{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:SZRPR4CDBOIU26VRMFGQS56DSU","short_pith_number":"pith:SZRPR4CD","canonical_record":{"source":{"id":"1808.08097","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-24T11:36:15Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"6d22549e951860136b3ed85f8890c8c622752d8d015052945bd7fe8a9af6d352","abstract_canon_sha256":"fe2213cbfa09f6a0eab108e76f4a8d0fef19158e85c70c8553bb33ba8941c331"},"schema_version":"1.0"},"canonical_sha256":"9662f8f0430b914d7ab1614d0977c3953df4aa80543cafd8d7f77f24c0d1044b","source":{"kind":"arxiv","id":"1808.08097","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.08097","created_at":"2026-05-18T00:07:22Z"},{"alias_kind":"arxiv_version","alias_value":"1808.08097v1","created_at":"2026-05-18T00:07:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.08097","created_at":"2026-05-18T00:07:22Z"},{"alias_kind":"pith_short_12","alias_value":"SZRPR4CDBOIU","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"SZRPR4CDBOIU26VR","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"SZRPR4CD","created_at":"2026-05-18T12:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:SZRPR4CDBOIU26VRMFGQS56DSU","target":"record","payload":{"canonical_record":{"source":{"id":"1808.08097","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-24T11:36:15Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"6d22549e951860136b3ed85f8890c8c622752d8d015052945bd7fe8a9af6d352","abstract_canon_sha256":"fe2213cbfa09f6a0eab108e76f4a8d0fef19158e85c70c8553bb33ba8941c331"},"schema_version":"1.0"},"canonical_sha256":"9662f8f0430b914d7ab1614d0977c3953df4aa80543cafd8d7f77f24c0d1044b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:22.439885Z","signature_b64":"NslJeyh5AQCkohTiEew9lOYgIyodMHPW7ua7cMoj+U0c+MtVJebk0F8iM00O3Aqc5DEuhxv2dMh8s8D9Id81Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9662f8f0430b914d7ab1614d0977c3953df4aa80543cafd8d7f77f24c0d1044b","last_reissued_at":"2026-05-18T00:07:22.439273Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:22.439273Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.08097","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":"2026-05-18T00:07:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7modIjMY20DsYVwXi9AP+WRGC6EjgmU2O0TBMB1wP8hovltXL+PjSt4+GSmOzACd8ji2O5Ep9sxtPyOSlP9JAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T13:55:42.691140Z"},"content_sha256":"e606432b97d3e151732a693569993744b0ba2a4649c5e046e8b03a9d70476c6e","schema_version":"1.0","event_id":"sha256:e606432b97d3e151732a693569993744b0ba2a4649c5e046e8b03a9d70476c6e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:SZRPR4CDBOIU26VRMFGQS56DSU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Memory Time Span in LSTMs for Multi-Speaker Source Separation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Hugo Van hamme, Jeroen Zegers","submitted_at":"2018-08-24T11:36:15Z","abstract_excerpt":"With deep learning approaches becoming state-of-the-art in many speech (as well as non-speech) related machine learning tasks, efforts are being taken to delve into the neural networks which are often considered as a black box. In this paper it is analyzed how recurrent neural network (RNNs) cope with temporal dependencies by determining the relevant memory time span in a long short-term memory (LSTM) cell. This is done by leaking the state variable with a controlled lifetime and evaluating the task performance. This technique can be used for any task to estimate the time span the LSTM exploit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.08097","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":"2026-05-18T00:07:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9Bvd3ULsh88R5QfqCFzrR+16w0wUV1GImYH8Uae6ZQC1OA12x5zP6nysz3bgmqBzOFJ1TfczAeC0k8Bsi/LjCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T13:55:42.691498Z"},"content_sha256":"684373d396c2bba6e804391c1a66d54d9d7d7ce6b82187769ccb4f1cddc09441","schema_version":"1.0","event_id":"sha256:684373d396c2bba6e804391c1a66d54d9d7d7ce6b82187769ccb4f1cddc09441"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SZRPR4CDBOIU26VRMFGQS56DSU/bundle.json","state_url":"https://pith.science/pith/SZRPR4CDBOIU26VRMFGQS56DSU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SZRPR4CDBOIU26VRMFGQS56DSU/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":"2026-07-08T13:55:42Z","links":{"resolver":"https://pith.science/pith/SZRPR4CDBOIU26VRMFGQS56DSU","bundle":"https://pith.science/pith/SZRPR4CDBOIU26VRMFGQS56DSU/bundle.json","state":"https://pith.science/pith/SZRPR4CDBOIU26VRMFGQS56DSU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SZRPR4CDBOIU26VRMFGQS56DSU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:SZRPR4CDBOIU26VRMFGQS56DSU","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":"fe2213cbfa09f6a0eab108e76f4a8d0fef19158e85c70c8553bb33ba8941c331","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-24T11:36:15Z","title_canon_sha256":"6d22549e951860136b3ed85f8890c8c622752d8d015052945bd7fe8a9af6d352"},"schema_version":"1.0","source":{"id":"1808.08097","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.08097","created_at":"2026-05-18T00:07:22Z"},{"alias_kind":"arxiv_version","alias_value":"1808.08097v1","created_at":"2026-05-18T00:07:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.08097","created_at":"2026-05-18T00:07:22Z"},{"alias_kind":"pith_short_12","alias_value":"SZRPR4CDBOIU","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"SZRPR4CDBOIU26VR","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"SZRPR4CD","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:684373d396c2bba6e804391c1a66d54d9d7d7ce6b82187769ccb4f1cddc09441","target":"graph","created_at":"2026-05-18T00:07:22Z","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":"With deep learning approaches becoming state-of-the-art in many speech (as well as non-speech) related machine learning tasks, efforts are being taken to delve into the neural networks which are often considered as a black box. In this paper it is analyzed how recurrent neural network (RNNs) cope with temporal dependencies by determining the relevant memory time span in a long short-term memory (LSTM) cell. This is done by leaking the state variable with a controlled lifetime and evaluating the task performance. This technique can be used for any task to estimate the time span the LSTM exploit","authors_text":"Hugo Van hamme, Jeroen Zegers","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-24T11:36:15Z","title":"Memory Time Span in LSTMs for Multi-Speaker Source Separation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.08097","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:e606432b97d3e151732a693569993744b0ba2a4649c5e046e8b03a9d70476c6e","target":"record","created_at":"2026-05-18T00:07:22Z","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":"fe2213cbfa09f6a0eab108e76f4a8d0fef19158e85c70c8553bb33ba8941c331","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-24T11:36:15Z","title_canon_sha256":"6d22549e951860136b3ed85f8890c8c622752d8d015052945bd7fe8a9af6d352"},"schema_version":"1.0","source":{"id":"1808.08097","kind":"arxiv","version":1}},"canonical_sha256":"9662f8f0430b914d7ab1614d0977c3953df4aa80543cafd8d7f77f24c0d1044b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9662f8f0430b914d7ab1614d0977c3953df4aa80543cafd8d7f77f24c0d1044b","first_computed_at":"2026-05-18T00:07:22.439273Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:07:22.439273Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NslJeyh5AQCkohTiEew9lOYgIyodMHPW7ua7cMoj+U0c+MtVJebk0F8iM00O3Aqc5DEuhxv2dMh8s8D9Id81Cw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:07:22.439885Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.08097","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e606432b97d3e151732a693569993744b0ba2a4649c5e046e8b03a9d70476c6e","sha256:684373d396c2bba6e804391c1a66d54d9d7d7ce6b82187769ccb4f1cddc09441"],"state_sha256":"cffbf82d29c8271a3b1c6311954121b897ed286e6222bc37e942f82227c3cfde"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lTf01lKV6dhJDC2qLBT6MdQyMOotr4wMgIs4pPXoiD9pRegVXbriEIJ6k2sW6Ir66YsktDjlHyV4IxJ6knOGCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-08T13:55:42.693479Z","bundle_sha256":"63b340d36c71b44e1e0be5c30eb1164d801bb07303aad259110b33d38438a49f"}}