{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:H2ZJZQ3BLJ6XNSB6XOKTIU4IIK","short_pith_number":"pith:H2ZJZQ3B","canonical_record":{"source":{"id":"2110.11044","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-10-21T10:44:23Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3962de89f32c66d2feb7361656025c6a73bbc7c6ab346fb51fc0c909b142f6b5","abstract_canon_sha256":"9b479664b62d7e0418fbe3a8df36bf074c1ce9478394f97f44033cee22317b6a"},"schema_version":"1.0"},"canonical_sha256":"3eb29cc3615a7d76c83ebb9534538842b5d107bde42df506d490bcda0068ae3f","source":{"kind":"arxiv","id":"2110.11044","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.11044","created_at":"2026-07-05T03:24:35Z"},{"alias_kind":"arxiv_version","alias_value":"2110.11044v1","created_at":"2026-07-05T03:24:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.11044","created_at":"2026-07-05T03:24:35Z"},{"alias_kind":"pith_short_12","alias_value":"H2ZJZQ3BLJ6X","created_at":"2026-07-05T03:24:35Z"},{"alias_kind":"pith_short_16","alias_value":"H2ZJZQ3BLJ6XNSB6","created_at":"2026-07-05T03:24:35Z"},{"alias_kind":"pith_short_8","alias_value":"H2ZJZQ3B","created_at":"2026-07-05T03:24:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:H2ZJZQ3BLJ6XNSB6XOKTIU4IIK","target":"record","payload":{"canonical_record":{"source":{"id":"2110.11044","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-10-21T10:44:23Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3962de89f32c66d2feb7361656025c6a73bbc7c6ab346fb51fc0c909b142f6b5","abstract_canon_sha256":"9b479664b62d7e0418fbe3a8df36bf074c1ce9478394f97f44033cee22317b6a"},"schema_version":"1.0"},"canonical_sha256":"3eb29cc3615a7d76c83ebb9534538842b5d107bde42df506d490bcda0068ae3f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:24:35.695345Z","signature_b64":"0ZOH3OZppApmQePglVVhGB26KOXj5G8e3MT3CnuCi6fqH1ATmTVyKq/19iDf16wsgqZJbDAOFLqQEYyMfOGcAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3eb29cc3615a7d76c83ebb9534538842b5d107bde42df506d490bcda0068ae3f","last_reissued_at":"2026-07-05T03:24:35.694910Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:24:35.694910Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2110.11044","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":"2026-07-05T03:24:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MI5Y7ly2r0H6hW7ENk9/+sCXpHizzacXPIGYhT2q8bfxmXPnBsh+tBJBbk9whDsYFEoknucm07CY/rTit/G+Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T21:58:13.220870Z"},"content_sha256":"2ebed28e044663ed41afffa1197b7ddd44a4424380e31d1d5744bd4bf75b20e4","schema_version":"1.0","event_id":"sha256:2ebed28e044663ed41afffa1197b7ddd44a4424380e31d1d5744bd4bf75b20e4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:H2ZJZQ3BLJ6XNSB6XOKTIU4IIK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian Meta-Learning Through Variational Gaussian Processes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Nikhil Sardana, Vivek Myers","submitted_at":"2021-10-21T10:44:23Z","abstract_excerpt":"Recent advances in the field of meta-learning have tackled domains consisting of large numbers of small (\"few-shot\") supervised learning tasks. Meta-learning algorithms must be able to rapidly adapt to any individual few-shot task, fitting to a small support set within a task and using it to predict the labels of the task's query set. This problem setting can be extended to the Bayesian context, wherein rather than predicting a single label for each query data point, a model predicts a distribution of labels capturing its uncertainty. Successful methods in this domain include Bayesian ensembli"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.11044","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/2110.11044/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":"2026-07-05T03:24:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sKb6SsVdxzrtTecSxLtcsMYFzA7tRYbJcLrb5DRHF7ppSnWcg+dXPEC5jKZt/7hkJ8Czt6q6dx8z9ARDdwKtCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T21:58:13.221521Z"},"content_sha256":"b51c86f9dccf05356cab3721c1a15af7106b53d3029d0509e372de63c644770e","schema_version":"1.0","event_id":"sha256:b51c86f9dccf05356cab3721c1a15af7106b53d3029d0509e372de63c644770e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/H2ZJZQ3BLJ6XNSB6XOKTIU4IIK/bundle.json","state_url":"https://pith.science/pith/H2ZJZQ3BLJ6XNSB6XOKTIU4IIK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/H2ZJZQ3BLJ6XNSB6XOKTIU4IIK/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":"2026-07-07T21:58:13Z","links":{"resolver":"https://pith.science/pith/H2ZJZQ3BLJ6XNSB6XOKTIU4IIK","bundle":"https://pith.science/pith/H2ZJZQ3BLJ6XNSB6XOKTIU4IIK/bundle.json","state":"https://pith.science/pith/H2ZJZQ3BLJ6XNSB6XOKTIU4IIK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/H2ZJZQ3BLJ6XNSB6XOKTIU4IIK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:H2ZJZQ3BLJ6XNSB6XOKTIU4IIK","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":"9b479664b62d7e0418fbe3a8df36bf074c1ce9478394f97f44033cee22317b6a","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-10-21T10:44:23Z","title_canon_sha256":"3962de89f32c66d2feb7361656025c6a73bbc7c6ab346fb51fc0c909b142f6b5"},"schema_version":"1.0","source":{"id":"2110.11044","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.11044","created_at":"2026-07-05T03:24:35Z"},{"alias_kind":"arxiv_version","alias_value":"2110.11044v1","created_at":"2026-07-05T03:24:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.11044","created_at":"2026-07-05T03:24:35Z"},{"alias_kind":"pith_short_12","alias_value":"H2ZJZQ3BLJ6X","created_at":"2026-07-05T03:24:35Z"},{"alias_kind":"pith_short_16","alias_value":"H2ZJZQ3BLJ6XNSB6","created_at":"2026-07-05T03:24:35Z"},{"alias_kind":"pith_short_8","alias_value":"H2ZJZQ3B","created_at":"2026-07-05T03:24:35Z"}],"graph_snapshots":[{"event_id":"sha256:b51c86f9dccf05356cab3721c1a15af7106b53d3029d0509e372de63c644770e","target":"graph","created_at":"2026-07-05T03:24:35Z","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/2110.11044/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances in the field of meta-learning have tackled domains consisting of large numbers of small (\"few-shot\") supervised learning tasks. Meta-learning algorithms must be able to rapidly adapt to any individual few-shot task, fitting to a small support set within a task and using it to predict the labels of the task's query set. This problem setting can be extended to the Bayesian context, wherein rather than predicting a single label for each query data point, a model predicts a distribution of labels capturing its uncertainty. Successful methods in this domain include Bayesian ensembli","authors_text":"Nikhil Sardana, Vivek Myers","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-10-21T10:44:23Z","title":"Bayesian Meta-Learning Through Variational Gaussian Processes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.11044","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":"sha256:2ebed28e044663ed41afffa1197b7ddd44a4424380e31d1d5744bd4bf75b20e4","target":"record","created_at":"2026-07-05T03:24:35Z","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":"9b479664b62d7e0418fbe3a8df36bf074c1ce9478394f97f44033cee22317b6a","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-10-21T10:44:23Z","title_canon_sha256":"3962de89f32c66d2feb7361656025c6a73bbc7c6ab346fb51fc0c909b142f6b5"},"schema_version":"1.0","source":{"id":"2110.11044","kind":"arxiv","version":1}},"canonical_sha256":"3eb29cc3615a7d76c83ebb9534538842b5d107bde42df506d490bcda0068ae3f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3eb29cc3615a7d76c83ebb9534538842b5d107bde42df506d490bcda0068ae3f","first_computed_at":"2026-07-05T03:24:35.694910Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:24:35.694910Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0ZOH3OZppApmQePglVVhGB26KOXj5G8e3MT3CnuCi6fqH1ATmTVyKq/19iDf16wsgqZJbDAOFLqQEYyMfOGcAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:24:35.695345Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.11044","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2ebed28e044663ed41afffa1197b7ddd44a4424380e31d1d5744bd4bf75b20e4","sha256:b51c86f9dccf05356cab3721c1a15af7106b53d3029d0509e372de63c644770e"],"state_sha256":"c7900195db396e0166c07fe380f82aff602b163c465c1cf1514c7b34f5e594eb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L4EUQ/O6w+bQGQSHwdARkd84cd5l9ZWapKXc7ivyh8QSU5XpZRPmzfg8gu85/bm0BmfNm4uCa3LDDyJebXIiAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T21:58:13.225775Z","bundle_sha256":"026580a9f965d8cfd472c3fd7edecb74d611dff1d8702b139a81305dc410e27f"}}