{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:OIY3ATNP4K74Z6SN2IFYA2I6TG","short_pith_number":"pith:OIY3ATNP","canonical_record":{"source":{"id":"2411.00635","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-01T14:46:17Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"9f2d94d0011a5ccc447162dfe115143921bb4515eae2701a209bab2a65082e78","abstract_canon_sha256":"b4eb3900e6a5015eea6a56bdc4100358c5622944156afd462d4e9b7cc5761be4"},"schema_version":"1.0"},"canonical_sha256":"7231b04dafe2bfccfa4dd20b80691e99b3a8703e1ed32241380994faadce9933","source":{"kind":"arxiv","id":"2411.00635","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.00635","created_at":"2026-07-05T11:20:47Z"},{"alias_kind":"arxiv_version","alias_value":"2411.00635v2","created_at":"2026-07-05T11:20:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.00635","created_at":"2026-07-05T11:20:47Z"},{"alias_kind":"pith_short_12","alias_value":"OIY3ATNP4K74","created_at":"2026-07-05T11:20:47Z"},{"alias_kind":"pith_short_16","alias_value":"OIY3ATNP4K74Z6SN","created_at":"2026-07-05T11:20:47Z"},{"alias_kind":"pith_short_8","alias_value":"OIY3ATNP","created_at":"2026-07-05T11:20:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:OIY3ATNP4K74Z6SN2IFYA2I6TG","target":"record","payload":{"canonical_record":{"source":{"id":"2411.00635","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-01T14:46:17Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"9f2d94d0011a5ccc447162dfe115143921bb4515eae2701a209bab2a65082e78","abstract_canon_sha256":"b4eb3900e6a5015eea6a56bdc4100358c5622944156afd462d4e9b7cc5761be4"},"schema_version":"1.0"},"canonical_sha256":"7231b04dafe2bfccfa4dd20b80691e99b3a8703e1ed32241380994faadce9933","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:20:47.765034Z","signature_b64":"/4srGUUlw3ESl5iDcw4jxt9Sr/fY8u0w+a3Npv4RQyVoIrQOBnq1hEqAWRnEzK+fz+h9FunCrkya5QHBEGIaDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7231b04dafe2bfccfa4dd20b80691e99b3a8703e1ed32241380994faadce9933","last_reissued_at":"2026-07-05T11:20:47.764402Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:20:47.764402Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.00635","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":"2026-07-05T11:20:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"icXXexPSLB4FUkFnLhjIa3Zq/34zpKMC60aQLCdQ/GAaIH3prbDTZqTUwyfngi0rG9v5Fd4a6K3qIbb0pgttCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T03:20:45.325102Z"},"content_sha256":"58927d3db2a6990fd56fe0856c63bbb4d82e74708603a809047d437ff4fd58e4","schema_version":"1.0","event_id":"sha256:58927d3db2a6990fd56fe0856c63bbb4d82e74708603a809047d437ff4fd58e4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:OIY3ATNP4K74Z6SN2IFYA2I6TG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Variational Neural Stochastic Differential Equations with Change Points","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Haibei Zhu, Svitlana Vyetrenko, Tucker Balch, Yousef El-Laham, Zhongchang Sun","submitted_at":"2024-11-01T14:46:17Z","abstract_excerpt":"In this work, we explore modeling change points in time-series data using neural stochastic differential equations (neural SDEs). We propose a novel model formulation and training procedure based on the variational autoencoder (VAE) framework for modeling time-series as a neural SDE. Unlike existing algorithms training neural SDEs as VAEs, our proposed algorithm only necessitates a Gaussian prior of the initial state of the latent stochastic process, rather than a Wiener process prior on the entire latent stochastic process. We develop two methodologies for modeling and estimating change point"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.00635","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.00635/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":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":"2026-07-05T11:20:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UMtcM8yAYlGLnrziQ5+LE+CGew58rHB5kr7VOfm4xGsYbK3piHAG7r563cXoSGV5lDUt0JNDScEdrvcPDdRVCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T03:20:45.325481Z"},"content_sha256":"7e47f2e876bc97944a18d2bb05e3209e081e7263413aa472fdefe1fa6171c91d","schema_version":"1.0","event_id":"sha256:7e47f2e876bc97944a18d2bb05e3209e081e7263413aa472fdefe1fa6171c91d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OIY3ATNP4K74Z6SN2IFYA2I6TG/bundle.json","state_url":"https://pith.science/pith/OIY3ATNP4K74Z6SN2IFYA2I6TG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OIY3ATNP4K74Z6SN2IFYA2I6TG/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":"2026-07-07T03:20:45Z","links":{"resolver":"https://pith.science/pith/OIY3ATNP4K74Z6SN2IFYA2I6TG","bundle":"https://pith.science/pith/OIY3ATNP4K74Z6SN2IFYA2I6TG/bundle.json","state":"https://pith.science/pith/OIY3ATNP4K74Z6SN2IFYA2I6TG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OIY3ATNP4K74Z6SN2IFYA2I6TG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:OIY3ATNP4K74Z6SN2IFYA2I6TG","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":"b4eb3900e6a5015eea6a56bdc4100358c5622944156afd462d4e9b7cc5761be4","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-01T14:46:17Z","title_canon_sha256":"9f2d94d0011a5ccc447162dfe115143921bb4515eae2701a209bab2a65082e78"},"schema_version":"1.0","source":{"id":"2411.00635","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.00635","created_at":"2026-07-05T11:20:47Z"},{"alias_kind":"arxiv_version","alias_value":"2411.00635v2","created_at":"2026-07-05T11:20:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.00635","created_at":"2026-07-05T11:20:47Z"},{"alias_kind":"pith_short_12","alias_value":"OIY3ATNP4K74","created_at":"2026-07-05T11:20:47Z"},{"alias_kind":"pith_short_16","alias_value":"OIY3ATNP4K74Z6SN","created_at":"2026-07-05T11:20:47Z"},{"alias_kind":"pith_short_8","alias_value":"OIY3ATNP","created_at":"2026-07-05T11:20:47Z"}],"graph_snapshots":[{"event_id":"sha256:7e47f2e876bc97944a18d2bb05e3209e081e7263413aa472fdefe1fa6171c91d","target":"graph","created_at":"2026-07-05T11:20:47Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2411.00635/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this work, we explore modeling change points in time-series data using neural stochastic differential equations (neural SDEs). We propose a novel model formulation and training procedure based on the variational autoencoder (VAE) framework for modeling time-series as a neural SDE. Unlike existing algorithms training neural SDEs as VAEs, our proposed algorithm only necessitates a Gaussian prior of the initial state of the latent stochastic process, rather than a Wiener process prior on the entire latent stochastic process. We develop two methodologies for modeling and estimating change point","authors_text":"Haibei Zhu, Svitlana Vyetrenko, Tucker Balch, Yousef El-Laham, Zhongchang Sun","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-01T14:46:17Z","title":"Variational Neural Stochastic Differential Equations with Change Points"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.00635","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":"sha256:58927d3db2a6990fd56fe0856c63bbb4d82e74708603a809047d437ff4fd58e4","target":"record","created_at":"2026-07-05T11:20:47Z","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":"b4eb3900e6a5015eea6a56bdc4100358c5622944156afd462d4e9b7cc5761be4","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-11-01T14:46:17Z","title_canon_sha256":"9f2d94d0011a5ccc447162dfe115143921bb4515eae2701a209bab2a65082e78"},"schema_version":"1.0","source":{"id":"2411.00635","kind":"arxiv","version":2}},"canonical_sha256":"7231b04dafe2bfccfa4dd20b80691e99b3a8703e1ed32241380994faadce9933","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7231b04dafe2bfccfa4dd20b80691e99b3a8703e1ed32241380994faadce9933","first_computed_at":"2026-07-05T11:20:47.764402Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:20:47.764402Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/4srGUUlw3ESl5iDcw4jxt9Sr/fY8u0w+a3Npv4RQyVoIrQOBnq1hEqAWRnEzK+fz+h9FunCrkya5QHBEGIaDw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:20:47.765034Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.00635","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:58927d3db2a6990fd56fe0856c63bbb4d82e74708603a809047d437ff4fd58e4","sha256:7e47f2e876bc97944a18d2bb05e3209e081e7263413aa472fdefe1fa6171c91d"],"state_sha256":"34fa655c2d27b3570e55f6b1c21beeefffca5fd5d03f13318a0c4f5d3fabd476"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HqlX6XhQfjkWCv09kSIVP+lqm/kcE9P4o3WHQ3uKRO6CunvmI3gdqF2i9Yd95tyW1I7FHdh9qvZXq/3opapTDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T03:20:45.327366Z","bundle_sha256":"9a6c7288eed92440adc543ea62f0fda883d02d36b57bae01b3afe977ab66d986"}}