{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:Q4J5CJTPZRSTRTQMX72J43X46V","short_pith_number":"pith:Q4J5CJTP","canonical_record":{"source":{"id":"2103.03788","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-05T16:35:10Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e2f0dc77809ade763b4a62d40816d54a6a0e9d9fe2e91f30d786524505766ce5","abstract_canon_sha256":"96a70adc023bcaa4ec0f820d6514b0618ca8f4e1750f0defef99f7e1fd065e03"},"schema_version":"1.0"},"canonical_sha256":"8713d1266fcc6538ce0cbff49e6efcf57356a7ed1cca2890458a0c1fedb57046","source":{"kind":"arxiv","id":"2103.03788","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.03788","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"arxiv_version","alias_value":"2103.03788v1","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.03788","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"pith_short_12","alias_value":"Q4J5CJTPZRST","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"pith_short_16","alias_value":"Q4J5CJTPZRSTRTQM","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"pith_short_8","alias_value":"Q4J5CJTP","created_at":"2026-07-05T02:20:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:Q4J5CJTPZRSTRTQMX72J43X46V","target":"record","payload":{"canonical_record":{"source":{"id":"2103.03788","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-05T16:35:10Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e2f0dc77809ade763b4a62d40816d54a6a0e9d9fe2e91f30d786524505766ce5","abstract_canon_sha256":"96a70adc023bcaa4ec0f820d6514b0618ca8f4e1750f0defef99f7e1fd065e03"},"schema_version":"1.0"},"canonical_sha256":"8713d1266fcc6538ce0cbff49e6efcf57356a7ed1cca2890458a0c1fedb57046","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:20:39.843975Z","signature_b64":"Joiup2OfMKy/0F9MarpFEzMwjBUpo0RyVxAyntMRwOWdi9kOyec81K/0gZ4rfB5Alip2cBQo/2s1RNQIZV0wCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8713d1266fcc6538ce0cbff49e6efcf57356a7ed1cca2890458a0c1fedb57046","last_reissued_at":"2026-07-05T02:20:39.843554Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:20:39.843554Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2103.03788","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":"2026-07-05T02:20:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kJG0xiUKKriXGCy6alcV5fd3w3BpZvJxj+P8kjN+C43zdgul8AlcGRVCPndj8RcC+SvJMMeH1+VsDjGr4DyQCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T06:54:02.176090Z"},"content_sha256":"c2c7c6e885b36b57a72fa3430d346a289c0b1b256e1d9e464e966bdbf7851e31","schema_version":"1.0","event_id":"sha256:c2c7c6e885b36b57a72fa3430d346a289c0b1b256e1d9e464e966bdbf7851e31"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:Q4J5CJTPZRSTRTQMX72J43X46V","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Loss Estimators Improve Model Generalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Andreas Spanias, Deepta Rajan, Jayaraman J. Thiagarajan, Vivek Narayanaswamy","submitted_at":"2021-03-05T16:35:10Z","abstract_excerpt":"With increased interest in adopting AI methods for clinical diagnosis, a vital step towards safe deployment of such tools is to ensure that the models not only produce accurate predictions but also do not generalize to data regimes where the training data provide no meaningful evidence. Existing approaches for ensuring the distribution of model predictions to be similar to that of the true distribution rely on explicit uncertainty estimators that are inherently hard to calibrate. In this paper, we propose to train a loss estimator alongside the predictive model, using a contrastive training ob"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.03788","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/2103.03788/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":"2026-07-05T02:20:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2mmEt28GXT5soUwGZM1/dUdgoHk2sXyAgXa7ahZafWGQwhm/tCQrYj3jbduEOCrQbykDwwaLcM3dTMUj4nSVAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T06:54:02.176490Z"},"content_sha256":"a0188463101dfa47b8d3d2b78599346d47cad8a86dc26f8bb0f95629745fd883","schema_version":"1.0","event_id":"sha256:a0188463101dfa47b8d3d2b78599346d47cad8a86dc26f8bb0f95629745fd883"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Q4J5CJTPZRSTRTQMX72J43X46V/bundle.json","state_url":"https://pith.science/pith/Q4J5CJTPZRSTRTQMX72J43X46V/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Q4J5CJTPZRSTRTQMX72J43X46V/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":"2026-07-24T06:54:02Z","links":{"resolver":"https://pith.science/pith/Q4J5CJTPZRSTRTQMX72J43X46V","bundle":"https://pith.science/pith/Q4J5CJTPZRSTRTQMX72J43X46V/bundle.json","state":"https://pith.science/pith/Q4J5CJTPZRSTRTQMX72J43X46V/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Q4J5CJTPZRSTRTQMX72J43X46V/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:Q4J5CJTPZRSTRTQMX72J43X46V","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":"96a70adc023bcaa4ec0f820d6514b0618ca8f4e1750f0defef99f7e1fd065e03","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-05T16:35:10Z","title_canon_sha256":"e2f0dc77809ade763b4a62d40816d54a6a0e9d9fe2e91f30d786524505766ce5"},"schema_version":"1.0","source":{"id":"2103.03788","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.03788","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"arxiv_version","alias_value":"2103.03788v1","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.03788","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"pith_short_12","alias_value":"Q4J5CJTPZRST","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"pith_short_16","alias_value":"Q4J5CJTPZRSTRTQM","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"pith_short_8","alias_value":"Q4J5CJTP","created_at":"2026-07-05T02:20:39Z"}],"graph_snapshots":[{"event_id":"sha256:a0188463101dfa47b8d3d2b78599346d47cad8a86dc26f8bb0f95629745fd883","target":"graph","created_at":"2026-07-05T02:20:39Z","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/2103.03788/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With increased interest in adopting AI methods for clinical diagnosis, a vital step towards safe deployment of such tools is to ensure that the models not only produce accurate predictions but also do not generalize to data regimes where the training data provide no meaningful evidence. Existing approaches for ensuring the distribution of model predictions to be similar to that of the true distribution rely on explicit uncertainty estimators that are inherently hard to calibrate. In this paper, we propose to train a loss estimator alongside the predictive model, using a contrastive training ob","authors_text":"Andreas Spanias, Deepta Rajan, Jayaraman J. Thiagarajan, Vivek Narayanaswamy","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-05T16:35:10Z","title":"Loss Estimators Improve Model Generalization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.03788","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:c2c7c6e885b36b57a72fa3430d346a289c0b1b256e1d9e464e966bdbf7851e31","target":"record","created_at":"2026-07-05T02:20:39Z","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":"96a70adc023bcaa4ec0f820d6514b0618ca8f4e1750f0defef99f7e1fd065e03","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-05T16:35:10Z","title_canon_sha256":"e2f0dc77809ade763b4a62d40816d54a6a0e9d9fe2e91f30d786524505766ce5"},"schema_version":"1.0","source":{"id":"2103.03788","kind":"arxiv","version":1}},"canonical_sha256":"8713d1266fcc6538ce0cbff49e6efcf57356a7ed1cca2890458a0c1fedb57046","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8713d1266fcc6538ce0cbff49e6efcf57356a7ed1cca2890458a0c1fedb57046","first_computed_at":"2026-07-05T02:20:39.843554Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:20:39.843554Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Joiup2OfMKy/0F9MarpFEzMwjBUpo0RyVxAyntMRwOWdi9kOyec81K/0gZ4rfB5Alip2cBQo/2s1RNQIZV0wCA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:20:39.843975Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.03788","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c2c7c6e885b36b57a72fa3430d346a289c0b1b256e1d9e464e966bdbf7851e31","sha256:a0188463101dfa47b8d3d2b78599346d47cad8a86dc26f8bb0f95629745fd883"],"state_sha256":"43c21dd08332e687e68d9b53a56360703dc14c914c2719985c2b740c119b7fef"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OjMy7GYsuy560WSQ/eELgtAHf0PRulX7CsHctICbSfZ24patV/c5rEW4g2Idm90gTIldhdG7peYHyguTBiUNCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-24T06:54:02.178751Z","bundle_sha256":"4deabe27a837bf95b2599ff08ae284c35dba9017eaa7feec342430f1bb136667"}}