{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:2TIRCFXVKWFHU4V52CK5ELFUXF","short_pith_number":"pith:2TIRCFXV","schema_version":"1.0","canonical_sha256":"d4d11116f5558a7a72bdd095d22cb4b9488a91ba8ceadc527376d473ca14c761","source":{"kind":"arxiv","id":"1801.10156","version":1},"attestation_state":"computed","paper":{"title":"Scalable backpropagation for Gaussian Processes using celerite","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Daniel Foreman-Mackey","submitted_at":"2018-01-30T19:00:00Z","abstract_excerpt":"This research note presents a derivation and implementation of efficient and scalable gradient computations using the celerite algorithm for Gaussian Process (GP) modeling. The algorithms are derived in a \"reverse accumulation\" or \"backpropagation\" framework and they can be easily integrated into existing automatic differentiation frameworks to provide a scalable method for evaluating the gradients of the GP likelihood with respect to all input parameters. The algorithm derived in this note uses less memory and is more efficient than versions using automatic differentiation and the computation"},"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":"1801.10156","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.IM","submitted_at":"2018-01-30T19:00:00Z","cross_cats_sorted":[],"title_canon_sha256":"158bcded055619e03b0776c41787f20a31e6fe693a468aca645e49de33d11e88","abstract_canon_sha256":"7c1b7bc43a8c534a22e0da7cff7c10a848ae1cffa4ffb0e608fc1526e92ca2d1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:41.546034Z","signature_b64":"BMuHnxMkITaM217JeLbZomLbUFRsriXefBXgjdaDCkEwjM35qElJN5iStYr5bW2SyuvFUN3gwiDEhwiNsNwqDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d4d11116f5558a7a72bdd095d22cb4b9488a91ba8ceadc527376d473ca14c761","last_reissued_at":"2026-05-18T00:24:41.545472Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:41.545472Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scalable backpropagation for Gaussian Processes using celerite","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"astro-ph.IM","authors_text":"Daniel Foreman-Mackey","submitted_at":"2018-01-30T19:00:00Z","abstract_excerpt":"This research note presents a derivation and implementation of efficient and scalable gradient computations using the celerite algorithm for Gaussian Process (GP) modeling. The algorithms are derived in a \"reverse accumulation\" or \"backpropagation\" framework and they can be easily integrated into existing automatic differentiation frameworks to provide a scalable method for evaluating the gradients of the GP likelihood with respect to all input parameters. The algorithm derived in this note uses less memory and is more efficient than versions using automatic differentiation and the computation"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.10156","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1801.10156","created_at":"2026-05-18T00:24:41.545550+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.10156v1","created_at":"2026-05-18T00:24:41.545550+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.10156","created_at":"2026-05-18T00:24:41.545550+00:00"},{"alias_kind":"pith_short_12","alias_value":"2TIRCFXVKWFH","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"2TIRCFXVKWFHU4V5","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"2TIRCFXV","created_at":"2026-05-18T12:32:02.567920+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2606.04875","citing_title":"A Model Selection Criterion for Multidimensional Gaussian Processes: Application to Radial Velocities","ref_index":207,"is_internal_anchor":true},{"citing_arxiv_id":"2606.05842","citing_title":"The X-ray emission of the long-period transient and accreting cataclysmic variable ASKAP J174508.9-505149","ref_index":276,"is_internal_anchor":true},{"citing_arxiv_id":"2606.06868","citing_title":"The mass of TOI-1883 b: A low density super-Neptune in the ridge regime transiting an early-M dwarf","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2TIRCFXVKWFHU4V52CK5ELFUXF","json":"https://pith.science/pith/2TIRCFXVKWFHU4V52CK5ELFUXF.json","graph_json":"https://pith.science/api/pith-number/2TIRCFXVKWFHU4V52CK5ELFUXF/graph.json","events_json":"https://pith.science/api/pith-number/2TIRCFXVKWFHU4V52CK5ELFUXF/events.json","paper":"https://pith.science/paper/2TIRCFXV"},"agent_actions":{"view_html":"https://pith.science/pith/2TIRCFXVKWFHU4V52CK5ELFUXF","download_json":"https://pith.science/pith/2TIRCFXVKWFHU4V52CK5ELFUXF.json","view_paper":"https://pith.science/paper/2TIRCFXV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.10156&json=true","fetch_graph":"https://pith.science/api/pith-number/2TIRCFXVKWFHU4V52CK5ELFUXF/graph.json","fetch_events":"https://pith.science/api/pith-number/2TIRCFXVKWFHU4V52CK5ELFUXF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2TIRCFXVKWFHU4V52CK5ELFUXF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2TIRCFXVKWFHU4V52CK5ELFUXF/action/storage_attestation","attest_author":"https://pith.science/pith/2TIRCFXVKWFHU4V52CK5ELFUXF/action/author_attestation","sign_citation":"https://pith.science/pith/2TIRCFXVKWFHU4V52CK5ELFUXF/action/citation_signature","submit_replication":"https://pith.science/pith/2TIRCFXVKWFHU4V52CK5ELFUXF/action/replication_record"}},"created_at":"2026-05-18T00:24:41.545550+00:00","updated_at":"2026-05-18T00:24:41.545550+00:00"}