{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:2Q3FBREEPZA3EMH3RZFB25HOZB","short_pith_number":"pith:2Q3FBREE","canonical_record":{"source":{"id":"2310.09250","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-13T17:06:34Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"44c2a8d8b5d5190dfa8dc9337d44a41ba046e2fcadbd25387b1fc6d7b1fa028e","abstract_canon_sha256":"6382e2ec19d9de3f0b3cceaa8c26d5421969a4296732f82959629b702fb23910"},"schema_version":"1.0"},"canonical_sha256":"d43650c4847e41b230fb8e4a1d74eec86567920de575cabee9b4e4819fc801fc","source":{"kind":"arxiv","id":"2310.09250","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.09250","created_at":"2026-07-05T07:00:41Z"},{"alias_kind":"arxiv_version","alias_value":"2310.09250v1","created_at":"2026-07-05T07:00:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.09250","created_at":"2026-07-05T07:00:41Z"},{"alias_kind":"pith_short_12","alias_value":"2Q3FBREEPZA3","created_at":"2026-07-05T07:00:41Z"},{"alias_kind":"pith_short_16","alias_value":"2Q3FBREEPZA3EMH3","created_at":"2026-07-05T07:00:41Z"},{"alias_kind":"pith_short_8","alias_value":"2Q3FBREE","created_at":"2026-07-05T07:00:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:2Q3FBREEPZA3EMH3RZFB25HOZB","target":"record","payload":{"canonical_record":{"source":{"id":"2310.09250","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-13T17:06:34Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"44c2a8d8b5d5190dfa8dc9337d44a41ba046e2fcadbd25387b1fc6d7b1fa028e","abstract_canon_sha256":"6382e2ec19d9de3f0b3cceaa8c26d5421969a4296732f82959629b702fb23910"},"schema_version":"1.0"},"canonical_sha256":"d43650c4847e41b230fb8e4a1d74eec86567920de575cabee9b4e4819fc801fc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:00:41.876942Z","signature_b64":"DS9F0dOqTy7mYd3bHU9Qvk8/nHoS3defH+FqsI+32s9q5T5l8NI2Ez/4O2IOSOHcxuW4oXVEOz9fpmpBEIk6Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d43650c4847e41b230fb8e4a1d74eec86567920de575cabee9b4e4819fc801fc","last_reissued_at":"2026-07-05T07:00:41.876505Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:00:41.876505Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.09250","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":"2026-07-05T07:00:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tUQH568+8SPogTYhlNWr08AAHdOQtpblqVTtBEvC2hptJxNtlCLaBR9kErpcTjE2DUl1GrNr0UJf51FW1T6UAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T14:37:04.935692Z"},"content_sha256":"c5c5c5fab24e09d4deeadc36dadd27ace9787a7e3d041b5bf1d0496ac97d1ac7","schema_version":"1.0","event_id":"sha256:c5c5c5fab24e09d4deeadc36dadd27ace9787a7e3d041b5bf1d0496ac97d1ac7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:2Q3FBREEPZA3EMH3RZFB25HOZB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"It's an Alignment, Not a Trade-off: Revisiting Bias and Variance in Deep Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chong You, Lin Chen, Michal Lukasik, Sanjiv Kumar, Wittawat Jitkrittum","submitted_at":"2023-10-13T17:06:34Z","abstract_excerpt":"Classical wisdom in machine learning holds that the generalization error can be decomposed into bias and variance, and these two terms exhibit a \\emph{trade-off}. However, in this paper, we show that for an ensemble of deep learning based classification models, bias and variance are \\emph{aligned} at a sample level, where squared bias is approximately \\emph{equal} to variance for correctly classified sample points. We present empirical evidence confirming this phenomenon in a variety of deep learning models and datasets. Moreover, we study this phenomenon from two theoretical perspectives: cal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.09250","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/2310.09250/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":"2026-07-05T07:00:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3GJq7ejYQkAu4UN6JzAJ8PEQBNSCBrmDZECouduiZIjP5B78TAqdY+G/Ymc5KnC7t9YcI4g1m7HXH1DJE5mLCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T14:37:04.936096Z"},"content_sha256":"334ab194966595ab54ee019f8205188cfb5f2df77bf6909e66f8b856491b448b","schema_version":"1.0","event_id":"sha256:334ab194966595ab54ee019f8205188cfb5f2df77bf6909e66f8b856491b448b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2Q3FBREEPZA3EMH3RZFB25HOZB/bundle.json","state_url":"https://pith.science/pith/2Q3FBREEPZA3EMH3RZFB25HOZB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2Q3FBREEPZA3EMH3RZFB25HOZB/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":"2026-07-05T14:37:04Z","links":{"resolver":"https://pith.science/pith/2Q3FBREEPZA3EMH3RZFB25HOZB","bundle":"https://pith.science/pith/2Q3FBREEPZA3EMH3RZFB25HOZB/bundle.json","state":"https://pith.science/pith/2Q3FBREEPZA3EMH3RZFB25HOZB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2Q3FBREEPZA3EMH3RZFB25HOZB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:2Q3FBREEPZA3EMH3RZFB25HOZB","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":"6382e2ec19d9de3f0b3cceaa8c26d5421969a4296732f82959629b702fb23910","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-13T17:06:34Z","title_canon_sha256":"44c2a8d8b5d5190dfa8dc9337d44a41ba046e2fcadbd25387b1fc6d7b1fa028e"},"schema_version":"1.0","source":{"id":"2310.09250","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.09250","created_at":"2026-07-05T07:00:41Z"},{"alias_kind":"arxiv_version","alias_value":"2310.09250v1","created_at":"2026-07-05T07:00:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.09250","created_at":"2026-07-05T07:00:41Z"},{"alias_kind":"pith_short_12","alias_value":"2Q3FBREEPZA3","created_at":"2026-07-05T07:00:41Z"},{"alias_kind":"pith_short_16","alias_value":"2Q3FBREEPZA3EMH3","created_at":"2026-07-05T07:00:41Z"},{"alias_kind":"pith_short_8","alias_value":"2Q3FBREE","created_at":"2026-07-05T07:00:41Z"}],"graph_snapshots":[{"event_id":"sha256:334ab194966595ab54ee019f8205188cfb5f2df77bf6909e66f8b856491b448b","target":"graph","created_at":"2026-07-05T07:00:41Z","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/2310.09250/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Classical wisdom in machine learning holds that the generalization error can be decomposed into bias and variance, and these two terms exhibit a \\emph{trade-off}. However, in this paper, we show that for an ensemble of deep learning based classification models, bias and variance are \\emph{aligned} at a sample level, where squared bias is approximately \\emph{equal} to variance for correctly classified sample points. We present empirical evidence confirming this phenomenon in a variety of deep learning models and datasets. Moreover, we study this phenomenon from two theoretical perspectives: cal","authors_text":"Chong You, Lin Chen, Michal Lukasik, Sanjiv Kumar, Wittawat Jitkrittum","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-13T17:06:34Z","title":"It's an Alignment, Not a Trade-off: Revisiting Bias and Variance in Deep Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.09250","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:c5c5c5fab24e09d4deeadc36dadd27ace9787a7e3d041b5bf1d0496ac97d1ac7","target":"record","created_at":"2026-07-05T07:00:41Z","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":"6382e2ec19d9de3f0b3cceaa8c26d5421969a4296732f82959629b702fb23910","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-13T17:06:34Z","title_canon_sha256":"44c2a8d8b5d5190dfa8dc9337d44a41ba046e2fcadbd25387b1fc6d7b1fa028e"},"schema_version":"1.0","source":{"id":"2310.09250","kind":"arxiv","version":1}},"canonical_sha256":"d43650c4847e41b230fb8e4a1d74eec86567920de575cabee9b4e4819fc801fc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d43650c4847e41b230fb8e4a1d74eec86567920de575cabee9b4e4819fc801fc","first_computed_at":"2026-07-05T07:00:41.876505Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:00:41.876505Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DS9F0dOqTy7mYd3bHU9Qvk8/nHoS3defH+FqsI+32s9q5T5l8NI2Ez/4O2IOSOHcxuW4oXVEOz9fpmpBEIk6Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:00:41.876942Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.09250","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c5c5c5fab24e09d4deeadc36dadd27ace9787a7e3d041b5bf1d0496ac97d1ac7","sha256:334ab194966595ab54ee019f8205188cfb5f2df77bf6909e66f8b856491b448b"],"state_sha256":"5a93d98d57b031af0b50893757fc134487ef63710029b9416ff878308b6bc69f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QfcAtD6gqvsNU33tIipgJfSBr40kqaR0+lJI+f1aWMQcIsvCqV+Ab7zv0NMZ7zp/tRPU+eb08/ok/nXofPzvCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T14:37:04.938437Z","bundle_sha256":"74423e9c7c076b8eabf381c3b2763e7c22a32aab7d3da49dd8581c360a767377"}}