{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:QHNAKZ3IDTE7GZ5D33SGSCAXYR","short_pith_number":"pith:QHNAKZ3I","canonical_record":{"source":{"id":"1905.05435","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-14T07:56:58Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"73b5e4638e7a50187add5ad6d10d935ed00a135620ea3ddbcccce5c5a025fb0b","abstract_canon_sha256":"56f603698f2a17882d713e074e6d963ae630234ab5c769ab75f230d1702da5a5"},"schema_version":"1.0"},"canonical_sha256":"81da0567681cc9f367a3dee4690817c46bf33042a1bcfe065aa8fc7f947bd7fd","source":{"kind":"arxiv","id":"1905.05435","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.05435","created_at":"2026-05-17T23:46:16Z"},{"alias_kind":"arxiv_version","alias_value":"1905.05435v1","created_at":"2026-05-17T23:46:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.05435","created_at":"2026-05-17T23:46:16Z"},{"alias_kind":"pith_short_12","alias_value":"QHNAKZ3IDTE7","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"QHNAKZ3IDTE7GZ5D","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"QHNAKZ3I","created_at":"2026-05-18T12:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:QHNAKZ3IDTE7GZ5D33SGSCAXYR","target":"record","payload":{"canonical_record":{"source":{"id":"1905.05435","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-14T07:56:58Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"73b5e4638e7a50187add5ad6d10d935ed00a135620ea3ddbcccce5c5a025fb0b","abstract_canon_sha256":"56f603698f2a17882d713e074e6d963ae630234ab5c769ab75f230d1702da5a5"},"schema_version":"1.0"},"canonical_sha256":"81da0567681cc9f367a3dee4690817c46bf33042a1bcfe065aa8fc7f947bd7fd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:16.488558Z","signature_b64":"sivP4d7sT3aCl7DU4HjfzxHJNEQRxhbfKyh+lRcFaD9uQbFOjXHD0wtE/L67uaNd4TrbDETTmzupyMrAogmYDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81da0567681cc9f367a3dee4690817c46bf33042a1bcfe065aa8fc7f947bd7fd","last_reissued_at":"2026-05-17T23:46:16.488010Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:16.488010Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.05435","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:46:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"q1CpBpPhw8qq8Kf3C0G3GybxCSeWR3wWKM9FiRmnghpal2Vr/JUckD+afZ04Xwp2dxKGQUCZet4AWA5Sp6sFDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T07:47:56.165243Z"},"content_sha256":"8a4a889115c08a2f6f6a70b6e589370f1562cffba847e61e4bceff5430ecad4c","schema_version":"1.0","event_id":"sha256:8a4a889115c08a2f6f6a70b6e589370f1562cffba847e61e4bceff5430ecad4c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:QHNAKZ3IDTE7GZ5D33SGSCAXYR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Gaussian Processes with Importance-Weighted Variational Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Hugh Salimbeni, James Hensman, Marc Peter Deisenroth, Vincent Dutordoir","submitted_at":"2019-05-14T07:56:58Z","abstract_excerpt":"Deep Gaussian processes (DGPs) can model complex marginal densities as well as complex mappings. Non-Gaussian marginals are essential for modelling real-world data, and can be generated from the DGP by incorporating uncorrelated variables to the model. Previous work on DGP models has introduced noise additively and used variational inference with a combination of sparse Gaussian processes and mean-field Gaussians for the approximate posterior. Additive noise attenuates the signal, and the Gaussian form of variational distribution may lead to an inaccurate posterior. We instead incorporate nois"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.05435","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:46:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ug9SX83sfXlD3fFHh5N4RImVAEpoivjqTEOJqUYkbZd214e67z8OUJQ7uH//sbJd/dWnyjDbccEZObkBiegdDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T07:47:56.165667Z"},"content_sha256":"4f142e9ac0f0f3a1b3e3259ba9ada380823265622dfbd2d1c2238a91dd67bd11","schema_version":"1.0","event_id":"sha256:4f142e9ac0f0f3a1b3e3259ba9ada380823265622dfbd2d1c2238a91dd67bd11"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QHNAKZ3IDTE7GZ5D33SGSCAXYR/bundle.json","state_url":"https://pith.science/pith/QHNAKZ3IDTE7GZ5D33SGSCAXYR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QHNAKZ3IDTE7GZ5D33SGSCAXYR/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":"2026-05-30T07:47:56Z","links":{"resolver":"https://pith.science/pith/QHNAKZ3IDTE7GZ5D33SGSCAXYR","bundle":"https://pith.science/pith/QHNAKZ3IDTE7GZ5D33SGSCAXYR/bundle.json","state":"https://pith.science/pith/QHNAKZ3IDTE7GZ5D33SGSCAXYR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QHNAKZ3IDTE7GZ5D33SGSCAXYR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:QHNAKZ3IDTE7GZ5D33SGSCAXYR","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":"56f603698f2a17882d713e074e6d963ae630234ab5c769ab75f230d1702da5a5","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-14T07:56:58Z","title_canon_sha256":"73b5e4638e7a50187add5ad6d10d935ed00a135620ea3ddbcccce5c5a025fb0b"},"schema_version":"1.0","source":{"id":"1905.05435","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.05435","created_at":"2026-05-17T23:46:16Z"},{"alias_kind":"arxiv_version","alias_value":"1905.05435v1","created_at":"2026-05-17T23:46:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.05435","created_at":"2026-05-17T23:46:16Z"},{"alias_kind":"pith_short_12","alias_value":"QHNAKZ3IDTE7","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"QHNAKZ3IDTE7GZ5D","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"QHNAKZ3I","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:4f142e9ac0f0f3a1b3e3259ba9ada380823265622dfbd2d1c2238a91dd67bd11","target":"graph","created_at":"2026-05-17T23:46:16Z","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"},"paper":{"abstract_excerpt":"Deep Gaussian processes (DGPs) can model complex marginal densities as well as complex mappings. Non-Gaussian marginals are essential for modelling real-world data, and can be generated from the DGP by incorporating uncorrelated variables to the model. Previous work on DGP models has introduced noise additively and used variational inference with a combination of sparse Gaussian processes and mean-field Gaussians for the approximate posterior. Additive noise attenuates the signal, and the Gaussian form of variational distribution may lead to an inaccurate posterior. We instead incorporate nois","authors_text":"Hugh Salimbeni, James Hensman, Marc Peter Deisenroth, Vincent Dutordoir","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-14T07:56:58Z","title":"Deep Gaussian Processes with Importance-Weighted Variational Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.05435","kind":"arxiv","version":1},"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":"sha256:8a4a889115c08a2f6f6a70b6e589370f1562cffba847e61e4bceff5430ecad4c","target":"record","created_at":"2026-05-17T23:46:16Z","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":"56f603698f2a17882d713e074e6d963ae630234ab5c769ab75f230d1702da5a5","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-05-14T07:56:58Z","title_canon_sha256":"73b5e4638e7a50187add5ad6d10d935ed00a135620ea3ddbcccce5c5a025fb0b"},"schema_version":"1.0","source":{"id":"1905.05435","kind":"arxiv","version":1}},"canonical_sha256":"81da0567681cc9f367a3dee4690817c46bf33042a1bcfe065aa8fc7f947bd7fd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"81da0567681cc9f367a3dee4690817c46bf33042a1bcfe065aa8fc7f947bd7fd","first_computed_at":"2026-05-17T23:46:16.488010Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:16.488010Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sivP4d7sT3aCl7DU4HjfzxHJNEQRxhbfKyh+lRcFaD9uQbFOjXHD0wtE/L67uaNd4TrbDETTmzupyMrAogmYDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:16.488558Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.05435","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8a4a889115c08a2f6f6a70b6e589370f1562cffba847e61e4bceff5430ecad4c","sha256:4f142e9ac0f0f3a1b3e3259ba9ada380823265622dfbd2d1c2238a91dd67bd11"],"state_sha256":"de766f5c6bf05b04e089b3f7617fe01e5817dfdb2bf12f949ff39481d7a3a6fe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KI6fJnJba38Q/8NJoCEmMMc94r050jENWptXVGWQ9PV5lzrPyoZwr0Fz6BZe/JTJMV2L2afLGtof/KWCeMEIBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T07:47:56.167946Z","bundle_sha256":"836fe60464fdd6361a61b2476c164261d064e04e346803edec5bafacdcd4bf9e"}}