{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:6FOHPYF2ZB7HCLHGFEK5XD3FCS","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":"c68f7897cf90f212827536f0c28ca6002c21e049f3b02c1b9908aaaf8d410c3a","cross_cats_sorted":["cs.CV","cs.DC","cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-06T03:58:29Z","title_canon_sha256":"3c13c35a048a2455d7c919c208c20716718fb4ea8c2ab0433c603a44f0363bb0"},"schema_version":"1.0","source":{"id":"1909.05073","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.05073","created_at":"2026-07-05T00:45:52Z"},{"alias_kind":"arxiv_version","alias_value":"1909.05073v4","created_at":"2026-07-05T00:45:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.05073","created_at":"2026-07-05T00:45:52Z"},{"alias_kind":"pith_short_12","alias_value":"6FOHPYF2ZB7H","created_at":"2026-07-05T00:45:52Z"},{"alias_kind":"pith_short_16","alias_value":"6FOHPYF2ZB7HCLHG","created_at":"2026-07-05T00:45:52Z"},{"alias_kind":"pith_short_8","alias_value":"6FOHPYF2","created_at":"2026-07-05T00:45:52Z"}],"graph_snapshots":[{"event_id":"sha256:174235016c204eacc549395283e8d1cbd41eb3199cb1edc863dc321b6ccb08c2","target":"graph","created_at":"2026-07-05T00:45:52Z","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/1909.05073/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Model compression techniques on Deep Neural Network (DNN) have been widely acknowledged as an effective way to achieve acceleration on a variety of platforms, and DNN weight pruning is a straightforward and effective method. There are currently two mainstreams of pruning methods representing two extremes of pruning regularity: non-structured, fine-grained pruning can achieve high sparsity and accuracy, but is not hardware friendly; structured, coarse-grained pruning exploits hardware-efficient structures in pruning, but suffers from accuracy drop when the pruning rate is high. In this paper, w","authors_text":"Bin Ren, Fu-Ming Guo, Jian Tang, Kaisheng Ma, Wei Niu, Xiaolong Ma, Xue Lin, Yanzhi Wang","cross_cats":["cs.CV","cs.DC","cs.NE","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-06T03:58:29Z","title":"PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-time Execution on Mobile Devices"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.05073","kind":"arxiv","version":4},"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":"sha256:6f18fd8328a495fd2655b3d5ef19d7dc58b7473decf3beb5f86bc0e157888bd3","target":"record","created_at":"2026-07-05T00:45:52Z","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":"c68f7897cf90f212827536f0c28ca6002c21e049f3b02c1b9908aaaf8d410c3a","cross_cats_sorted":["cs.CV","cs.DC","cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-06T03:58:29Z","title_canon_sha256":"3c13c35a048a2455d7c919c208c20716718fb4ea8c2ab0433c603a44f0363bb0"},"schema_version":"1.0","source":{"id":"1909.05073","kind":"arxiv","version":4}},"canonical_sha256":"f15c77e0bac87e712ce62915db8f6514aedec65eb82eddb78fc3cb497f2a2781","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f15c77e0bac87e712ce62915db8f6514aedec65eb82eddb78fc3cb497f2a2781","first_computed_at":"2026-07-05T00:45:52.260438Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:45:52.260438Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"i2sE0EkkTBIFfBowKSuBbbXTGDylGou72asPgSmzqOk5/IIhqvvituTp58tJX6SgxdqsHUyYZrAXOZy6rintAg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:45:52.260915Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.05073","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6f18fd8328a495fd2655b3d5ef19d7dc58b7473decf3beb5f86bc0e157888bd3","sha256:174235016c204eacc549395283e8d1cbd41eb3199cb1edc863dc321b6ccb08c2"],"state_sha256":"20a8fda6acecaa117eab90ccd1868c1d20de64c01753f2acab8e450e8d4a7f35"}