{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:IRHF5H7JTPLHE7WUFMVDADQ3LC","short_pith_number":"pith:IRHF5H7J","canonical_record":{"source":{"id":"2506.23280","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-29T15:12:50Z","cross_cats_sorted":[],"title_canon_sha256":"5d061f681fc27baf88e6fda13cd55fdabec67a5c1c57906e7b26f8cf5cc4eae4","abstract_canon_sha256":"e8f9a6b8068cf47884150aa122144cc3ff63642a6a8c8c3b928a73cc908a78a9"},"schema_version":"1.0"},"canonical_sha256":"444e5e9fe99bd6727ed42b2a300e1b58aa7ed18701d27c24cad64a69886598e3","source":{"kind":"arxiv","id":"2506.23280","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.23280","created_at":"2026-07-05T11:29:19Z"},{"alias_kind":"arxiv_version","alias_value":"2506.23280v1","created_at":"2026-07-05T11:29:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.23280","created_at":"2026-07-05T11:29:19Z"},{"alias_kind":"pith_short_12","alias_value":"IRHF5H7JTPLH","created_at":"2026-07-05T11:29:19Z"},{"alias_kind":"pith_short_16","alias_value":"IRHF5H7JTPLHE7WU","created_at":"2026-07-05T11:29:19Z"},{"alias_kind":"pith_short_8","alias_value":"IRHF5H7J","created_at":"2026-07-05T11:29:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:IRHF5H7JTPLHE7WUFMVDADQ3LC","target":"record","payload":{"canonical_record":{"source":{"id":"2506.23280","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-29T15:12:50Z","cross_cats_sorted":[],"title_canon_sha256":"5d061f681fc27baf88e6fda13cd55fdabec67a5c1c57906e7b26f8cf5cc4eae4","abstract_canon_sha256":"e8f9a6b8068cf47884150aa122144cc3ff63642a6a8c8c3b928a73cc908a78a9"},"schema_version":"1.0"},"canonical_sha256":"444e5e9fe99bd6727ed42b2a300e1b58aa7ed18701d27c24cad64a69886598e3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:29:19.132229Z","signature_b64":"NVzxA4v1rEYViwNMnJvcLtlPTyRbuTkTpftBMD6NjgMqCuFiH2TSOoJWWnHiqPMbtB5O854RNNenkGdyG2Q4CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"444e5e9fe99bd6727ed42b2a300e1b58aa7ed18701d27c24cad64a69886598e3","last_reissued_at":"2026-07-05T11:29:19.131684Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:29:19.131684Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.23280","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":"2026-07-05T11:29:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SIQINZvqQcjgYfoYhLtSJ9muAb/ayh39xA6Me0GjXcD4EnqUXVdlzIvDf72L4L/NDRk1C+uT47SeFujFQuDDBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T04:55:57.473615Z"},"content_sha256":"be3490948a51228af281251f98303c87e393b2974f26269b7b8d1e49788ab9f3","schema_version":"1.0","event_id":"sha256:be3490948a51228af281251f98303c87e393b2974f26269b7b8d1e49788ab9f3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:IRHF5H7JTPLHE7WUFMVDADQ3LC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"BAPE: Learning an Explicit Bayes Classifier for Long-tailed Visual Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chaoqun Du, Gao Huang, Shiji Song, Yulin Wang","submitted_at":"2025-06-29T15:12:50Z","abstract_excerpt":"Bayesian decision theory advocates the Bayes classifier as the optimal approach for minimizing the risk in machine learning problems. Current deep learning algorithms usually solve for the optimal classifier by \\emph{implicitly} estimating the posterior probabilities, \\emph{e.g.}, by minimizing the Softmax cross-entropy loss. This simple methodology has been proven effective for meticulously balanced academic benchmark datasets. However, it is not applicable to the long-tailed data distributions in the real world, where it leads to the gradient imbalance issue and fails to ensure the Bayes opt"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.23280","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/2506.23280/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":"2026-07-05T11:29:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"owSQVSKIvnZhmQpj6LXPNIwanTxBrKeEyJa1/ZNqmewnxWWxyJYeySu2PhDCDgUIx4Qof+3xXprhNiM9G54sDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T04:55:57.473997Z"},"content_sha256":"a460f15e4fbe8a3faec2321051305bff2f819e56faac893ff728cffac984f718","schema_version":"1.0","event_id":"sha256:a460f15e4fbe8a3faec2321051305bff2f819e56faac893ff728cffac984f718"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IRHF5H7JTPLHE7WUFMVDADQ3LC/bundle.json","state_url":"https://pith.science/pith/IRHF5H7JTPLHE7WUFMVDADQ3LC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IRHF5H7JTPLHE7WUFMVDADQ3LC/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":"2026-07-07T04:55:57Z","links":{"resolver":"https://pith.science/pith/IRHF5H7JTPLHE7WUFMVDADQ3LC","bundle":"https://pith.science/pith/IRHF5H7JTPLHE7WUFMVDADQ3LC/bundle.json","state":"https://pith.science/pith/IRHF5H7JTPLHE7WUFMVDADQ3LC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IRHF5H7JTPLHE7WUFMVDADQ3LC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IRHF5H7JTPLHE7WUFMVDADQ3LC","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":"e8f9a6b8068cf47884150aa122144cc3ff63642a6a8c8c3b928a73cc908a78a9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-29T15:12:50Z","title_canon_sha256":"5d061f681fc27baf88e6fda13cd55fdabec67a5c1c57906e7b26f8cf5cc4eae4"},"schema_version":"1.0","source":{"id":"2506.23280","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.23280","created_at":"2026-07-05T11:29:19Z"},{"alias_kind":"arxiv_version","alias_value":"2506.23280v1","created_at":"2026-07-05T11:29:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.23280","created_at":"2026-07-05T11:29:19Z"},{"alias_kind":"pith_short_12","alias_value":"IRHF5H7JTPLH","created_at":"2026-07-05T11:29:19Z"},{"alias_kind":"pith_short_16","alias_value":"IRHF5H7JTPLHE7WU","created_at":"2026-07-05T11:29:19Z"},{"alias_kind":"pith_short_8","alias_value":"IRHF5H7J","created_at":"2026-07-05T11:29:19Z"}],"graph_snapshots":[{"event_id":"sha256:a460f15e4fbe8a3faec2321051305bff2f819e56faac893ff728cffac984f718","target":"graph","created_at":"2026-07-05T11:29:19Z","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/2506.23280/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bayesian decision theory advocates the Bayes classifier as the optimal approach for minimizing the risk in machine learning problems. Current deep learning algorithms usually solve for the optimal classifier by \\emph{implicitly} estimating the posterior probabilities, \\emph{e.g.}, by minimizing the Softmax cross-entropy loss. This simple methodology has been proven effective for meticulously balanced academic benchmark datasets. However, it is not applicable to the long-tailed data distributions in the real world, where it leads to the gradient imbalance issue and fails to ensure the Bayes opt","authors_text":"Chaoqun Du, Gao Huang, Shiji Song, Yulin Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-29T15:12:50Z","title":"BAPE: Learning an Explicit Bayes Classifier for Long-tailed Visual Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.23280","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:be3490948a51228af281251f98303c87e393b2974f26269b7b8d1e49788ab9f3","target":"record","created_at":"2026-07-05T11:29:19Z","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":"e8f9a6b8068cf47884150aa122144cc3ff63642a6a8c8c3b928a73cc908a78a9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-29T15:12:50Z","title_canon_sha256":"5d061f681fc27baf88e6fda13cd55fdabec67a5c1c57906e7b26f8cf5cc4eae4"},"schema_version":"1.0","source":{"id":"2506.23280","kind":"arxiv","version":1}},"canonical_sha256":"444e5e9fe99bd6727ed42b2a300e1b58aa7ed18701d27c24cad64a69886598e3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"444e5e9fe99bd6727ed42b2a300e1b58aa7ed18701d27c24cad64a69886598e3","first_computed_at":"2026-07-05T11:29:19.131684Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:29:19.131684Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NVzxA4v1rEYViwNMnJvcLtlPTyRbuTkTpftBMD6NjgMqCuFiH2TSOoJWWnHiqPMbtB5O854RNNenkGdyG2Q4CA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:29:19.132229Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.23280","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:be3490948a51228af281251f98303c87e393b2974f26269b7b8d1e49788ab9f3","sha256:a460f15e4fbe8a3faec2321051305bff2f819e56faac893ff728cffac984f718"],"state_sha256":"8b0b92a46748a0c0dbcdda7c6f2f4ffddc269403a3665de638615bf53bc46c4b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/oo7KkLW6pFPq7a8jgBAAEdnJDDrA8ZZEVO2PXpRG22AXSpbBQ0sTi4O2SmMdeAiY2CL0836pkITG14NpkIcBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T04:55:57.477009Z","bundle_sha256":"0847ed4b180708abcedef21f56108d7859c039d46b410970f847ca1127bad61c"}}