{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:KCHC3AKSFPJDRBJPVTXLMMUKHZ","short_pith_number":"pith:KCHC3AKS","schema_version":"1.0","canonical_sha256":"508e2d81522bd238852faceeb6328a3e5d8b3c6208ba38814cba27bdad06e9f3","source":{"kind":"arxiv","id":"1905.06435","version":1},"attestation_state":"computed","paper":{"title":"Dynamic Neural Network Channel Execution for Efficient Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Pietro Lio, Simeon E. Spasov","submitted_at":"2019-05-15T21:10:28Z","abstract_excerpt":"Existing methods for reducing the computational burden of neural networks at run-time, such as parameter pruning or dynamic computational path selection, focus solely on improving computational efficiency during inference. On the other hand, in this work, we propose a novel method which reduces the memory footprint and number of computing operations required for training and inference. Our framework efficiently integrates pruning as part of the training procedure by exploring and tracking the relative importance of convolutional channels. At each training step, we select only a subset of highl"},"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":"1905.06435","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-05-15T21:10:28Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"8936ff48ae0361945c72f55002939110b2f3ab94d27b2d5faed954281712b880","abstract_canon_sha256":"bfefa93e8dfa65824c65444735a4545235c7481860d970d70d7a1eed0fb1b97b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:02.446800Z","signature_b64":"4oS5ePMzGx32rESligXEwDxiwkavYyuuvpAYz2TJo7U1DJsCtJmIemFUs/me458PiCj71UIV1xMvb1fVtGwYCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"508e2d81522bd238852faceeb6328a3e5d8b3c6208ba38814cba27bdad06e9f3","last_reissued_at":"2026-05-17T23:46:02.446238Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:02.446238Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamic Neural Network Channel Execution for Efficient Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Pietro Lio, Simeon E. Spasov","submitted_at":"2019-05-15T21:10:28Z","abstract_excerpt":"Existing methods for reducing the computational burden of neural networks at run-time, such as parameter pruning or dynamic computational path selection, focus solely on improving computational efficiency during inference. On the other hand, in this work, we propose a novel method which reduces the memory footprint and number of computing operations required for training and inference. Our framework efficiently integrates pruning as part of the training procedure by exploring and tracking the relative importance of convolutional channels. At each training step, we select only a subset of highl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.06435","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":""},"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":"1905.06435","created_at":"2026-05-17T23:46:02.446337+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.06435v1","created_at":"2026-05-17T23:46:02.446337+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.06435","created_at":"2026-05-17T23:46:02.446337+00:00"},{"alias_kind":"pith_short_12","alias_value":"KCHC3AKSFPJD","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"KCHC3AKSFPJDRBJP","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"KCHC3AKS","created_at":"2026-05-18T12:33:21.387695+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.22169","citing_title":"Balancing Uncertainty and Diversity of Samples: Leveraging Diversity of Least, High Confidence Samples for Effective Active Learning","ref_index":68,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KCHC3AKSFPJDRBJPVTXLMMUKHZ","json":"https://pith.science/pith/KCHC3AKSFPJDRBJPVTXLMMUKHZ.json","graph_json":"https://pith.science/api/pith-number/KCHC3AKSFPJDRBJPVTXLMMUKHZ/graph.json","events_json":"https://pith.science/api/pith-number/KCHC3AKSFPJDRBJPVTXLMMUKHZ/events.json","paper":"https://pith.science/paper/KCHC3AKS"},"agent_actions":{"view_html":"https://pith.science/pith/KCHC3AKSFPJDRBJPVTXLMMUKHZ","download_json":"https://pith.science/pith/KCHC3AKSFPJDRBJPVTXLMMUKHZ.json","view_paper":"https://pith.science/paper/KCHC3AKS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.06435&json=true","fetch_graph":"https://pith.science/api/pith-number/KCHC3AKSFPJDRBJPVTXLMMUKHZ/graph.json","fetch_events":"https://pith.science/api/pith-number/KCHC3AKSFPJDRBJPVTXLMMUKHZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KCHC3AKSFPJDRBJPVTXLMMUKHZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KCHC3AKSFPJDRBJPVTXLMMUKHZ/action/storage_attestation","attest_author":"https://pith.science/pith/KCHC3AKSFPJDRBJPVTXLMMUKHZ/action/author_attestation","sign_citation":"https://pith.science/pith/KCHC3AKSFPJDRBJPVTXLMMUKHZ/action/citation_signature","submit_replication":"https://pith.science/pith/KCHC3AKSFPJDRBJPVTXLMMUKHZ/action/replication_record"}},"created_at":"2026-05-17T23:46:02.446337+00:00","updated_at":"2026-05-17T23:46:02.446337+00:00"}