{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:FT2X5FKTAPJFDBGN42CCFZ7OJN","short_pith_number":"pith:FT2X5FKT","schema_version":"1.0","canonical_sha256":"2cf57e955303d25184cde68422e7ee4b6c4e7e9235e6db3e5fb864c72702c96d","source":{"kind":"arxiv","id":"1908.09195","version":1},"attestation_state":"computed","paper":{"title":"Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.AP","authors_text":"Felipe A. Medeiros, Samuel I. Berchuck, Sayan Mukherjee","submitted_at":"2019-08-24T20:02:41Z","abstract_excerpt":"As big spatial data becomes increasingly prevalent, classical spatiotemporal (ST) methods often do not scale well. While methods have been developed to account for high-dimensional spatial objects, the setting where there are exceedingly large samples of spatial observations has had less attention. The variational autoencoder (VAE), an unsupervised generative model based on deep learning and approximate Bayesian inference, fills this void using a latent variable specification that is inferred jointly across the large number of samples. In this manuscript, we compare the performance of the VAE "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1908.09195","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2019-08-24T20:02:41Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e8ea4fbb04bc71cf5d4bf3ff6c4f8acac7493806c99a5312b5ebb0441b9e8c4a","abstract_canon_sha256":"592a284b09c0124339861de2db5198e29e911f35ce3e32c85a81ead404d06177"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:59:37.572178Z","signature_b64":"Y7OyF+ALg8DTRdqPI/0FTQPEg6IFFkwU90u4PCztwEbDknBYCmk0iRjSnvbi1XPHVpa2JFPL9RCupI+LlvNFBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2cf57e955303d25184cde68422e7ee4b6c4e7e9235e6db3e5fb864c72702c96d","last_reissued_at":"2026-07-04T23:59:37.571642Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:59:37.571642Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.AP","authors_text":"Felipe A. Medeiros, Samuel I. Berchuck, Sayan Mukherjee","submitted_at":"2019-08-24T20:02:41Z","abstract_excerpt":"As big spatial data becomes increasingly prevalent, classical spatiotemporal (ST) methods often do not scale well. While methods have been developed to account for high-dimensional spatial objects, the setting where there are exceedingly large samples of spatial observations has had less attention. The variational autoencoder (VAE), an unsupervised generative model based on deep learning and approximate Bayesian inference, fills this void using a latent variable specification that is inferred jointly across the large number of samples. In this manuscript, we compare the performance of the VAE "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.09195","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.09195/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1908.09195","created_at":"2026-07-04T23:59:37.571702+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.09195v1","created_at":"2026-07-04T23:59:37.571702+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.09195","created_at":"2026-07-04T23:59:37.571702+00:00"},{"alias_kind":"pith_short_12","alias_value":"FT2X5FKTAPJF","created_at":"2026-07-04T23:59:37.571702+00:00"},{"alias_kind":"pith_short_16","alias_value":"FT2X5FKTAPJFDBGN","created_at":"2026-07-04T23:59:37.571702+00:00"},{"alias_kind":"pith_short_8","alias_value":"FT2X5FKT","created_at":"2026-07-04T23:59:37.571702+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FT2X5FKTAPJFDBGN42CCFZ7OJN","json":"https://pith.science/pith/FT2X5FKTAPJFDBGN42CCFZ7OJN.json","graph_json":"https://pith.science/api/pith-number/FT2X5FKTAPJFDBGN42CCFZ7OJN/graph.json","events_json":"https://pith.science/api/pith-number/FT2X5FKTAPJFDBGN42CCFZ7OJN/events.json","paper":"https://pith.science/paper/FT2X5FKT"},"agent_actions":{"view_html":"https://pith.science/pith/FT2X5FKTAPJFDBGN42CCFZ7OJN","download_json":"https://pith.science/pith/FT2X5FKTAPJFDBGN42CCFZ7OJN.json","view_paper":"https://pith.science/paper/FT2X5FKT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.09195&json=true","fetch_graph":"https://pith.science/api/pith-number/FT2X5FKTAPJFDBGN42CCFZ7OJN/graph.json","fetch_events":"https://pith.science/api/pith-number/FT2X5FKTAPJFDBGN42CCFZ7OJN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FT2X5FKTAPJFDBGN42CCFZ7OJN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FT2X5FKTAPJFDBGN42CCFZ7OJN/action/storage_attestation","attest_author":"https://pith.science/pith/FT2X5FKTAPJFDBGN42CCFZ7OJN/action/author_attestation","sign_citation":"https://pith.science/pith/FT2X5FKTAPJFDBGN42CCFZ7OJN/action/citation_signature","submit_replication":"https://pith.science/pith/FT2X5FKTAPJFDBGN42CCFZ7OJN/action/replication_record"}},"created_at":"2026-07-04T23:59:37.571702+00:00","updated_at":"2026-07-04T23:59:37.571702+00:00"}