{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:BEWLC6LOZTRZZ6G3E7M66F7DJH","short_pith_number":"pith:BEWLC6LO","canonical_record":{"source":{"id":"1903.10258","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-25T12:05:27Z","cross_cats_sorted":[],"title_canon_sha256":"81911356f7b13d11b548ed213e4a2bff8e0c943f57919af2279579ff394e58c2","abstract_canon_sha256":"e4f5e722b9d352debf44d5200ff4c509c4eb05d2530b5e2a8f676f6426da3d6c"},"schema_version":"1.0"},"canonical_sha256":"092cb1796ecce39cf8db27d9ef17e349f7e8e7c43bea39989d09471344980e96","source":{"kind":"arxiv","id":"1903.10258","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.10258","created_at":"2026-07-04T23:55:55Z"},{"alias_kind":"arxiv_version","alias_value":"1903.10258v3","created_at":"2026-07-04T23:55:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.10258","created_at":"2026-07-04T23:55:55Z"},{"alias_kind":"pith_short_12","alias_value":"BEWLC6LOZTRZ","created_at":"2026-07-04T23:55:55Z"},{"alias_kind":"pith_short_16","alias_value":"BEWLC6LOZTRZZ6G3","created_at":"2026-07-04T23:55:55Z"},{"alias_kind":"pith_short_8","alias_value":"BEWLC6LO","created_at":"2026-07-04T23:55:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:BEWLC6LOZTRZZ6G3E7M66F7DJH","target":"record","payload":{"canonical_record":{"source":{"id":"1903.10258","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-25T12:05:27Z","cross_cats_sorted":[],"title_canon_sha256":"81911356f7b13d11b548ed213e4a2bff8e0c943f57919af2279579ff394e58c2","abstract_canon_sha256":"e4f5e722b9d352debf44d5200ff4c509c4eb05d2530b5e2a8f676f6426da3d6c"},"schema_version":"1.0"},"canonical_sha256":"092cb1796ecce39cf8db27d9ef17e349f7e8e7c43bea39989d09471344980e96","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:55:55.363174Z","signature_b64":"9fWiBxEUEZpKDaGPF/TOnOKWMdxOfyoalPgMOhlsIVTQU6Z8jTpshvza6W7dFfw08dLd0s1PiW1pSHCyZ07cBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"092cb1796ecce39cf8db27d9ef17e349f7e8e7c43bea39989d09471344980e96","last_reissued_at":"2026-07-04T23:55:55.362632Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:55:55.362632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.10258","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-04T23:55:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J12aJkYeYbha25Mr3qQ5T1anJwPRe8aXj1RodNWQP/QFBW6grvbWoPAp4G7THEO+7GjUFZcSIc4kNJTipNsHCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T03:07:50.239873Z"},"content_sha256":"da9bf360c675afb628d0e9a93330360a11819dd902968e8f72c1bd285b79a69d","schema_version":"1.0","event_id":"sha256:da9bf360c675afb628d0e9a93330360a11819dd902968e8f72c1bd285b79a69d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:BEWLC6LOZTRZZ6G3E7M66F7DJH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haoyuan Mu, Jian Sun, Tim Kwang-Ting Cheng, Xiangyu Zhang, Xin Yang, Zechun Liu, Zichao Guo","submitted_at":"2019-03-25T12:05:27Z","abstract_excerpt":"In this paper, we propose a novel meta learning approach for automatic channel pruning of very deep neural networks. We first train a PruningNet, a kind of meta network, which is able to generate weight parameters for any pruned structure given the target network. We use a simple stochastic structure sampling method for training the PruningNet. Then, we apply an evolutionary procedure to search for good-performing pruned networks. The search is highly efficient because the weights are directly generated by the trained PruningNet and we do not need any finetuning at search time. With a single P"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.10258","kind":"arxiv","version":3},"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/1903.10258/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-04T23:55:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UJvZS0fR9t6T5ruP0ktI4wL07guUkWMvcTilXYSRArPIeskKm4ehdeg79hWZqnJJ3NYp4Wfu8xm/EVtfmW35DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T03:07:50.240182Z"},"content_sha256":"e8057f4c5d812c2fa5facec28bf79305a02589cf9968b967e6d367aa1ae2d956","schema_version":"1.0","event_id":"sha256:e8057f4c5d812c2fa5facec28bf79305a02589cf9968b967e6d367aa1ae2d956"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BEWLC6LOZTRZZ6G3E7M66F7DJH/bundle.json","state_url":"https://pith.science/pith/BEWLC6LOZTRZZ6G3E7M66F7DJH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BEWLC6LOZTRZZ6G3E7M66F7DJH/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-06T03:07:50Z","links":{"resolver":"https://pith.science/pith/BEWLC6LOZTRZZ6G3E7M66F7DJH","bundle":"https://pith.science/pith/BEWLC6LOZTRZZ6G3E7M66F7DJH/bundle.json","state":"https://pith.science/pith/BEWLC6LOZTRZZ6G3E7M66F7DJH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BEWLC6LOZTRZZ6G3E7M66F7DJH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:BEWLC6LOZTRZZ6G3E7M66F7DJH","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":"e4f5e722b9d352debf44d5200ff4c509c4eb05d2530b5e2a8f676f6426da3d6c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-25T12:05:27Z","title_canon_sha256":"81911356f7b13d11b548ed213e4a2bff8e0c943f57919af2279579ff394e58c2"},"schema_version":"1.0","source":{"id":"1903.10258","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.10258","created_at":"2026-07-04T23:55:55Z"},{"alias_kind":"arxiv_version","alias_value":"1903.10258v3","created_at":"2026-07-04T23:55:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.10258","created_at":"2026-07-04T23:55:55Z"},{"alias_kind":"pith_short_12","alias_value":"BEWLC6LOZTRZ","created_at":"2026-07-04T23:55:55Z"},{"alias_kind":"pith_short_16","alias_value":"BEWLC6LOZTRZZ6G3","created_at":"2026-07-04T23:55:55Z"},{"alias_kind":"pith_short_8","alias_value":"BEWLC6LO","created_at":"2026-07-04T23:55:55Z"}],"graph_snapshots":[{"event_id":"sha256:e8057f4c5d812c2fa5facec28bf79305a02589cf9968b967e6d367aa1ae2d956","target":"graph","created_at":"2026-07-04T23:55:55Z","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/1903.10258/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we propose a novel meta learning approach for automatic channel pruning of very deep neural networks. We first train a PruningNet, a kind of meta network, which is able to generate weight parameters for any pruned structure given the target network. We use a simple stochastic structure sampling method for training the PruningNet. Then, we apply an evolutionary procedure to search for good-performing pruned networks. The search is highly efficient because the weights are directly generated by the trained PruningNet and we do not need any finetuning at search time. With a single P","authors_text":"Haoyuan Mu, Jian Sun, Tim Kwang-Ting Cheng, Xiangyu Zhang, Xin Yang, Zechun Liu, Zichao Guo","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-25T12:05:27Z","title":"MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.10258","kind":"arxiv","version":3},"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:da9bf360c675afb628d0e9a93330360a11819dd902968e8f72c1bd285b79a69d","target":"record","created_at":"2026-07-04T23:55:55Z","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":"e4f5e722b9d352debf44d5200ff4c509c4eb05d2530b5e2a8f676f6426da3d6c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-25T12:05:27Z","title_canon_sha256":"81911356f7b13d11b548ed213e4a2bff8e0c943f57919af2279579ff394e58c2"},"schema_version":"1.0","source":{"id":"1903.10258","kind":"arxiv","version":3}},"canonical_sha256":"092cb1796ecce39cf8db27d9ef17e349f7e8e7c43bea39989d09471344980e96","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"092cb1796ecce39cf8db27d9ef17e349f7e8e7c43bea39989d09471344980e96","first_computed_at":"2026-07-04T23:55:55.362632Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:55:55.362632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9fWiBxEUEZpKDaGPF/TOnOKWMdxOfyoalPgMOhlsIVTQU6Z8jTpshvza6W7dFfw08dLd0s1PiW1pSHCyZ07cBA==","signature_status":"signed_v1","signed_at":"2026-07-04T23:55:55.363174Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.10258","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:da9bf360c675afb628d0e9a93330360a11819dd902968e8f72c1bd285b79a69d","sha256:e8057f4c5d812c2fa5facec28bf79305a02589cf9968b967e6d367aa1ae2d956"],"state_sha256":"2c311479530111891ecafd6a4f4b0fec3981af10b5a92a8fe78f83bb0c6c25bb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rX8VEoXNMxyt6WVtv62k5OWI3gzddIQRiTR1DCi93lR1fpHtBdcrPn7LYf4KPB+R1HYIy4SvRUKkmiPJflcjBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T03:07:50.243725Z","bundle_sha256":"8b1b39b7dd9f4ac2139f95df87b11f773e356304669c7dc214ac532d2701cd7d"}}