canon_sha256":"f472d2a0be97561e6b6aae291e184dc2f7c9e4cad12e6f91f95a53a9fc67cd13"},"schema_version":"1.0"},"canonical_sha256":"319b0297e964b48d14825830c03eac474ce259b1bca1de5f1505907e61ad121e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:55.304718Z","signature_b64":"shY0VLjScmIIPIREFvoo0uL6cRLi03UsCwmodev4sSOA2QzNYKYET2mZPFC6466i448CyenJUgQuccHvl20mBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"319b0297e964b48d14825830c03eac474ce259b1bca1de5f1505907e61ad121e","last_reissued_at":"2026-05-18T00:06:55.304029Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:55.304029Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.09522","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":"2026-05-18T00:06:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R7I03ZIjcFPhLtkrVp9iaY26Tt2IfVbIjUqh6pWRe9CKoucE97It6hGMi/8bqsG5EMrUNGBLXGXzAPkgYpDvDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T07:18:43.496725Z"},"content_sha256":"51ebd4cb7dbc048afda493697e8d3f6f3b410f57a4121df133b8bc5faa63da93","schema_version":"1.0","event_id":"sha256:51ebd4cb7dbc048afda493697e8d3f6f3b410f57a4121df133b8bc5faa63da93"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:GGNQFF7JMS2I2FECLAYMAPVMI5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Layer Trajectory LSTM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Changliang Liu, Jinyu Li, Yifan Gong","submitted_at":"2018-08-28T20:13:18Z","abstract_excerpt":"It is popular to stack LSTM layers to get better modeling power, especially when large amount of training data is available. However, an LSTM-RNN with too many vanilla LSTM layers is very hard to train and there still exists the gradient vanishing issue if the network goes too deep. This issue can be partially solved by adding skip connections between layers, such as residual LSTM. In this paper, we propose a layer trajectory LSTM (ltLSTM) which builds a layer-LSTM using all the layer outputs from a standard multi-layer time-LSTM. This layer-LSTM scans the outputs from time-LSTMs, and uses the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.09522","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":"2026-05-18T00:06:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OV4km0M9p+zc5H8SwLMvzkczvm5/NQh20Ge5+/GR3e2fnujSVylcIyk09qpjCzok2huN/vlmcJPv3sd46JebDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T07:18:43.497588Z"},"content_sha256":"c665d6e126c2551fb4df0466fe20ff7a21aa3ac5924c4a436e6df9b20e63ba55","schema_version":"1.0","event_id":"sha256:c665d6e126c2551fb4df0466fe20ff7a21aa3ac5924c4a436e6df9b20e63ba55"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GGNQFF7JMS2I2FECLAYMAPVMI5/bundle.json","state_url":"https://pith.science/pith/GGNQFF7JMS2I2FECLAYMAPVMI5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GGNQFF7JMS2I2FECLAYMAPVMI5/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":"2026-05-30T07:18:43Z","links":{"resolver":"https://pith.science/pith/GGNQFF7JMS2I2FECLAYMAPVMI5","bundle":"https://pith.science/pith/GGNQFF7JMS2I2FECLAYMAPVMI5/bundle.json","state":"https://pith.science/pith/GGNQFF7JMS2I2FECLAYMAPVMI5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GGNQFF7JMS2I2FECLAYMAPVMI5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:GGNQFF7JMS2I2FECLAYMAPVMI5","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":"f472d2a0be97561e6b6aae291e184dc2f7c9e4cad12e6f91f95a53a9fc67cd13","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-28T20:13:18Z","title_canon_sha256":"ccf550e736e9787a73725a0638be40c2e4be620b240e5ce18e694f8e45004710"},"schema_version":"1.0","source":{"id":"1808.09522","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.09522","created_at":"2026-05-18T00:06:55Z"},{"alias_kind":"arxiv_version","alias_value":"1808.09522v1","created_at":"2026-05-18T00:06:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.09522","created_at":"2026-05-18T00:06:55Z"},{"alias_kind":"pith_short_12","alias_value":"GGNQFF7JMS2I","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"GGNQFF7JMS2I2FEC","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"GGNQFF7J","created_at":"2026-05-18T12:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:c665d6e126c2551fb4df0466fe20ff7a21aa3ac5924c4a436e6df9b20e63ba55","target":"graph","created_at":"2026-05-18T00:06:55Z","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":"It is popular to stack LSTM layers to get better modeling power, especially when large amount of training data is available. However, an LSTM-RNN with too many vanilla LSTM layers is very hard to train and there still exists the gradient vanishing issue if the network goes too deep. This issue can be partially solved by adding skip connections between layers, such as residual LSTM. In this paper, we propose a layer trajectory LSTM (ltLSTM) which builds a layer-LSTM using all the layer outputs from a standard multi-layer time-LSTM. This layer-LSTM scans the outputs from time-LSTMs, and uses the","authors_text":"Changliang Liu, Jinyu Li, Yifan Gong","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-28T20:13:18Z","title":"Layer Trajectory LSTM"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.09522","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":"sha256:51ebd4cb7dbc048afda493697e8d3f6f3b410f57a4121df133b8bc5faa63da93","target":"record","created_at":"2026-05-18T00:06:55Z","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":"f472d2a0be97561e6b6aae291e184dc2f7c9e4cad12e6f91f95a53a9fc67cd13","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-28T20:13:18Z","title_canon_sha256":"ccf550e736e9787a73725a0638be40c2e4be620b240e5ce18e694f8e45004710"},"schema_version":"1.0","source":{"id":"1808.09522","kind":"arxiv","version":1}},"canonical_sha256":"319b0297e964b48d14825830c03eac474ce259b1bca1de5f1505907e61ad121e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"319b0297e964b48d14825830c03eac474ce259b1bca1de5f1505907e61ad121e","first_computed_at":"2026-05-18T00:06:55.304029Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:06:55.304029Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"shY0VLjScmIIPIREFvoo0uL6cRLi03UsCwmodev4sSOA2QzNYKYET2mZPFC6466i448CyenJUgQuccHvl20mBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:06:55.304718Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.09522","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:51ebd4cb7dbc048afda493697e8d3f6f3b410f57a4121df133b8bc5faa63da93","sha256:c665d6e126c2551fb4df0466fe20ff7a21aa3ac5924c4a436e6df9b20e63ba55"],"state_sha256":"ceaa3e97e88c05d5b75f3095f5ea08d29343dd23385b6b34b16293e1b5d31fec"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ppknEnC2Tf+f1/953VvbrnyspGlv89KL7IyPv1k3zAqm2z3qm8YvifH8DtJ1l5sQruqUO4sPASGG9/kRm58pAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T07:18:43.501997Z","bundle_sha256":"ae0aabc80a8c7dd620028edb5d71a88875f9631293c248d5592f0967f31aaf3b"}}