{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:IPWEABLRE63K3UFGRKCHZ5ETRR","short_pith_number":"pith:IPWEABLR","canonical_record":{"source":{"id":"2010.10177","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-10-20T10:19:56Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"3acfec2ff9b1d211e874fd009d09ececdc59ff36bb6ce47e8ef633527175a4a2","abstract_canon_sha256":"fe3d5139db0884b1a4ca367d7abf2b76ae62d8668e3fa1ee41d236abcfd8fd85"},"schema_version":"1.0"},"canonical_sha256":"43ec40057127b6add0a68a847cf4938c7f240aeedc40c7917559e67241d5f5d8","source":{"kind":"arxiv","id":"2010.10177","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.10177","created_at":"2026-07-05T01:45:31Z"},{"alias_kind":"arxiv_version","alias_value":"2010.10177v2","created_at":"2026-07-05T01:45:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.10177","created_at":"2026-07-05T01:45:31Z"},{"alias_kind":"pith_short_12","alias_value":"IPWEABLRE63K","created_at":"2026-07-05T01:45:31Z"},{"alias_kind":"pith_short_16","alias_value":"IPWEABLRE63K3UFG","created_at":"2026-07-05T01:45:31Z"},{"alias_kind":"pith_short_8","alias_value":"IPWEABLR","created_at":"2026-07-05T01:45:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:IPWEABLRE63K3UFGRKCHZ5ETRR","target":"record","payload":{"canonical_record":{"source":{"id":"2010.10177","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-10-20T10:19:56Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"3acfec2ff9b1d211e874fd009d09ececdc59ff36bb6ce47e8ef633527175a4a2","abstract_canon_sha256":"fe3d5139db0884b1a4ca367d7abf2b76ae62d8668e3fa1ee41d236abcfd8fd85"},"schema_version":"1.0"},"canonical_sha256":"43ec40057127b6add0a68a847cf4938c7f240aeedc40c7917559e67241d5f5d8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:45:31.545960Z","signature_b64":"rVGfEnaet43azpvdM+qAuxVCuKx+Ip6zluS7hrJkki2vEh8BArEvMqVsRFH/k/aqQc67h0bk1DYFhGIIX405Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"43ec40057127b6add0a68a847cf4938c7f240aeedc40c7917559e67241d5f5d8","last_reissued_at":"2026-07-05T01:45:31.545450Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:45:31.545450Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2010.10177","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":"2026-07-05T01:45:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LzpWuANtPShqR9stG0AcHAyqgufzWqKpGdUk+9ZXyJoVqJqV73PMSRQmS16DpbCZ+30X2Zba6z220v+wLcGMDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T10:41:00.457952Z"},"content_sha256":"d1ee8a37be4257afca7c19d11f3291578ae6e4540f97af92fbe3de1a87920bbc","schema_version":"1.0","event_id":"sha256:d1ee8a37be4257afca7c19d11f3291578ae6e4540f97af92fbe3de1a87920bbc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:IPWEABLRE63K3UFGRKCHZ5ETRR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sparse Gaussian Process Variational Autoencoders","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"stat.ML","authors_text":"Jonathan So, Matthew Ashman, Michael Pearce, Richard E. Turner, Vincent Fortuin, Will Tebbutt","submitted_at":"2020-10-20T10:19:56Z","abstract_excerpt":"Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering. An effective framework for handling such data are Gaussian process deep generative models (GP-DGMs), which employ GP priors over the latent variables of DGMs. Existing approaches for performing inference in GP-DGMs do not support sparse GP approximations based on inducing points, which are essential for the computational efficiency of GPs, nor do they handle missing data -- a natural occurrence in many spatio-temporal datasets -- in a principled manner. We address these shortcomings with the de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.10177","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/2010.10177/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":"2026-07-05T01:45:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hO3ilCaEeV8xcpd8oZNXpHjHpCxDv9GUwg/ZwiJNAmTRFEDa3pSd6Jt0w9IV5U7S1KNWOIKj27QG3u4mf+SKCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T10:41:00.459800Z"},"content_sha256":"3ba471a97e829c69913308b36f2acf25c32bb99199b5ed87e68b4480c25f94fb","schema_version":"1.0","event_id":"sha256:3ba471a97e829c69913308b36f2acf25c32bb99199b5ed87e68b4480c25f94fb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IPWEABLRE63K3UFGRKCHZ5ETRR/bundle.json","state_url":"https://pith.science/pith/IPWEABLRE63K3UFGRKCHZ5ETRR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IPWEABLRE63K3UFGRKCHZ5ETRR/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":"2026-08-18T10:41:00Z","links":{"resolver":"https://pith.science/pith/IPWEABLRE63K3UFGRKCHZ5ETRR","bundle":"https://pith.science/pith/IPWEABLRE63K3UFGRKCHZ5ETRR/bundle.json","state":"https://pith.science/pith/IPWEABLRE63K3UFGRKCHZ5ETRR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IPWEABLRE63K3UFGRKCHZ5ETRR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:IPWEABLRE63K3UFGRKCHZ5ETRR","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":"fe3d5139db0884b1a4ca367d7abf2b76ae62d8668e3fa1ee41d236abcfd8fd85","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-10-20T10:19:56Z","title_canon_sha256":"3acfec2ff9b1d211e874fd009d09ececdc59ff36bb6ce47e8ef633527175a4a2"},"schema_version":"1.0","source":{"id":"2010.10177","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.10177","created_at":"2026-07-05T01:45:31Z"},{"alias_kind":"arxiv_version","alias_value":"2010.10177v2","created_at":"2026-07-05T01:45:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.10177","created_at":"2026-07-05T01:45:31Z"},{"alias_kind":"pith_short_12","alias_value":"IPWEABLRE63K","created_at":"2026-07-05T01:45:31Z"},{"alias_kind":"pith_short_16","alias_value":"IPWEABLRE63K3UFG","created_at":"2026-07-05T01:45:31Z"},{"alias_kind":"pith_short_8","alias_value":"IPWEABLR","created_at":"2026-07-05T01:45:31Z"}],"graph_snapshots":[{"event_id":"sha256:3ba471a97e829c69913308b36f2acf25c32bb99199b5ed87e68b4480c25f94fb","target":"graph","created_at":"2026-07-05T01:45:31Z","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/2010.10177/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering. An effective framework for handling such data are Gaussian process deep generative models (GP-DGMs), which employ GP priors over the latent variables of DGMs. Existing approaches for performing inference in GP-DGMs do not support sparse GP approximations based on inducing points, which are essential for the computational efficiency of GPs, nor do they handle missing data -- a natural occurrence in many spatio-temporal datasets -- in a principled manner. We address these shortcomings with the de","authors_text":"Jonathan So, Matthew Ashman, Michael Pearce, Richard E. Turner, Vincent Fortuin, Will Tebbutt","cross_cats":["cs.LG","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-10-20T10:19:56Z","title":"Sparse Gaussian Process Variational Autoencoders"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.10177","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:d1ee8a37be4257afca7c19d11f3291578ae6e4540f97af92fbe3de1a87920bbc","target":"record","created_at":"2026-07-05T01:45:31Z","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":"fe3d5139db0884b1a4ca367d7abf2b76ae62d8668e3fa1ee41d236abcfd8fd85","cross_cats_sorted":["cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-10-20T10:19:56Z","title_canon_sha256":"3acfec2ff9b1d211e874fd009d09ececdc59ff36bb6ce47e8ef633527175a4a2"},"schema_version":"1.0","source":{"id":"2010.10177","kind":"arxiv","version":2}},"canonical_sha256":"43ec40057127b6add0a68a847cf4938c7f240aeedc40c7917559e67241d5f5d8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"43ec40057127b6add0a68a847cf4938c7f240aeedc40c7917559e67241d5f5d8","first_computed_at":"2026-07-05T01:45:31.545450Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:45:31.545450Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rVGfEnaet43azpvdM+qAuxVCuKx+Ip6zluS7hrJkki2vEh8BArEvMqVsRFH/k/aqQc67h0bk1DYFhGIIX405Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:45:31.545960Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.10177","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d1ee8a37be4257afca7c19d11f3291578ae6e4540f97af92fbe3de1a87920bbc","sha256:3ba471a97e829c69913308b36f2acf25c32bb99199b5ed87e68b4480c25f94fb"],"state_sha256":"25a1288e1f14449227cf4ff69b44200082993b9c8e9273a3e548a3fd443247ac"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F7XLjzVqfvYOjf97c0PrCQ0WX9wrV4YDiEGMAZXwJs2HvyEBKQrdJ0fUA3/Vh/rm0p9s0vVJHlUiIWZx4djXCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T10:41:00.467510Z","bundle_sha256":"1ca3cfa59bfffda3bc54d7f6581d03fd4bdc4558c086faf4a04b95b153479504"}}