{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:UFNQGSEU2JR6TAKAHR6257SFS3","short_pith_number":"pith:UFNQGSEU","schema_version":"1.0","canonical_sha256":"a15b034894d263e981403c7daefe4596c3f01fbb9e9e93177eb35af98da72d3e","source":{"kind":"arxiv","id":"1901.09290","version":5},"attestation_state":"computed","paper":{"title":"PruneTrain: Fast Neural Network Training by Dynamic Sparse Model Reconfiguration","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Esha Choukse, Mattan Erez, Sangkug Lym, Siavash Zangeneh, Sujay Sanghavi, Wei Wen","submitted_at":"2019-01-26T23:18:49Z","abstract_excerpt":"State-of-the-art convolutional neural networks (CNNs) used in vision applications have large models with numerous weights. Training these models is very compute- and memory-resource intensive. Much research has been done on pruning or compressing these models to reduce the cost of inference, but little work has addressed the costs of training. We focus precisely on accelerating training. We propose PruneTrain, a cost-efficient mechanism that gradually reduces the training cost during training. PruneTrain uses a structured group-lasso regularization approach that drives the training optimizatio"},"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":"1901.09290","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-01-26T23:18:49Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4aa990a0fbcf5c7d123ca199f3496cda2f47ce59a5056a5bf89a4788ea8e3e5d","abstract_canon_sha256":"f61ec7aaa7de144a3767ccf92b5606edf7ca29d7e3c1cc92ee367398fbbeee9a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:24:36.561849Z","signature_b64":"rjYU2+ibWYng6yuHFkqSnZcDOOFyojqoTQSZVrbKFD8iFSmBN9tjB85l8QXe8Y5oosGHiBO+SUbcEBn51JMmBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a15b034894d263e981403c7daefe4596c3f01fbb9e9e93177eb35af98da72d3e","last_reissued_at":"2026-07-05T00:24:36.561253Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:24:36.561253Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PruneTrain: Fast Neural Network Training by Dynamic Sparse Model Reconfiguration","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Esha Choukse, Mattan Erez, Sangkug Lym, Siavash Zangeneh, Sujay Sanghavi, Wei Wen","submitted_at":"2019-01-26T23:18:49Z","abstract_excerpt":"State-of-the-art convolutional neural networks (CNNs) used in vision applications have large models with numerous weights. Training these models is very compute- and memory-resource intensive. Much research has been done on pruning or compressing these models to reduce the cost of inference, but little work has addressed the costs of training. We focus precisely on accelerating training. We propose PruneTrain, a cost-efficient mechanism that gradually reduces the training cost during training. PruneTrain uses a structured group-lasso regularization approach that drives the training optimizatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.09290","kind":"arxiv","version":5},"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/1901.09290/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":"1901.09290","created_at":"2026-07-05T00:24:36.561323+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.09290v5","created_at":"2026-07-05T00:24:36.561323+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.09290","created_at":"2026-07-05T00:24:36.561323+00:00"},{"alias_kind":"pith_short_12","alias_value":"UFNQGSEU2JR6","created_at":"2026-07-05T00:24:36.561323+00:00"},{"alias_kind":"pith_short_16","alias_value":"UFNQGSEU2JR6TAKA","created_at":"2026-07-05T00:24:36.561323+00:00"},{"alias_kind":"pith_short_8","alias_value":"UFNQGSEU","created_at":"2026-07-05T00:24:36.561323+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.05460","citing_title":"Accelerated CNN Training Through Gradient Approximation","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UFNQGSEU2JR6TAKAHR6257SFS3","json":"https://pith.science/pith/UFNQGSEU2JR6TAKAHR6257SFS3.json","graph_json":"https://pith.science/api/pith-number/UFNQGSEU2JR6TAKAHR6257SFS3/graph.json","events_json":"https://pith.science/api/pith-number/UFNQGSEU2JR6TAKAHR6257SFS3/events.json","paper":"https://pith.science/paper/UFNQGSEU"},"agent_actions":{"view_html":"https://pith.science/pith/UFNQGSEU2JR6TAKAHR6257SFS3","download_json":"https://pith.science/pith/UFNQGSEU2JR6TAKAHR6257SFS3.json","view_paper":"https://pith.science/paper/UFNQGSEU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.09290&json=true","fetch_graph":"https://pith.science/api/pith-number/UFNQGSEU2JR6TAKAHR6257SFS3/graph.json","fetch_events":"https://pith.science/api/pith-number/UFNQGSEU2JR6TAKAHR6257SFS3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UFNQGSEU2JR6TAKAHR6257SFS3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UFNQGSEU2JR6TAKAHR6257SFS3/action/storage_attestation","attest_author":"https://pith.science/pith/UFNQGSEU2JR6TAKAHR6257SFS3/action/author_attestation","sign_citation":"https://pith.science/pith/UFNQGSEU2JR6TAKAHR6257SFS3/action/citation_signature","submit_replication":"https://pith.science/pith/UFNQGSEU2JR6TAKAHR6257SFS3/action/replication_record"}},"created_at":"2026-07-05T00:24:36.561323+00:00","updated_at":"2026-07-05T00:24:36.561323+00:00"}