{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:YBECPFO45Z6JECS3P5NMFWP7UT","short_pith_number":"pith:YBECPFO4","schema_version":"1.0","canonical_sha256":"c0482795dcee7c920a5b7f5ac2d9ffa4fd508aa0b9c921385a6417450e59c221","source":{"kind":"arxiv","id":"2110.08764","version":1},"attestation_state":"computed","paper":{"title":"S-Cyc: A Learning Rate Schedule for Iterative Pruning of ReLU-based Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.LG","authors_text":"Chong Min John Tan, Mehul Motani, Shiyu Liu","submitted_at":"2021-10-17T08:58:08Z","abstract_excerpt":"We explore a new perspective on adapting the learning rate (LR) schedule to improve the performance of the ReLU-based network as it is iteratively pruned. Our work and contribution consist of four parts: (i) We find that, as the ReLU-based network is iteratively pruned, the distribution of weight gradients tends to become narrower. This leads to the finding that as the network becomes more sparse, a larger value of LR should be used to train the pruned network. (ii) Motivated by this finding, we propose a novel LR schedule, called S-Cyclical (S-Cyc) which adapts the conventional cyclical LR sc"},"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":"2110.08764","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-17T08:58:08Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"1b46da170596c781fadf384147cd04b87d269a2514bef9694443baa476bbd488","abstract_canon_sha256":"9f6f2494519bfc96396b28c9fa0619f5282990d04b588d14e4e0466b3001588b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:23:20.252366Z","signature_b64":"6CoV7p/k0DJU1cA4PlDVv53baXdDaCOX3C7HjuOkOnjP9kkQsSkNTsTSofeye2PwuocbdtlC0cFYCYTUoc6mBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c0482795dcee7c920a5b7f5ac2d9ffa4fd508aa0b9c921385a6417450e59c221","last_reissued_at":"2026-07-05T03:23:20.252028Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:23:20.252028Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"S-Cyc: A Learning Rate Schedule for Iterative Pruning of ReLU-based Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.LG","authors_text":"Chong Min John Tan, Mehul Motani, Shiyu Liu","submitted_at":"2021-10-17T08:58:08Z","abstract_excerpt":"We explore a new perspective on adapting the learning rate (LR) schedule to improve the performance of the ReLU-based network as it is iteratively pruned. Our work and contribution consist of four parts: (i) We find that, as the ReLU-based network is iteratively pruned, the distribution of weight gradients tends to become narrower. This leads to the finding that as the network becomes more sparse, a larger value of LR should be used to train the pruned network. (ii) Motivated by this finding, we propose a novel LR schedule, called S-Cyclical (S-Cyc) which adapts the conventional cyclical LR sc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.08764","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/2110.08764/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":"2110.08764","created_at":"2026-07-05T03:23:20.252080+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.08764v1","created_at":"2026-07-05T03:23:20.252080+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.08764","created_at":"2026-07-05T03:23:20.252080+00:00"},{"alias_kind":"pith_short_12","alias_value":"YBECPFO45Z6J","created_at":"2026-07-05T03:23:20.252080+00:00"},{"alias_kind":"pith_short_16","alias_value":"YBECPFO45Z6JECS3","created_at":"2026-07-05T03:23:20.252080+00:00"},{"alias_kind":"pith_short_8","alias_value":"YBECPFO4","created_at":"2026-07-05T03:23:20.252080+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.07411","citing_title":"ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks","ref_index":28,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YBECPFO45Z6JECS3P5NMFWP7UT","json":"https://pith.science/pith/YBECPFO45Z6JECS3P5NMFWP7UT.json","graph_json":"https://pith.science/api/pith-number/YBECPFO45Z6JECS3P5NMFWP7UT/graph.json","events_json":"https://pith.science/api/pith-number/YBECPFO45Z6JECS3P5NMFWP7UT/events.json","paper":"https://pith.science/paper/YBECPFO4"},"agent_actions":{"view_html":"https://pith.science/pith/YBECPFO45Z6JECS3P5NMFWP7UT","download_json":"https://pith.science/pith/YBECPFO45Z6JECS3P5NMFWP7UT.json","view_paper":"https://pith.science/paper/YBECPFO4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.08764&json=true","fetch_graph":"https://pith.science/api/pith-number/YBECPFO45Z6JECS3P5NMFWP7UT/graph.json","fetch_events":"https://pith.science/api/pith-number/YBECPFO45Z6JECS3P5NMFWP7UT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YBECPFO45Z6JECS3P5NMFWP7UT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YBECPFO45Z6JECS3P5NMFWP7UT/action/storage_attestation","attest_author":"https://pith.science/pith/YBECPFO45Z6JECS3P5NMFWP7UT/action/author_attestation","sign_citation":"https://pith.science/pith/YBECPFO45Z6JECS3P5NMFWP7UT/action/citation_signature","submit_replication":"https://pith.science/pith/YBECPFO45Z6JECS3P5NMFWP7UT/action/replication_record"}},"created_at":"2026-07-05T03:23:20.252080+00:00","updated_at":"2026-07-05T03:23:20.252080+00:00"}