{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:SLZUGMM5METCXDAT7GGM2NOABI","short_pith_number":"pith:SLZUGMM5","schema_version":"1.0","canonical_sha256":"92f343319d61262b8c13f98ccd35c00a39f4c71c1e134964b5b6ed7f4c850fe1","source":{"kind":"arxiv","id":"1904.12368","version":2},"attestation_state":"computed","paper":{"title":"Towards Efficient Model Compression via Learned Global Ranking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Cha Zhang, Diana Marculescu, Ruizhou Ding, Ting-Wu Chin","submitted_at":"2019-04-28T18:51:26Z","abstract_excerpt":"Pruning convolutional filters has demonstrated its effectiveness in compressing ConvNets. Prior art in filter pruning requires users to specify a target model complexity (e.g., model size or FLOP count) for the resulting architecture. However, determining a target model complexity can be difficult for optimizing various embodied AI applications such as autonomous robots, drones, and user-facing applications. First, both the accuracy and the speed of ConvNets can affect the performance of the application. Second, the performance of the application can be hard to assess without evaluating ConvNe"},"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":"1904.12368","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-28T18:51:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b6c47ce05d70de90c3418f4f9fe71f4892bbe44088f0948d183b867c1e31f9a9","abstract_canon_sha256":"ed49f0ccc25ccf6fe17f259ff77faa29af5840036ff7e528f7b6ed1c21c76982"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:47:56.879007Z","signature_b64":"5Dyn8BaJ0dam6tGhkTQOO5QE2RWbTyvNXjeMJamqBXKULFv+sF2X4g2s59l6EsCWC8GaXojoh1EO9OyJ5AzGAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"92f343319d61262b8c13f98ccd35c00a39f4c71c1e134964b5b6ed7f4c850fe1","last_reissued_at":"2026-07-05T00:47:56.878516Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:47:56.878516Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Efficient Model Compression via Learned Global Ranking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Cha Zhang, Diana Marculescu, Ruizhou Ding, Ting-Wu Chin","submitted_at":"2019-04-28T18:51:26Z","abstract_excerpt":"Pruning convolutional filters has demonstrated its effectiveness in compressing ConvNets. Prior art in filter pruning requires users to specify a target model complexity (e.g., model size or FLOP count) for the resulting architecture. However, determining a target model complexity can be difficult for optimizing various embodied AI applications such as autonomous robots, drones, and user-facing applications. First, both the accuracy and the speed of ConvNets can affect the performance of the application. Second, the performance of the application can be hard to assess without evaluating ConvNe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.12368","kind":"arxiv","version":2},"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/1904.12368/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":"1904.12368","created_at":"2026-07-05T00:47:56.878590+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.12368v2","created_at":"2026-07-05T00:47:56.878590+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.12368","created_at":"2026-07-05T00:47:56.878590+00:00"},{"alias_kind":"pith_short_12","alias_value":"SLZUGMM5METC","created_at":"2026-07-05T00:47:56.878590+00:00"},{"alias_kind":"pith_short_16","alias_value":"SLZUGMM5METCXDAT","created_at":"2026-07-05T00:47:56.878590+00:00"},{"alias_kind":"pith_short_8","alias_value":"SLZUGMM5","created_at":"2026-07-05T00:47:56.878590+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SLZUGMM5METCXDAT7GGM2NOABI","json":"https://pith.science/pith/SLZUGMM5METCXDAT7GGM2NOABI.json","graph_json":"https://pith.science/api/pith-number/SLZUGMM5METCXDAT7GGM2NOABI/graph.json","events_json":"https://pith.science/api/pith-number/SLZUGMM5METCXDAT7GGM2NOABI/events.json","paper":"https://pith.science/paper/SLZUGMM5"},"agent_actions":{"view_html":"https://pith.science/pith/SLZUGMM5METCXDAT7GGM2NOABI","download_json":"https://pith.science/pith/SLZUGMM5METCXDAT7GGM2NOABI.json","view_paper":"https://pith.science/paper/SLZUGMM5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.12368&json=true","fetch_graph":"https://pith.science/api/pith-number/SLZUGMM5METCXDAT7GGM2NOABI/graph.json","fetch_events":"https://pith.science/api/pith-number/SLZUGMM5METCXDAT7GGM2NOABI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SLZUGMM5METCXDAT7GGM2NOABI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SLZUGMM5METCXDAT7GGM2NOABI/action/storage_attestation","attest_author":"https://pith.science/pith/SLZUGMM5METCXDAT7GGM2NOABI/action/author_attestation","sign_citation":"https://pith.science/pith/SLZUGMM5METCXDAT7GGM2NOABI/action/citation_signature","submit_replication":"https://pith.science/pith/SLZUGMM5METCXDAT7GGM2NOABI/action/replication_record"}},"created_at":"2026-07-05T00:47:56.878590+00:00","updated_at":"2026-07-05T00:47:56.878590+00:00"}