{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FTTT2SNJKIK7UU2KPJFOQITFGF","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":"9f0817f65b8e42cf9063c592c959a95304891011b361882cc099ed7fc15ef11d","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-04-24T16:31:55Z","title_canon_sha256":"833419e4fedd3e22c3d5d1d54b10383bf54e1bd69cc013c8a77ae5265406580d"},"schema_version":"1.0","source":{"id":"2504.17719","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.17719","created_at":"2026-07-05T10:53:40Z"},{"alias_kind":"arxiv_version","alias_value":"2504.17719v1","created_at":"2026-07-05T10:53:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.17719","created_at":"2026-07-05T10:53:40Z"},{"alias_kind":"pith_short_12","alias_value":"FTTT2SNJKIK7","created_at":"2026-07-05T10:53:40Z"},{"alias_kind":"pith_short_16","alias_value":"FTTT2SNJKIK7UU2K","created_at":"2026-07-05T10:53:40Z"},{"alias_kind":"pith_short_8","alias_value":"FTTT2SNJ","created_at":"2026-07-05T10:53:40Z"}],"graph_snapshots":[{"event_id":"sha256:0fa2ee3dc8937944ffcc0b8e740b015e206a6dd3bd388787ec66c60e4bce782d","target":"graph","created_at":"2026-07-05T10:53:40Z","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/2504.17719/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Reliable uncertainty estimates are crucial in modern machine learning. Deep Gaussian Processes (DGPs) and Deep Sigma Point Processes (DSPPs) extend GPs hierarchically, offering promising methods for uncertainty quantification grounded in Bayesian principles. However, their empirical calibration and robustness under distribution shift relative to baselines like Deep Ensembles remain understudied. This work evaluates these models on regression (CASP dataset) and classification (ESR dataset) tasks, assessing predictive performance (MAE, Accu- racy), calibration using Negative Log-Likelihood (NLL)","authors_text":"Jeremias Lino Ferrao, Matthijs van der Lende, Niclas M\\\"uller-Hof","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-04-24T16:31:55Z","title":"Evaluating Uncertainty in Deep Gaussian Processes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.17719","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":"sha256:0ddf0b4926c460c0b7677aab45febe1d5f2795d60aae8c96dc0be6790a9d3573","target":"record","created_at":"2026-07-05T10:53:40Z","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":"9f0817f65b8e42cf9063c592c959a95304891011b361882cc099ed7fc15ef11d","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-04-24T16:31:55Z","title_canon_sha256":"833419e4fedd3e22c3d5d1d54b10383bf54e1bd69cc013c8a77ae5265406580d"},"schema_version":"1.0","source":{"id":"2504.17719","kind":"arxiv","version":1}},"canonical_sha256":"2ce73d49a95215fa534a7a4ae82265317914f261e7de42322fe6759fdbf58161","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2ce73d49a95215fa534a7a4ae82265317914f261e7de42322fe6759fdbf58161","first_computed_at":"2026-07-05T10:53:40.055408Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:53:40.055408Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JHcy8ruXKsoyRitk/ULRJU1jXsu7jnIh96rwaJf7OALNq22mgddBpx/JDFnRizih/lBsAi+82H7emXEKm323AA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:53:40.055855Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.17719","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0ddf0b4926c460c0b7677aab45febe1d5f2795d60aae8c96dc0be6790a9d3573","sha256:0fa2ee3dc8937944ffcc0b8e740b015e206a6dd3bd388787ec66c60e4bce782d"],"state_sha256":"2dd413de36ed25d533b8e1764456aee14a99a8bf48f4fe3f43e31b0efb97ac53"}