{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:JX7XCHRMBHWXQD33QUGR3RBGMQ","short_pith_number":"pith:JX7XCHRM","canonical_record":{"source":{"id":"1809.07751","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-09-20T17:36:24Z","cross_cats_sorted":["cs.AI","cs.LG","math.PR"],"title_canon_sha256":"bf3286d37fd3f985bb08d11ed674d422d0daced7c19ec365941f8c77e9c9958e","abstract_canon_sha256":"4fe28b3f77f07d45b0d8448db9de714224b212062e7977145727cdca5ad7dea0"},"schema_version":"1.0"},"canonical_sha256":"4dff711e2c09ed780f7b850d1dc426643735e65385e3964216e966ca6ab0ae35","source":{"kind":"arxiv","id":"1809.07751","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.07751","created_at":"2026-05-18T00:05:15Z"},{"alias_kind":"arxiv_version","alias_value":"1809.07751v1","created_at":"2026-05-18T00:05:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.07751","created_at":"2026-05-18T00:05:15Z"},{"alias_kind":"pith_short_12","alias_value":"JX7XCHRMBHWX","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"JX7XCHRMBHWXQD33","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"JX7XCHRM","created_at":"2026-05-18T12:32:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:JX7XCHRMBHWXQD33QUGR3RBGMQ","target":"record","payload":{"canonical_record":{"source":{"id":"1809.07751","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-09-20T17:36:24Z","cross_cats_sorted":["cs.AI","cs.LG","math.PR"],"title_canon_sha256":"bf3286d37fd3f985bb08d11ed674d422d0daced7c19ec365941f8c77e9c9958e","abstract_canon_sha256":"4fe28b3f77f07d45b0d8448db9de714224b212062e7977145727cdca5ad7dea0"},"schema_version":"1.0"},"canonical_sha256":"4dff711e2c09ed780f7b850d1dc426643735e65385e3964216e966ca6ab0ae35","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:15.251555Z","signature_b64":"7EbDiVk6xva0e2xSfdW1kHeundLqO6KdNkgTtv5VP1skLsV5nfHuQHOXX1/TPOkoVbmEDIDpOz96qfFecEDEBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4dff711e2c09ed780f7b850d1dc426643735e65385e3964216e966ca6ab0ae35","last_reissued_at":"2026-05-18T00:05:15.250901Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:15.250901Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.07751","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":"2026-05-18T00:05:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J9WWgsxLFRPUd6GXPn8DdjWsUIqR5KU1zdKg7i9AqIuq+P2esb2/26eND8SnkDBjeiOsU7NQ8IkoKkPvd1JWDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T08:49:13.070965Z"},"content_sha256":"cd3476a154ff0b776c591c46092df2a91b47bd9d1a7ef79cdbf3bc0334d8bafa","schema_version":"1.0","event_id":"sha256:cd3476a154ff0b776c591c46092df2a91b47bd9d1a7ef79cdbf3bc0334d8bafa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:JX7XCHRMBHWXQD33QUGR3RBGMQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Spline-Based Probability Calibration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","math.PR"],"primary_cat":"stat.ML","authors_text":"Brian Lucena","submitted_at":"2018-09-20T17:36:24Z","abstract_excerpt":"In many classification problems it is desirable to output well-calibrated probabilities on the different classes. We propose a robust, non-parametric method of calibrating probabilities called SplineCalib that utilizes smoothing splines to determine a calibration function. We demonstrate how applying certain transformations as part of the calibration process can improve performance on problems in deep learning and other domains where the scores tend to be \"overconfident\". We adapt the approach to multi-class problems and find that better calibration can improve accuracy as well as log-loss by "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.07751","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":"2026-05-18T00:05:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yNM68H1YDOt3bkLWryyiEJNGLETZxa/AB9ousMbxkNPJtFXMhnf2jKUpiHWyhPcERZULcBpiCU2VQvYyidq7BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T08:49:13.071308Z"},"content_sha256":"74cb480a33a7f6d1117e63e819fc99a142a80f7f16828cdf1e0f7acd16e41584","schema_version":"1.0","event_id":"sha256:74cb480a33a7f6d1117e63e819fc99a142a80f7f16828cdf1e0f7acd16e41584"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JX7XCHRMBHWXQD33QUGR3RBGMQ/bundle.json","state_url":"https://pith.science/pith/JX7XCHRMBHWXQD33QUGR3RBGMQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JX7XCHRMBHWXQD33QUGR3RBGMQ/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":"2026-07-01T08:49:13Z","links":{"resolver":"https://pith.science/pith/JX7XCHRMBHWXQD33QUGR3RBGMQ","bundle":"https://pith.science/pith/JX7XCHRMBHWXQD33QUGR3RBGMQ/bundle.json","state":"https://pith.science/pith/JX7XCHRMBHWXQD33QUGR3RBGMQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JX7XCHRMBHWXQD33QUGR3RBGMQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:JX7XCHRMBHWXQD33QUGR3RBGMQ","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":"4fe28b3f77f07d45b0d8448db9de714224b212062e7977145727cdca5ad7dea0","cross_cats_sorted":["cs.AI","cs.LG","math.PR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-09-20T17:36:24Z","title_canon_sha256":"bf3286d37fd3f985bb08d11ed674d422d0daced7c19ec365941f8c77e9c9958e"},"schema_version":"1.0","source":{"id":"1809.07751","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.07751","created_at":"2026-05-18T00:05:15Z"},{"alias_kind":"arxiv_version","alias_value":"1809.07751v1","created_at":"2026-05-18T00:05:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.07751","created_at":"2026-05-18T00:05:15Z"},{"alias_kind":"pith_short_12","alias_value":"JX7XCHRMBHWX","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"JX7XCHRMBHWXQD33","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"JX7XCHRM","created_at":"2026-05-18T12:32:33Z"}],"graph_snapshots":[{"event_id":"sha256:74cb480a33a7f6d1117e63e819fc99a142a80f7f16828cdf1e0f7acd16e41584","target":"graph","created_at":"2026-05-18T00:05:15Z","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":"In many classification problems it is desirable to output well-calibrated probabilities on the different classes. We propose a robust, non-parametric method of calibrating probabilities called SplineCalib that utilizes smoothing splines to determine a calibration function. We demonstrate how applying certain transformations as part of the calibration process can improve performance on problems in deep learning and other domains where the scores tend to be \"overconfident\". We adapt the approach to multi-class problems and find that better calibration can improve accuracy as well as log-loss by ","authors_text":"Brian Lucena","cross_cats":["cs.AI","cs.LG","math.PR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-09-20T17:36:24Z","title":"Spline-Based Probability Calibration"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.07751","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:cd3476a154ff0b776c591c46092df2a91b47bd9d1a7ef79cdbf3bc0334d8bafa","target":"record","created_at":"2026-05-18T00:05:15Z","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":"4fe28b3f77f07d45b0d8448db9de714224b212062e7977145727cdca5ad7dea0","cross_cats_sorted":["cs.AI","cs.LG","math.PR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-09-20T17:36:24Z","title_canon_sha256":"bf3286d37fd3f985bb08d11ed674d422d0daced7c19ec365941f8c77e9c9958e"},"schema_version":"1.0","source":{"id":"1809.07751","kind":"arxiv","version":1}},"canonical_sha256":"4dff711e2c09ed780f7b850d1dc426643735e65385e3964216e966ca6ab0ae35","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4dff711e2c09ed780f7b850d1dc426643735e65385e3964216e966ca6ab0ae35","first_computed_at":"2026-05-18T00:05:15.250901Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:05:15.250901Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7EbDiVk6xva0e2xSfdW1kHeundLqO6KdNkgTtv5VP1skLsV5nfHuQHOXX1/TPOkoVbmEDIDpOz96qfFecEDEBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:05:15.251555Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.07751","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cd3476a154ff0b776c591c46092df2a91b47bd9d1a7ef79cdbf3bc0334d8bafa","sha256:74cb480a33a7f6d1117e63e819fc99a142a80f7f16828cdf1e0f7acd16e41584"],"state_sha256":"12cb9bbfbebba5ed7492241f0d2e37c79a480760394d1a176b73cf3cfca902b0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MeUPhYTdM/iAwxwaLSsJ8R7mjykPvH7aOMVCetphiz0NibNGibiyJl/kk2evQs/JpeMR91HQDEAv4ukBFF0NDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-01T08:49:13.073542Z","bundle_sha256":"6e8e3a107d8b79461cfeefc718a5c3e3e33b1a6e0fc1e9dc1c8252effe9ebec2"}}