{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LMDILNC6YWF5W7WRIXM2SAUM47","short_pith_number":"pith:LMDILNC6","schema_version":"1.0","canonical_sha256":"5b0685b45ec58bdb7ed145d9a9028ce7c7359568462a81efe438eebb3a26998b","source":{"kind":"arxiv","id":"2409.19732","version":1},"attestation_state":"computed","paper":{"title":"Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Haoran Wang, JingHao Zheng, Tao Li, Xiaolin Huang, Xinwen Cheng, Zhehao Huang, Zhengbao He","submitted_at":"2024-09-29T15:17:33Z","abstract_excerpt":"Machine unlearning (MU) has emerged to enhance the privacy and trustworthiness of deep neural networks. Approximate MU is a practical method for large-scale models. Our investigation into approximate MU starts with identifying the steepest descent direction, minimizing the output Kullback-Leibler divergence to exact MU inside a parameters' neighborhood. This probed direction decomposes into three components: weighted forgetting gradient ascent, fine-tuning retaining gradient descent, and a weight saliency matrix. Such decomposition derived from Euclidean metric encompasses most existing gradie"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2409.19732","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-29T15:17:33Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"04a40457244b0a5914e655fddd304b30d15b8acb7a611f452c8a4f61c5f0feba","abstract_canon_sha256":"f58a5a14255a55aec24a8c5da194f56caa6aa08242156aef66c70fbbac24aa86"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:13:21.648216Z","signature_b64":"61998v0sE5YXxUVV/hyVfyM2sG+bkDsWjohUhw9RrCog4CjT7uwZzLQ5zwdwAy6YrLJI3iDxgHAmlDk4MrxvAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b0685b45ec58bdb7ed145d9a9028ce7c7359568462a81efe438eebb3a26998b","last_reissued_at":"2026-07-05T09:13:21.647769Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:13:21.647769Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Haoran Wang, JingHao Zheng, Tao Li, Xiaolin Huang, Xinwen Cheng, Zhehao Huang, Zhengbao He","submitted_at":"2024-09-29T15:17:33Z","abstract_excerpt":"Machine unlearning (MU) has emerged to enhance the privacy and trustworthiness of deep neural networks. Approximate MU is a practical method for large-scale models. Our investigation into approximate MU starts with identifying the steepest descent direction, minimizing the output Kullback-Leibler divergence to exact MU inside a parameters' neighborhood. This probed direction decomposes into three components: weighted forgetting gradient ascent, fine-tuning retaining gradient descent, and a weight saliency matrix. Such decomposition derived from Euclidean metric encompasses most existing gradie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.19732","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/2409.19732/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2409.19732","created_at":"2026-07-05T09:13:21.647827+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.19732v1","created_at":"2026-07-05T09:13:21.647827+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.19732","created_at":"2026-07-05T09:13:21.647827+00:00"},{"alias_kind":"pith_short_12","alias_value":"LMDILNC6YWF5","created_at":"2026-07-05T09:13:21.647827+00:00"},{"alias_kind":"pith_short_16","alias_value":"LMDILNC6YWF5W7WR","created_at":"2026-07-05T09:13:21.647827+00:00"},{"alias_kind":"pith_short_8","alias_value":"LMDILNC6","created_at":"2026-07-05T09:13:21.647827+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.29454","citing_title":"A Full-Pipeline Framework for Evaluating Membership Inference Attacks in Machine Learning","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LMDILNC6YWF5W7WRIXM2SAUM47","json":"https://pith.science/pith/LMDILNC6YWF5W7WRIXM2SAUM47.json","graph_json":"https://pith.science/api/pith-number/LMDILNC6YWF5W7WRIXM2SAUM47/graph.json","events_json":"https://pith.science/api/pith-number/LMDILNC6YWF5W7WRIXM2SAUM47/events.json","paper":"https://pith.science/paper/LMDILNC6"},"agent_actions":{"view_html":"https://pith.science/pith/LMDILNC6YWF5W7WRIXM2SAUM47","download_json":"https://pith.science/pith/LMDILNC6YWF5W7WRIXM2SAUM47.json","view_paper":"https://pith.science/paper/LMDILNC6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.19732&json=true","fetch_graph":"https://pith.science/api/pith-number/LMDILNC6YWF5W7WRIXM2SAUM47/graph.json","fetch_events":"https://pith.science/api/pith-number/LMDILNC6YWF5W7WRIXM2SAUM47/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LMDILNC6YWF5W7WRIXM2SAUM47/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LMDILNC6YWF5W7WRIXM2SAUM47/action/storage_attestation","attest_author":"https://pith.science/pith/LMDILNC6YWF5W7WRIXM2SAUM47/action/author_attestation","sign_citation":"https://pith.science/pith/LMDILNC6YWF5W7WRIXM2SAUM47/action/citation_signature","submit_replication":"https://pith.science/pith/LMDILNC6YWF5W7WRIXM2SAUM47/action/replication_record"}},"created_at":"2026-07-05T09:13:21.647827+00:00","updated_at":"2026-07-05T09:13:21.647827+00:00"}