{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:OJG6NL5BBZKNC6GJU222CQL2F3","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":"cf9e21e19f8153857a8ca966133a331dea95b5e2ebd8c56c68f8585ea7c38214","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-29T10:45:06Z","title_canon_sha256":"e05ab563f2e0943d032aa0ecd1a7674656fc293150ebf08fc2f5d4c3e11a60c1"},"schema_version":"1.0","source":{"id":"2405.18979","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.18979","created_at":"2026-07-05T09:39:55Z"},{"alias_kind":"arxiv_version","alias_value":"2405.18979v3","created_at":"2026-07-05T09:39:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.18979","created_at":"2026-07-05T09:39:55Z"},{"alias_kind":"pith_short_12","alias_value":"OJG6NL5BBZKN","created_at":"2026-07-05T09:39:55Z"},{"alias_kind":"pith_short_16","alias_value":"OJG6NL5BBZKNC6GJ","created_at":"2026-07-05T09:39:55Z"},{"alias_kind":"pith_short_8","alias_value":"OJG6NL5B","created_at":"2026-07-05T09:39:55Z"}],"graph_snapshots":[{"event_id":"sha256:c4fe201d285f5e2c3e26c25411360358aa877b97730df2ceca9adb31f27454ba","target":"graph","created_at":"2026-07-05T09:39:55Z","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/2405.18979/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Leveraging the models' outputs, specifically the logits, is a common approach to estimating the test accuracy of a pre-trained neural network on out-of-distribution (OOD) samples without requiring access to the corresponding ground truth labels. Despite their ease of implementation and computational efficiency, current logit-based methods are vulnerable to overconfidence issues, leading to prediction bias, especially under the natural shift. In this work, we first study the relationship between logits and generalization performance from the view of low-density separation assumption. Our findin","authors_text":"Ambroise Odonnat, Bo An, Jianfeng Zhang, Renchunzi Xie, Vasilii Feofanov, Weijian Deng","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-29T10:45:06Z","title":"MANO: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.18979","kind":"arxiv","version":3},"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:b9ed16eeef521ddfdddcb9f533e47c7665ef788d1296c6a9f9cc9824ae007dba","target":"record","created_at":"2026-07-05T09:39:55Z","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":"cf9e21e19f8153857a8ca966133a331dea95b5e2ebd8c56c68f8585ea7c38214","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-29T10:45:06Z","title_canon_sha256":"e05ab563f2e0943d032aa0ecd1a7674656fc293150ebf08fc2f5d4c3e11a60c1"},"schema_version":"1.0","source":{"id":"2405.18979","kind":"arxiv","version":3}},"canonical_sha256":"724de6afa10e54d178c9a6b5a1417a2efa4085e8cb5992ba0d58e4df6bd20545","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"724de6afa10e54d178c9a6b5a1417a2efa4085e8cb5992ba0d58e4df6bd20545","first_computed_at":"2026-07-05T09:39:55.113258Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:39:55.113258Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uGiPf9K3ItjzGn61vFkuo3xRrQhEIIUV+E2UPC2J3ItfIo0wBJ7452Us87ChOHsnLg8HPvkPpGAedh7DILvkBA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:39:55.113757Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.18979","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b9ed16eeef521ddfdddcb9f533e47c7665ef788d1296c6a9f9cc9824ae007dba","sha256:c4fe201d285f5e2c3e26c25411360358aa877b97730df2ceca9adb31f27454ba"],"state_sha256":"db77b93a5df8672abad068a9596ec632dd74dd4e64b0bc5d1d6ea516bd122043"}