{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:CFBKP67AZRLRBCQFKAUASIVTG2","short_pith_number":"pith:CFBKP67A","canonical_record":{"source":{"id":"2211.10285","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-18T15:17:28Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"6677fe7c0234ec2c64fa82b065e815a598830cea86a1b26ba08a60a329734dd6","abstract_canon_sha256":"ae23142cb00c283a984bd134b44b8c006f0260696717e3bd813d7a9943ea0027"},"schema_version":"1.0"},"canonical_sha256":"1142a7fbe0cc57108a0550280922b336a03cabf5752297ae8481c7df80a4e9ab","source":{"kind":"arxiv","id":"2211.10285","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.10285","created_at":"2026-07-05T09:36:35Z"},{"alias_kind":"arxiv_version","alias_value":"2211.10285v2","created_at":"2026-07-05T09:36:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.10285","created_at":"2026-07-05T09:36:35Z"},{"alias_kind":"pith_short_12","alias_value":"CFBKP67AZRLR","created_at":"2026-07-05T09:36:35Z"},{"alias_kind":"pith_short_16","alias_value":"CFBKP67AZRLRBCQF","created_at":"2026-07-05T09:36:35Z"},{"alias_kind":"pith_short_8","alias_value":"CFBKP67A","created_at":"2026-07-05T09:36:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:CFBKP67AZRLRBCQFKAUASIVTG2","target":"record","payload":{"canonical_record":{"source":{"id":"2211.10285","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-18T15:17:28Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"6677fe7c0234ec2c64fa82b065e815a598830cea86a1b26ba08a60a329734dd6","abstract_canon_sha256":"ae23142cb00c283a984bd134b44b8c006f0260696717e3bd813d7a9943ea0027"},"schema_version":"1.0"},"canonical_sha256":"1142a7fbe0cc57108a0550280922b336a03cabf5752297ae8481c7df80a4e9ab","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:36:35.043212Z","signature_b64":"Q9/mcRO6uk8ePnx9uzr52XGkUGn11ZagRDSEkXENVi8n/VgripN90mQiETAilnEfnlVHHtnaXy4lYyLvKx/FCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1142a7fbe0cc57108a0550280922b336a03cabf5752297ae8481c7df80a4e9ab","last_reissued_at":"2026-07-05T09:36:35.042778Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:36:35.042778Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2211.10285","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:36:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uj5KHQssrgLkaQikBRX5dCnscTqGMfVrhjeNrAAHAkg1momRaf7EhqbXrmB+LeR6Tq1aZzBC8Z/5G9M5wj3QCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T01:02:39.238054Z"},"content_sha256":"cb28da4b7640f203f984430c565f480065531d2c48163bc33892f63edef00e7e","schema_version":"1.0","event_id":"sha256:cb28da4b7640f203f984430c565f480065531d2c48163bc33892f63edef00e7e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:CFBKP67AZRLRBCQFKAUASIVTG2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Fair Loss Function for Network Pruning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.LG","authors_text":"Alexander Wong, Robbie Meyer","submitted_at":"2022-11-18T15:17:28Z","abstract_excerpt":"Model pruning can enable the deployment of neural networks in environments with resource constraints. While pruning may have a small effect on the overall performance of the model, it can exacerbate existing biases into the model such that subsets of samples see significantly degraded performance. In this paper, we introduce the performance weighted loss function, a simple modified cross-entropy loss function that can be used to limit the introduction of biases during pruning. Experiments using the CelebA, Fitzpatrick17k and CIFAR-10 datasets demonstrate that the proposed method is a simple an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.10285","kind":"arxiv","version":2},"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/2211.10285/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":"2026-07-05T09:36:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9uusuwLi1sD58kNnSwRmghf49s0zAxOyyKliEYVHD5Nowh3qdF6JOGSJfPqUw37rc5xnJ31G8IFHmvpErGp9Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T01:02:39.238656Z"},"content_sha256":"cdd3beb37e0f8d0ce1eb9012d17de7bbff6149abcecbf6a0cbbcc37fe37d118a","schema_version":"1.0","event_id":"sha256:cdd3beb37e0f8d0ce1eb9012d17de7bbff6149abcecbf6a0cbbcc37fe37d118a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CFBKP67AZRLRBCQFKAUASIVTG2/bundle.json","state_url":"https://pith.science/pith/CFBKP67AZRLRBCQFKAUASIVTG2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CFBKP67AZRLRBCQFKAUASIVTG2/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":"2026-08-09T01:02:39Z","links":{"resolver":"https://pith.science/pith/CFBKP67AZRLRBCQFKAUASIVTG2","bundle":"https://pith.science/pith/CFBKP67AZRLRBCQFKAUASIVTG2/bundle.json","state":"https://pith.science/pith/CFBKP67AZRLRBCQFKAUASIVTG2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CFBKP67AZRLRBCQFKAUASIVTG2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:CFBKP67AZRLRBCQFKAUASIVTG2","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":"ae23142cb00c283a984bd134b44b8c006f0260696717e3bd813d7a9943ea0027","cross_cats_sorted":["cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-18T15:17:28Z","title_canon_sha256":"6677fe7c0234ec2c64fa82b065e815a598830cea86a1b26ba08a60a329734dd6"},"schema_version":"1.0","source":{"id":"2211.10285","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.10285","created_at":"2026-07-05T09:36:35Z"},{"alias_kind":"arxiv_version","alias_value":"2211.10285v2","created_at":"2026-07-05T09:36:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.10285","created_at":"2026-07-05T09:36:35Z"},{"alias_kind":"pith_short_12","alias_value":"CFBKP67AZRLR","created_at":"2026-07-05T09:36:35Z"},{"alias_kind":"pith_short_16","alias_value":"CFBKP67AZRLRBCQF","created_at":"2026-07-05T09:36:35Z"},{"alias_kind":"pith_short_8","alias_value":"CFBKP67A","created_at":"2026-07-05T09:36:35Z"}],"graph_snapshots":[{"event_id":"sha256:cdd3beb37e0f8d0ce1eb9012d17de7bbff6149abcecbf6a0cbbcc37fe37d118a","target":"graph","created_at":"2026-07-05T09:36:35Z","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/2211.10285/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Model pruning can enable the deployment of neural networks in environments with resource constraints. While pruning may have a small effect on the overall performance of the model, it can exacerbate existing biases into the model such that subsets of samples see significantly degraded performance. In this paper, we introduce the performance weighted loss function, a simple modified cross-entropy loss function that can be used to limit the introduction of biases during pruning. Experiments using the CelebA, Fitzpatrick17k and CIFAR-10 datasets demonstrate that the proposed method is a simple an","authors_text":"Alexander Wong, Robbie Meyer","cross_cats":["cs.CY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-18T15:17:28Z","title":"A Fair Loss Function for Network Pruning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.10285","kind":"arxiv","version":2},"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:cb28da4b7640f203f984430c565f480065531d2c48163bc33892f63edef00e7e","target":"record","created_at":"2026-07-05T09:36:35Z","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":"ae23142cb00c283a984bd134b44b8c006f0260696717e3bd813d7a9943ea0027","cross_cats_sorted":["cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-18T15:17:28Z","title_canon_sha256":"6677fe7c0234ec2c64fa82b065e815a598830cea86a1b26ba08a60a329734dd6"},"schema_version":"1.0","source":{"id":"2211.10285","kind":"arxiv","version":2}},"canonical_sha256":"1142a7fbe0cc57108a0550280922b336a03cabf5752297ae8481c7df80a4e9ab","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1142a7fbe0cc57108a0550280922b336a03cabf5752297ae8481c7df80a4e9ab","first_computed_at":"2026-07-05T09:36:35.042778Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:36:35.042778Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Q9/mcRO6uk8ePnx9uzr52XGkUGn11ZagRDSEkXENVi8n/VgripN90mQiETAilnEfnlVHHtnaXy4lYyLvKx/FCg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:36:35.043212Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.10285","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cb28da4b7640f203f984430c565f480065531d2c48163bc33892f63edef00e7e","sha256:cdd3beb37e0f8d0ce1eb9012d17de7bbff6149abcecbf6a0cbbcc37fe37d118a"],"state_sha256":"ace8306dbfdb41b4d8885c7c905f6cf27f036b7bba151c611157b4ce77432e70"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gPyIIWPp3CNwkKaBIkgsPkYqyl2lisI+uOUPt/uP+IjCZokZxDVMMpcgnSo0F9OrbwMrsVhSetNuCMOyXzBLCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T01:02:39.241873Z","bundle_sha256":"fc92e2d7ecaae180f6e0e79abeee3d828a8ff0b1504b0c6a3754b878d5c162cf"}}