{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:Z3FXQHWYEISDXROBKABEGGHRMO","short_pith_number":"pith:Z3FXQHWY","canonical_record":{"source":{"id":"2011.02952","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-05T16:29:22Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4f88d62ecb0b376b5c92c62eda1832469c44e1bd2c60addc0d9ff90ac174ad60","abstract_canon_sha256":"a49c86ac051eccfdc9685caad525d76f26445f8f4c5c32994f78b1470986d279"},"schema_version":"1.0"},"canonical_sha256":"cecb781ed822243bc5c150024318f163af2965a43ad5697445d3990edc538cd2","source":{"kind":"arxiv","id":"2011.02952","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.02952","created_at":"2026-07-05T01:58:12Z"},{"alias_kind":"arxiv_version","alias_value":"2011.02952v2","created_at":"2026-07-05T01:58:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.02952","created_at":"2026-07-05T01:58:12Z"},{"alias_kind":"pith_short_12","alias_value":"Z3FXQHWYEISD","created_at":"2026-07-05T01:58:12Z"},{"alias_kind":"pith_short_16","alias_value":"Z3FXQHWYEISDXROB","created_at":"2026-07-05T01:58:12Z"},{"alias_kind":"pith_short_8","alias_value":"Z3FXQHWY","created_at":"2026-07-05T01:58:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:Z3FXQHWYEISDXROBKABEGGHRMO","target":"record","payload":{"canonical_record":{"source":{"id":"2011.02952","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-05T16:29:22Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4f88d62ecb0b376b5c92c62eda1832469c44e1bd2c60addc0d9ff90ac174ad60","abstract_canon_sha256":"a49c86ac051eccfdc9685caad525d76f26445f8f4c5c32994f78b1470986d279"},"schema_version":"1.0"},"canonical_sha256":"cecb781ed822243bc5c150024318f163af2965a43ad5697445d3990edc538cd2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:58:12.536642Z","signature_b64":"7RYk0KLZCjakmNdRU6JM85tu0KD5Wj7wPsqq8M1LhvtGCNF+eHWHGtmmQIKBernPoaNZ7FTvUoHOKr5V/Qg5Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cecb781ed822243bc5c150024318f163af2965a43ad5697445d3990edc538cd2","last_reissued_at":"2026-07-05T01:58:12.536189Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:58:12.536189Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2011.02952","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":"2026-07-05T01:58:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n78CC/3Dux+u91f1Nulpm3eSVqlg8oQpDA/Od/wrQut/a+DsBuHhPRPeOp9EgqVHPglATtvxODDZYlsP4VTGCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T19:49:27.469338Z"},"content_sha256":"c116ba294bf7026878bb380cdec8cd9b9a5730b8a530a8c2b6a10344a4e545d6","schema_version":"1.0","event_id":"sha256:c116ba294bf7026878bb380cdec8cd9b9a5730b8a530a8c2b6a10344a4e545d6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:Z3FXQHWYEISDXROBKABEGGHRMO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generalized Negative Correlation Learning for Deep Ensembling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Katharina Morik, Lukas Pfahler, Sebastian Buschj\\\"ager","submitted_at":"2020-11-05T16:29:22Z","abstract_excerpt":"Ensemble algorithms offer state of the art performance in many machine learning applications. A common explanation for their excellent performance is due to the bias-variance decomposition of the mean squared error which shows that the algorithm's error can be decomposed into its bias and variance. Both quantities are often opposed to each other and ensembles offer an effective way to manage them as they reduce the variance through a diverse set of base learners while keeping the bias low at the same time. Even though there have been numerous works on decomposing other loss functions, the exac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.02952","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/2011.02952/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":"2026-07-05T01:58:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ItrlJF3hMTz31Dx7cd3GoFwYQrlojPrHdIKoU0g7GGAFzWJKBkYTcmI/hmJnX0PJlQOOHKBI/ccF51My+B0hCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T19:49:27.469929Z"},"content_sha256":"211854ae53d67c548b93305f6d6a7ed9830ca8248f6b5c6dd3b92e3c9e146cf3","schema_version":"1.0","event_id":"sha256:211854ae53d67c548b93305f6d6a7ed9830ca8248f6b5c6dd3b92e3c9e146cf3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Z3FXQHWYEISDXROBKABEGGHRMO/bundle.json","state_url":"https://pith.science/pith/Z3FXQHWYEISDXROBKABEGGHRMO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Z3FXQHWYEISDXROBKABEGGHRMO/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":"2026-08-20T19:49:27Z","links":{"resolver":"https://pith.science/pith/Z3FXQHWYEISDXROBKABEGGHRMO","bundle":"https://pith.science/pith/Z3FXQHWYEISDXROBKABEGGHRMO/bundle.json","state":"https://pith.science/pith/Z3FXQHWYEISDXROBKABEGGHRMO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Z3FXQHWYEISDXROBKABEGGHRMO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:Z3FXQHWYEISDXROBKABEGGHRMO","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":"a49c86ac051eccfdc9685caad525d76f26445f8f4c5c32994f78b1470986d279","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-05T16:29:22Z","title_canon_sha256":"4f88d62ecb0b376b5c92c62eda1832469c44e1bd2c60addc0d9ff90ac174ad60"},"schema_version":"1.0","source":{"id":"2011.02952","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.02952","created_at":"2026-07-05T01:58:12Z"},{"alias_kind":"arxiv_version","alias_value":"2011.02952v2","created_at":"2026-07-05T01:58:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.02952","created_at":"2026-07-05T01:58:12Z"},{"alias_kind":"pith_short_12","alias_value":"Z3FXQHWYEISD","created_at":"2026-07-05T01:58:12Z"},{"alias_kind":"pith_short_16","alias_value":"Z3FXQHWYEISDXROB","created_at":"2026-07-05T01:58:12Z"},{"alias_kind":"pith_short_8","alias_value":"Z3FXQHWY","created_at":"2026-07-05T01:58:12Z"}],"graph_snapshots":[{"event_id":"sha256:211854ae53d67c548b93305f6d6a7ed9830ca8248f6b5c6dd3b92e3c9e146cf3","target":"graph","created_at":"2026-07-05T01:58:12Z","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/2011.02952/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Ensemble algorithms offer state of the art performance in many machine learning applications. A common explanation for their excellent performance is due to the bias-variance decomposition of the mean squared error which shows that the algorithm's error can be decomposed into its bias and variance. Both quantities are often opposed to each other and ensembles offer an effective way to manage them as they reduce the variance through a diverse set of base learners while keeping the bias low at the same time. Even though there have been numerous works on decomposing other loss functions, the exac","authors_text":"Katharina Morik, Lukas Pfahler, Sebastian Buschj\\\"ager","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-05T16:29:22Z","title":"Generalized Negative Correlation Learning for Deep Ensembling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.02952","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:c116ba294bf7026878bb380cdec8cd9b9a5730b8a530a8c2b6a10344a4e545d6","target":"record","created_at":"2026-07-05T01:58:12Z","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":"a49c86ac051eccfdc9685caad525d76f26445f8f4c5c32994f78b1470986d279","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-05T16:29:22Z","title_canon_sha256":"4f88d62ecb0b376b5c92c62eda1832469c44e1bd2c60addc0d9ff90ac174ad60"},"schema_version":"1.0","source":{"id":"2011.02952","kind":"arxiv","version":2}},"canonical_sha256":"cecb781ed822243bc5c150024318f163af2965a43ad5697445d3990edc538cd2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cecb781ed822243bc5c150024318f163af2965a43ad5697445d3990edc538cd2","first_computed_at":"2026-07-05T01:58:12.536189Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:58:12.536189Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7RYk0KLZCjakmNdRU6JM85tu0KD5Wj7wPsqq8M1LhvtGCNF+eHWHGtmmQIKBernPoaNZ7FTvUoHOKr5V/Qg5Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:58:12.536642Z","signed_message":"canonical_sha256_bytes"},"source_id":"2011.02952","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c116ba294bf7026878bb380cdec8cd9b9a5730b8a530a8c2b6a10344a4e545d6","sha256:211854ae53d67c548b93305f6d6a7ed9830ca8248f6b5c6dd3b92e3c9e146cf3"],"state_sha256":"480a47f5694d74440b83338dd88dfbf34249cc0ce9587f8d1fbf0ce9b29626c7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+ky1DaqM3sixlv8gJAliWU0x7iNmz3dGD9Hb+Y8aa/qs+FQ64XvJjVz6C3J/K4/rcLHe/h0pcrVKARWtreZ6Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T19:49:27.475168Z","bundle_sha256":"e2c8f58200f87fc54740cb197d5d973864a0c70e47938e39e126efe3c26250c1"}}