{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:NIRQQ7AVXFO4WRKIMDSXQQJMLM","short_pith_number":"pith:NIRQQ7AV","canonical_record":{"source":{"id":"1902.09238","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-25T12:52:29Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"fb3b3043416c833232f06026d21885e981236c872cdfb2cd39521bae8bd1888c","abstract_canon_sha256":"e521f92e4b95eed60486939dfcecd249e4425edc4d9a5a1057b0538a7223420c"},"schema_version":"1.0"},"canonical_sha256":"6a23087c15b95dcb454860e578412c5b2582ffbceed325e55b056478722308e2","source":{"kind":"arxiv","id":"1902.09238","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.09238","created_at":"2026-05-17T23:52:46Z"},{"alias_kind":"arxiv_version","alias_value":"1902.09238v1","created_at":"2026-05-17T23:52:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.09238","created_at":"2026-05-17T23:52:46Z"},{"alias_kind":"pith_short_12","alias_value":"NIRQQ7AVXFO4","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"NIRQQ7AVXFO4WRKI","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"NIRQQ7AV","created_at":"2026-05-18T12:33:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:NIRQQ7AVXFO4WRKIMDSXQQJMLM","target":"record","payload":{"canonical_record":{"source":{"id":"1902.09238","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-25T12:52:29Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"fb3b3043416c833232f06026d21885e981236c872cdfb2cd39521bae8bd1888c","abstract_canon_sha256":"e521f92e4b95eed60486939dfcecd249e4425edc4d9a5a1057b0538a7223420c"},"schema_version":"1.0"},"canonical_sha256":"6a23087c15b95dcb454860e578412c5b2582ffbceed325e55b056478722308e2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:46.272644Z","signature_b64":"g1KXnK8WbEkUcHpa2N15VD0gi3/XQCsizXftt5c8pGTFxbemVe8lZ3Zp/voO7eTrLM9R+J2ucIpGNN1BdMSODA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6a23087c15b95dcb454860e578412c5b2582ffbceed325e55b056478722308e2","last_reissued_at":"2026-05-17T23:52:46.271447Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:46.271447Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1902.09238","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":"2026-05-17T23:52:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cAV9FC/Jw91fRxceLFbiNQiCzVi0y0+szruSHVwfGcfeuIUb0Beqt+HiYWaSYoMLULbAQ9uhaLlOcvs09sv1Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-27T14:56:00.715502Z"},"content_sha256":"7c55ec1dd9ad4b5c0535201f9ed734ddd022c7a67db549ad7e4a0ee2159f2cba","schema_version":"1.0","event_id":"sha256:7c55ec1dd9ad4b5c0535201f9ed734ddd022c7a67db549ad7e4a0ee2159f2cba"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:NIRQQ7AVXFO4WRKIMDSXQQJMLM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The MBPEP: a deep ensemble pruning algorithm providing high quality uncertainty prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Hao Wang, Jin He, Qijun Huang, Ruihan Hu, Sheng Chang","submitted_at":"2019-02-25T12:52:29Z","abstract_excerpt":"Machine learning algorithms have been effectively applied into various real world tasks. However, it is difficult to provide high-quality machine learning solutions to accommodate an unknown distribution of input datasets; this difficulty is called the uncertainty prediction problems. In this paper, a margin-based Pareto deep ensemble pruning (MBPEP) model is proposed. It achieves the high-quality uncertainty estimation with a small value of the prediction interval width (MPIW) and a high confidence of prediction interval coverage probability (PICP) by using deep ensemble networks. In addition"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.09238","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":"2026-05-17T23:52:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Hb0HMDicVd+62XDqf++TypeYnpWxgDAEOoNsd4pP0PW+K+YSrqKXocykn/9JT9XSaP3LyUe0LQsqgS0WI+UQBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-27T14:56:00.715861Z"},"content_sha256":"7d084e0ce0f868dbe28c0b0facb873cbcac728b2503fe5beb2bb8c7afe7cf004","schema_version":"1.0","event_id":"sha256:7d084e0ce0f868dbe28c0b0facb873cbcac728b2503fe5beb2bb8c7afe7cf004"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NIRQQ7AVXFO4WRKIMDSXQQJMLM/bundle.json","state_url":"https://pith.science/pith/NIRQQ7AVXFO4WRKIMDSXQQJMLM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NIRQQ7AVXFO4WRKIMDSXQQJMLM/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":"2026-06-27T14:56:00Z","links":{"resolver":"https://pith.science/pith/NIRQQ7AVXFO4WRKIMDSXQQJMLM","bundle":"https://pith.science/pith/NIRQQ7AVXFO4WRKIMDSXQQJMLM/bundle.json","state":"https://pith.science/pith/NIRQQ7AVXFO4WRKIMDSXQQJMLM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NIRQQ7AVXFO4WRKIMDSXQQJMLM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:NIRQQ7AVXFO4WRKIMDSXQQJMLM","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":"e521f92e4b95eed60486939dfcecd249e4425edc4d9a5a1057b0538a7223420c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-25T12:52:29Z","title_canon_sha256":"fb3b3043416c833232f06026d21885e981236c872cdfb2cd39521bae8bd1888c"},"schema_version":"1.0","source":{"id":"1902.09238","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.09238","created_at":"2026-05-17T23:52:46Z"},{"alias_kind":"arxiv_version","alias_value":"1902.09238v1","created_at":"2026-05-17T23:52:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.09238","created_at":"2026-05-17T23:52:46Z"},{"alias_kind":"pith_short_12","alias_value":"NIRQQ7AVXFO4","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"NIRQQ7AVXFO4WRKI","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"NIRQQ7AV","created_at":"2026-05-18T12:33:24Z"}],"graph_snapshots":[{"event_id":"sha256:7d084e0ce0f868dbe28c0b0facb873cbcac728b2503fe5beb2bb8c7afe7cf004","target":"graph","created_at":"2026-05-17T23:52:46Z","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":"Machine learning algorithms have been effectively applied into various real world tasks. However, it is difficult to provide high-quality machine learning solutions to accommodate an unknown distribution of input datasets; this difficulty is called the uncertainty prediction problems. In this paper, a margin-based Pareto deep ensemble pruning (MBPEP) model is proposed. It achieves the high-quality uncertainty estimation with a small value of the prediction interval width (MPIW) and a high confidence of prediction interval coverage probability (PICP) by using deep ensemble networks. In addition","authors_text":"Hao Wang, Jin He, Qijun Huang, Ruihan Hu, Sheng Chang","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-25T12:52:29Z","title":"The MBPEP: a deep ensemble pruning algorithm providing high quality uncertainty prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.09238","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":"sha256:7c55ec1dd9ad4b5c0535201f9ed734ddd022c7a67db549ad7e4a0ee2159f2cba","target":"record","created_at":"2026-05-17T23:52:46Z","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":"e521f92e4b95eed60486939dfcecd249e4425edc4d9a5a1057b0538a7223420c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-25T12:52:29Z","title_canon_sha256":"fb3b3043416c833232f06026d21885e981236c872cdfb2cd39521bae8bd1888c"},"schema_version":"1.0","source":{"id":"1902.09238","kind":"arxiv","version":1}},"canonical_sha256":"6a23087c15b95dcb454860e578412c5b2582ffbceed325e55b056478722308e2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6a23087c15b95dcb454860e578412c5b2582ffbceed325e55b056478722308e2","first_computed_at":"2026-05-17T23:52:46.271447Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:46.271447Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"g1KXnK8WbEkUcHpa2N15VD0gi3/XQCsizXftt5c8pGTFxbemVe8lZ3Zp/voO7eTrLM9R+J2ucIpGNN1BdMSODA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:46.272644Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.09238","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7c55ec1dd9ad4b5c0535201f9ed734ddd022c7a67db549ad7e4a0ee2159f2cba","sha256:7d084e0ce0f868dbe28c0b0facb873cbcac728b2503fe5beb2bb8c7afe7cf004"],"state_sha256":"21fa92c2d48e3cc2c6269bbac4e1c5088823a62ef8129d4a32ac6e2acc06edcf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rARtJ1wXqp37zWIRZWxeHgQnv5BCN0dxLUxiMHtJrOMy6zRYCNDFYG15M1tuCYSosaz2ZAE+gYUUB5F7p0POAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-27T14:56:00.718653Z","bundle_sha256":"5b1cbc61bee4209141d4efe193c01a0f462f197728a371fd263ee93f111f4334"}}