{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:TOXJ65IJVDUNMQVAFU6GM4ZCFX","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":"ccc1c26e45698781b5179c7fe9a0c4a65c91c3edff2216ab5c74356c34ec2b07","cross_cats_sorted":["cs.IT","cs.NE","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-26T16:40:59Z","title_canon_sha256":"9a57856a7c6985b26b801186bef71e25de9b66d1fade78c69c926881fef942c1"},"schema_version":"1.0","source":{"id":"2501.15592","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.15592","created_at":"2026-07-05T10:05:49Z"},{"alias_kind":"arxiv_version","alias_value":"2501.15592v1","created_at":"2026-07-05T10:05:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.15592","created_at":"2026-07-05T10:05:49Z"},{"alias_kind":"pith_short_12","alias_value":"TOXJ65IJVDUN","created_at":"2026-07-05T10:05:49Z"},{"alias_kind":"pith_short_16","alias_value":"TOXJ65IJVDUNMQVA","created_at":"2026-07-05T10:05:49Z"},{"alias_kind":"pith_short_8","alias_value":"TOXJ65IJ","created_at":"2026-07-05T10:05:49Z"}],"graph_snapshots":[{"event_id":"sha256:5084f310c31e27829b9fee46f8e71104be29b83d3b3de7eb3fafda417d2248dd","target":"graph","created_at":"2026-07-05T10:05:49Z","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/2501.15592/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Iterative magnitude pruning methods (IMPs), proven to be successful in reducing the number of insignificant nodes in over-parameterized deep neural networks (DNNs), have been getting an enormous amount of attention with the rapid deployment of DNNs into cutting-edge technologies with computation and memory constraints. Despite IMPs popularity in pruning networks, a fundamental limitation of existing IMP algorithms is the significant training time required for each pruning iteration. Our paper introduces a novel \\textit{stopping criterion} for IMPs that monitors information and gradient flows b","authors_text":"Salimeh Yasaei Sekeh, Soheil Gharatappeh","cross_cats":["cs.IT","cs.NE","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-26T16:40:59Z","title":"Information Consistent Pruning: How to Efficiently Search for Sparse Networks?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.15592","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":"sha256:11332361216266c30a2464309f1d6e076ace303876fd9795a67ef41e22de92bb","target":"record","created_at":"2026-07-05T10:05:49Z","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":"ccc1c26e45698781b5179c7fe9a0c4a65c91c3edff2216ab5c74356c34ec2b07","cross_cats_sorted":["cs.IT","cs.NE","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-26T16:40:59Z","title_canon_sha256":"9a57856a7c6985b26b801186bef71e25de9b66d1fade78c69c926881fef942c1"},"schema_version":"1.0","source":{"id":"2501.15592","kind":"arxiv","version":1}},"canonical_sha256":"9bae9f7509a8e8d642a02d3c6673222dfa6e2f0f15cf75bd980db52164e1951b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9bae9f7509a8e8d642a02d3c6673222dfa6e2f0f15cf75bd980db52164e1951b","first_computed_at":"2026-07-05T10:05:49.548172Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:05:49.548172Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JcI5hUGURviMpP4F9VqVP4CLVs/cmrocxtPisDhVo/g6ZlJb4Aq6BexbVkDtIO9gD8YMHL3V08XhL5RIYrcTCA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:05:49.548686Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.15592","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:11332361216266c30a2464309f1d6e076ace303876fd9795a67ef41e22de92bb","sha256:5084f310c31e27829b9fee46f8e71104be29b83d3b3de7eb3fafda417d2248dd"],"state_sha256":"dbe762cdfc4b2bcec331fb574ab16c5507aae7829706a9fecf1e14d750e4be98"}