{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:X5GBKRC6MBDG7JD7CBL3C7PT5W","short_pith_number":"pith:X5GBKRC6","schema_version":"1.0","canonical_sha256":"bf4c15445e60466fa47f1057b17df3edb4b18f4accbf56911f0ba2459e959cd3","source":{"kind":"arxiv","id":"2304.05091","version":1},"attestation_state":"computed","paper":{"title":"Actually Sparse Variational Gaussian Processes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Daniel Augusto de Souza, Harry Jake Cunningham, Marc Peter Deisenroth, Mark van der Wilk, So Takao","submitted_at":"2023-04-11T09:38:58Z","abstract_excerpt":"Gaussian processes (GPs) are typically criticised for their unfavourable scaling in both computational and memory requirements. For large datasets, sparse GPs reduce these demands by conditioning on a small set of inducing variables designed to summarise the data. In practice however, for large datasets requiring many inducing variables, such as low-lengthscale spatial data, even sparse GPs can become computationally expensive, limited by the number of inducing variables one can use. In this work, we propose a new class of inter-domain variational GP, constructed by projecting a GP onto a set "},"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":"2304.05091","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-04-11T09:38:58Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"577d8edff8f4b0f69fe7a4ea6b063163229606618ac938818bfe59732317c331","abstract_canon_sha256":"312a1953f1b51c3f33200002e9119cd62a33d5c79938c35fb0ab13ad4099d05f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:59:55.171110Z","signature_b64":"WsHk48FAHPkZ5ijH4Eamr70lmxR0Oi1zHp1NJ3KxCMn99dH5sSJa5QmVNn/SH0QharlMvuuaE/U3Njv0PWrlBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bf4c15445e60466fa47f1057b17df3edb4b18f4accbf56911f0ba2459e959cd3","last_reissued_at":"2026-07-05T05:59:55.170771Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:59:55.170771Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Actually Sparse Variational Gaussian Processes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Daniel Augusto de Souza, Harry Jake Cunningham, Marc Peter Deisenroth, Mark van der Wilk, So Takao","submitted_at":"2023-04-11T09:38:58Z","abstract_excerpt":"Gaussian processes (GPs) are typically criticised for their unfavourable scaling in both computational and memory requirements. For large datasets, sparse GPs reduce these demands by conditioning on a small set of inducing variables designed to summarise the data. In practice however, for large datasets requiring many inducing variables, such as low-lengthscale spatial data, even sparse GPs can become computationally expensive, limited by the number of inducing variables one can use. In this work, we propose a new class of inter-domain variational GP, constructed by projecting a GP onto a set "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.05091","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/2304.05091/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":"2304.05091","created_at":"2026-07-05T05:59:55.170832+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.05091v1","created_at":"2026-07-05T05:59:55.170832+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.05091","created_at":"2026-07-05T05:59:55.170832+00:00"},{"alias_kind":"pith_short_12","alias_value":"X5GBKRC6MBDG","created_at":"2026-07-05T05:59:55.170832+00:00"},{"alias_kind":"pith_short_16","alias_value":"X5GBKRC6MBDG7JD7","created_at":"2026-07-05T05:59:55.170832+00:00"},{"alias_kind":"pith_short_8","alias_value":"X5GBKRC6","created_at":"2026-07-05T05:59:55.170832+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/X5GBKRC6MBDG7JD7CBL3C7PT5W","json":"https://pith.science/pith/X5GBKRC6MBDG7JD7CBL3C7PT5W.json","graph_json":"https://pith.science/api/pith-number/X5GBKRC6MBDG7JD7CBL3C7PT5W/graph.json","events_json":"https://pith.science/api/pith-number/X5GBKRC6MBDG7JD7CBL3C7PT5W/events.json","paper":"https://pith.science/paper/X5GBKRC6"},"agent_actions":{"view_html":"https://pith.science/pith/X5GBKRC6MBDG7JD7CBL3C7PT5W","download_json":"https://pith.science/pith/X5GBKRC6MBDG7JD7CBL3C7PT5W.json","view_paper":"https://pith.science/paper/X5GBKRC6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.05091&json=true","fetch_graph":"https://pith.science/api/pith-number/X5GBKRC6MBDG7JD7CBL3C7PT5W/graph.json","fetch_events":"https://pith.science/api/pith-number/X5GBKRC6MBDG7JD7CBL3C7PT5W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X5GBKRC6MBDG7JD7CBL3C7PT5W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X5GBKRC6MBDG7JD7CBL3C7PT5W/action/storage_attestation","attest_author":"https://pith.science/pith/X5GBKRC6MBDG7JD7CBL3C7PT5W/action/author_attestation","sign_citation":"https://pith.science/pith/X5GBKRC6MBDG7JD7CBL3C7PT5W/action/citation_signature","submit_replication":"https://pith.science/pith/X5GBKRC6MBDG7JD7CBL3C7PT5W/action/replication_record"}},"created_at":"2026-07-05T05:59:55.170832+00:00","updated_at":"2026-07-05T05:59:55.170832+00:00"}