{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:LQXKIRWYCSB4I2DJCRCMSKNFN7","short_pith_number":"pith:LQXKIRWY","canonical_record":{"source":{"id":"1810.03023","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-06T16:55:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7732f55877302ce3886799193219fcdd495a40a13082646620458a7935d20ab1","abstract_canon_sha256":"79afe07743532197668022f39f0584be85fd261af0556ec6bf84862a5826bb2d"},"schema_version":"1.0"},"canonical_sha256":"5c2ea446d81483c468691444c929a56fe46ae3dac52bf8adc4135ae4d67b149e","source":{"kind":"arxiv","id":"1810.03023","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.03023","created_at":"2026-05-17T23:56:40Z"},{"alias_kind":"arxiv_version","alias_value":"1810.03023v2","created_at":"2026-05-17T23:56:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.03023","created_at":"2026-05-17T23:56:40Z"},{"alias_kind":"pith_short_12","alias_value":"LQXKIRWYCSB4","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LQXKIRWYCSB4I2DJ","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LQXKIRWY","created_at":"2026-05-18T12:32:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:LQXKIRWYCSB4I2DJCRCMSKNFN7","target":"record","payload":{"canonical_record":{"source":{"id":"1810.03023","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-06T16:55:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7732f55877302ce3886799193219fcdd495a40a13082646620458a7935d20ab1","abstract_canon_sha256":"79afe07743532197668022f39f0584be85fd261af0556ec6bf84862a5826bb2d"},"schema_version":"1.0"},"canonical_sha256":"5c2ea446d81483c468691444c929a56fe46ae3dac52bf8adc4135ae4d67b149e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:40.950306Z","signature_b64":"cs0krWJLJorFBeaPl2GwxVepMUGxFjXCpbQqD1ADHa3IggnxXKfQxMR+F/x8Iwxo1CFTKqXVkkXFgeKXY2CCAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5c2ea446d81483c468691444c929a56fe46ae3dac52bf8adc4135ae4d67b149e","last_reissued_at":"2026-05-17T23:56:40.949678Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:40.949678Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.03023","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:56:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DFHo2iqWhtHut5BBumdtc37LrNNefc3y8RNRHGdQQGZdoAs1Em628wnpzenFepXx7g79hfmYfeW+hS/h+y+PAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T12:36:04.332407Z"},"content_sha256":"756ff43f66f12a3f3ccdc941896e673816d1d9c9bd3a74412ce449b5cc8d8696","schema_version":"1.0","event_id":"sha256:756ff43f66f12a3f3ccdc941896e673816d1d9c9bd3a74412ce449b5cc8d8696"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:LQXKIRWYCSB4I2DJCRCMSKNFN7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"h-detach: Modifying the LSTM Gradient Towards Better Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Bhargav Kanuparthi, Devansh Arpit, Giancarlo Kerg, Ioannis Mitliagkas, Nan Rosemary Ke, Yoshua Bengio","submitted_at":"2018-10-06T16:55:46Z","abstract_excerpt":"Recurrent neural networks are known for their notorious exploding and vanishing gradient problem (EVGP). This problem becomes more evident in tasks where the information needed to correctly solve them exist over long time scales, because EVGP prevents important gradient components from being back-propagated adequately over a large number of steps. We introduce a simple stochastic algorithm (\\textit{h}-detach) that is specific to LSTM optimization and targeted towards addressing this problem. Specifically, we show that when the LSTM weights are large, the gradient components through the linear "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.03023","kind":"arxiv","version":2},"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":"2026-05-17T23:56:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ErtHCTXKgJ6cRJgSaPlxUPY4WYUp+suEnevWrV3x7P/PB5dtazY9zNBf5iC+wcIRf43ZChsizehkuDy/poHjCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T12:36:04.332753Z"},"content_sha256":"fb784950bbb480e497c1902e5cf5055fb16de20c36c610e9b824f900ed088b27","schema_version":"1.0","event_id":"sha256:fb784950bbb480e497c1902e5cf5055fb16de20c36c610e9b824f900ed088b27"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LQXKIRWYCSB4I2DJCRCMSKNFN7/bundle.json","state_url":"https://pith.science/pith/LQXKIRWYCSB4I2DJCRCMSKNFN7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LQXKIRWYCSB4I2DJCRCMSKNFN7/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":"2026-07-10T12:36:04Z","links":{"resolver":"https://pith.science/pith/LQXKIRWYCSB4I2DJCRCMSKNFN7","bundle":"https://pith.science/pith/LQXKIRWYCSB4I2DJCRCMSKNFN7/bundle.json","state":"https://pith.science/pith/LQXKIRWYCSB4I2DJCRCMSKNFN7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LQXKIRWYCSB4I2DJCRCMSKNFN7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:LQXKIRWYCSB4I2DJCRCMSKNFN7","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":"79afe07743532197668022f39f0584be85fd261af0556ec6bf84862a5826bb2d","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-06T16:55:46Z","title_canon_sha256":"7732f55877302ce3886799193219fcdd495a40a13082646620458a7935d20ab1"},"schema_version":"1.0","source":{"id":"1810.03023","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.03023","created_at":"2026-05-17T23:56:40Z"},{"alias_kind":"arxiv_version","alias_value":"1810.03023v2","created_at":"2026-05-17T23:56:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.03023","created_at":"2026-05-17T23:56:40Z"},{"alias_kind":"pith_short_12","alias_value":"LQXKIRWYCSB4","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LQXKIRWYCSB4I2DJ","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LQXKIRWY","created_at":"2026-05-18T12:32:37Z"}],"graph_snapshots":[{"event_id":"sha256:fb784950bbb480e497c1902e5cf5055fb16de20c36c610e9b824f900ed088b27","target":"graph","created_at":"2026-05-17T23:56:40Z","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":"Recurrent neural networks are known for their notorious exploding and vanishing gradient problem (EVGP). This problem becomes more evident in tasks where the information needed to correctly solve them exist over long time scales, because EVGP prevents important gradient components from being back-propagated adequately over a large number of steps. We introduce a simple stochastic algorithm (\\textit{h}-detach) that is specific to LSTM optimization and targeted towards addressing this problem. Specifically, we show that when the LSTM weights are large, the gradient components through the linear ","authors_text":"Bhargav Kanuparthi, Devansh Arpit, Giancarlo Kerg, Ioannis Mitliagkas, Nan Rosemary Ke, Yoshua Bengio","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-06T16:55:46Z","title":"h-detach: Modifying the LSTM Gradient Towards Better Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.03023","kind":"arxiv","version":2},"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":"sha256:756ff43f66f12a3f3ccdc941896e673816d1d9c9bd3a74412ce449b5cc8d8696","target":"record","created_at":"2026-05-17T23:56:40Z","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":"79afe07743532197668022f39f0584be85fd261af0556ec6bf84862a5826bb2d","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-06T16:55:46Z","title_canon_sha256":"7732f55877302ce3886799193219fcdd495a40a13082646620458a7935d20ab1"},"schema_version":"1.0","source":{"id":"1810.03023","kind":"arxiv","version":2}},"canonical_sha256":"5c2ea446d81483c468691444c929a56fe46ae3dac52bf8adc4135ae4d67b149e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5c2ea446d81483c468691444c929a56fe46ae3dac52bf8adc4135ae4d67b149e","first_computed_at":"2026-05-17T23:56:40.949678Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:56:40.949678Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cs0krWJLJorFBeaPl2GwxVepMUGxFjXCpbQqD1ADHa3IggnxXKfQxMR+F/x8Iwxo1CFTKqXVkkXFgeKXY2CCAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:56:40.950306Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.03023","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:756ff43f66f12a3f3ccdc941896e673816d1d9c9bd3a74412ce449b5cc8d8696","sha256:fb784950bbb480e497c1902e5cf5055fb16de20c36c610e9b824f900ed088b27"],"state_sha256":"28948ae4316eadb005f885e53d97030d22f6394de7651aaeb2b13bb8077eff2d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KRTSfEzquPlzr4Nv+g8iPF6II0sgKO6UbveC9woX2eT1vWXclr8jul/y3pHMWmWlAG9yq0kkLuEEiM5VakFjDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-10T12:36:04.334734Z","bundle_sha256":"e6cf1ee635152789381f6c1424a05a54abb40b90059d74ae076a7cc9b0b9925b"}}