{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:OCR6STEUBPTLN5IGFRESFYAA2I","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":"4c4c8f42a68278a95552a8c597c6635aaa69ef8a61f5563fcf78bfaefce3487b","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-11T04:37:10Z","title_canon_sha256":"d2ca6888afdbc01a1015c199fa8cd9bf84efbc8c18cc7f462ba5c4130faf05c3"},"schema_version":"1.0","source":{"id":"1905.04446","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.04446","created_at":"2026-05-17T23:46:24Z"},{"alias_kind":"arxiv_version","alias_value":"1905.04446v1","created_at":"2026-05-17T23:46:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.04446","created_at":"2026-05-17T23:46:24Z"},{"alias_kind":"pith_short_12","alias_value":"OCR6STEUBPTL","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"OCR6STEUBPTLN5IG","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"OCR6STEU","created_at":"2026-05-18T12:33:24Z"}],"graph_snapshots":[{"event_id":"sha256:8f0c62286c2dbac088549f3eeae917a791527918056d198f93fd7a4d0389af72","target":"graph","created_at":"2026-05-17T23:46:24Z","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"},"paper":{"abstract_excerpt":"While convolutional neural networks (CNN) have achieved impressive performance on various classification/recognition tasks, they typically consist of a massive number of parameters. This results in significant memory requirement as well as computational overheads. Consequently, there is a growing need for filter-level pruning approaches for compressing CNN based models that not only reduce the total number of parameters but reduce the overall computation as well. We present a new min-max framework for filter-level pruning of CNNs. Our framework, called Play and Prune (PP), jointly prunes and f","authors_text":"Piyush Rai, Pravendra Singh, Vinay Kumar Verma, Vinay P. Namboodiri","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-11T04:37:10Z","title":"Play and Prune: Adaptive Filter Pruning for Deep Model Compression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.04446","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:a4f962d518bef5eee083d0640be213331a9f16dfd39080f0b496d2f533ea41ba","target":"record","created_at":"2026-05-17T23:46:24Z","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":"4c4c8f42a68278a95552a8c597c6635aaa69ef8a61f5563fcf78bfaefce3487b","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-11T04:37:10Z","title_canon_sha256":"d2ca6888afdbc01a1015c199fa8cd9bf84efbc8c18cc7f462ba5c4130faf05c3"},"schema_version":"1.0","source":{"id":"1905.04446","kind":"arxiv","version":1}},"canonical_sha256":"70a3e94c940be6b6f5062c4922e000d22e4d0108bcf6282539af11f2b6386273","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"70a3e94c940be6b6f5062c4922e000d22e4d0108bcf6282539af11f2b6386273","first_computed_at":"2026-05-17T23:46:24.826922Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:24.826922Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xTRmbYSFn1DkhFsprUqIFetUaXufT1C3hpZoQLKDpJHWIdjWB+8SgFx2haHSypSVZTxMCnS6B4qVPTfbjmRTAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:24.827555Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.04446","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a4f962d518bef5eee083d0640be213331a9f16dfd39080f0b496d2f533ea41ba","sha256:8f0c62286c2dbac088549f3eeae917a791527918056d198f93fd7a4d0389af72"],"state_sha256":"e00057a02e30fa7a4ccaa82f9d2388fca0473940464faffa97b5bd059b4cafda"}