{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:74EVPETJZTNTVMDLOORA5KDQTB","short_pith_number":"pith:74EVPETJ","schema_version":"1.0","canonical_sha256":"ff09579269ccdb3ab06b73a20ea8709847e38e0057a89144175482b4cf673a70","source":{"kind":"arxiv","id":"1806.06457","version":2},"attestation_state":"computed","paper":{"title":"Fast Convex Pruning of Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Afshin Abdi, Alireza Aghasi, Justin Romberg","submitted_at":"2018-06-17T22:16:18Z","abstract_excerpt":"We develop a fast, tractable technique called Net-Trim for simplifying a trained neural network. The method is a convex post-processing module, which prunes (sparsifies) a trained network layer by layer, while preserving the internal responses. We present a comprehensive analysis of Net-Trim from both the algorithmic and sample complexity standpoints, centered on a fast, scalable convex optimization program. Our analysis includes consistency results between the initial and retrained models before and after Net-Trim application and guarantees on the number of training samples needed to discover"},"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":"1806.06457","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-06-17T22:16:18Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"d64d0601cb52b281171f6417a101550cf4e9a9391bf1cfc4d770eb78296c221f","abstract_canon_sha256":"08311f4af7b91c59917eebd92182d73fbe9f47ae46878e69ca2068bdc5eed170"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:41.734174Z","signature_b64":"outAKS3BSf3Ob1WmjJfQeGNSGX80v+2Trwel3OkVHaMy3p1fxSIUt0e4y+rc+FVO+rEjVD8gRCwV9z70MwHmBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff09579269ccdb3ab06b73a20ea8709847e38e0057a89144175482b4cf673a70","last_reissued_at":"2026-05-17T23:52:41.733574Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:41.733574Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast Convex Pruning of Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Afshin Abdi, Alireza Aghasi, Justin Romberg","submitted_at":"2018-06-17T22:16:18Z","abstract_excerpt":"We develop a fast, tractable technique called Net-Trim for simplifying a trained neural network. The method is a convex post-processing module, which prunes (sparsifies) a trained network layer by layer, while preserving the internal responses. We present a comprehensive analysis of Net-Trim from both the algorithmic and sample complexity standpoints, centered on a fast, scalable convex optimization program. Our analysis includes consistency results between the initial and retrained models before and after Net-Trim application and guarantees on the number of training samples needed to discover"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.06457","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":""},"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":"1806.06457","created_at":"2026-05-17T23:52:41.733698+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.06457v2","created_at":"2026-05-17T23:52:41.733698+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.06457","created_at":"2026-05-17T23:52:41.733698+00:00"},{"alias_kind":"pith_short_12","alias_value":"74EVPETJZTNT","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_16","alias_value":"74EVPETJZTNTVMDL","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_8","alias_value":"74EVPETJ","created_at":"2026-05-18T12:32:11.075285+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/74EVPETJZTNTVMDLOORA5KDQTB","json":"https://pith.science/pith/74EVPETJZTNTVMDLOORA5KDQTB.json","graph_json":"https://pith.science/api/pith-number/74EVPETJZTNTVMDLOORA5KDQTB/graph.json","events_json":"https://pith.science/api/pith-number/74EVPETJZTNTVMDLOORA5KDQTB/events.json","paper":"https://pith.science/paper/74EVPETJ"},"agent_actions":{"view_html":"https://pith.science/pith/74EVPETJZTNTVMDLOORA5KDQTB","download_json":"https://pith.science/pith/74EVPETJZTNTVMDLOORA5KDQTB.json","view_paper":"https://pith.science/paper/74EVPETJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.06457&json=true","fetch_graph":"https://pith.science/api/pith-number/74EVPETJZTNTVMDLOORA5KDQTB/graph.json","fetch_events":"https://pith.science/api/pith-number/74EVPETJZTNTVMDLOORA5KDQTB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/74EVPETJZTNTVMDLOORA5KDQTB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/74EVPETJZTNTVMDLOORA5KDQTB/action/storage_attestation","attest_author":"https://pith.science/pith/74EVPETJZTNTVMDLOORA5KDQTB/action/author_attestation","sign_citation":"https://pith.science/pith/74EVPETJZTNTVMDLOORA5KDQTB/action/citation_signature","submit_replication":"https://pith.science/pith/74EVPETJZTNTVMDLOORA5KDQTB/action/replication_record"}},"created_at":"2026-05-17T23:52:41.733698+00:00","updated_at":"2026-05-17T23:52:41.733698+00:00"}