{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ZTAM75AFZGZ5V4SZMVXGBZLGIH","short_pith_number":"pith:ZTAM75AF","canonical_record":{"source":{"id":"2206.01198","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-06-02T17:58:54Z","cross_cats_sorted":[],"title_canon_sha256":"bb077d251f4ced668ed5a1427767bce4a799ab35351945290da69c4b7f42accd","abstract_canon_sha256":"c257a387c3fc75ecb6e8af61b58ad4ef21f5d90de73a4e9f3bcddccbc44591e6"},"schema_version":"1.0"},"canonical_sha256":"ccc0cff405c9b3daf259656e60e56641db8e17d186b3293fac4b6d6d140a864d","source":{"kind":"arxiv","id":"2206.01198","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.01198","created_at":"2026-07-05T04:28:37Z"},{"alias_kind":"arxiv_version","alias_value":"2206.01198v1","created_at":"2026-07-05T04:28:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.01198","created_at":"2026-07-05T04:28:37Z"},{"alias_kind":"pith_short_12","alias_value":"ZTAM75AFZGZ5","created_at":"2026-07-05T04:28:37Z"},{"alias_kind":"pith_short_16","alias_value":"ZTAM75AFZGZ5V4SZ","created_at":"2026-07-05T04:28:37Z"},{"alias_kind":"pith_short_8","alias_value":"ZTAM75AF","created_at":"2026-07-05T04:28:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ZTAM75AFZGZ5V4SZMVXGBZLGIH","target":"record","payload":{"canonical_record":{"source":{"id":"2206.01198","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-06-02T17:58:54Z","cross_cats_sorted":[],"title_canon_sha256":"bb077d251f4ced668ed5a1427767bce4a799ab35351945290da69c4b7f42accd","abstract_canon_sha256":"c257a387c3fc75ecb6e8af61b58ad4ef21f5d90de73a4e9f3bcddccbc44591e6"},"schema_version":"1.0"},"canonical_sha256":"ccc0cff405c9b3daf259656e60e56641db8e17d186b3293fac4b6d6d140a864d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:28:37.932354Z","signature_b64":"EZ6JIwKuU+6uuX7GxngHc6s9xcIZ0dQxymCGL1IKkXKtpKfA0bvmtNJ3auuMvuYNpq4QYWr8PNoIMCQmBrhjCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ccc0cff405c9b3daf259656e60e56641db8e17d186b3293fac4b6d6d140a864d","last_reissued_at":"2026-07-05T04:28:37.931836Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:28:37.931836Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2206.01198","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:28:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ExyrdjL6IsUC6DyPzssufTLeLIQc1w7Wvy5Vws68FGzkq+AYl+Qk0i83H8P4f9jz2EuCU5KlyDufNoNHPO9DCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T04:10:02.723655Z"},"content_sha256":"a0a4635829e021669c74ef9856b9f90a7e350eff4923c8d34a9bd6ca2258acfe","schema_version":"1.0","event_id":"sha256:a0a4635829e021669c74ef9856b9f90a7e350eff4923c8d34a9bd6ca2258acfe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ZTAM75AFZGZ5V4SZMVXGBZLGIH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Geng Yuan, Pu Zhao, Xin Chen, Xue Lin, Yanyu Li, Yanzhi Wang","submitted_at":"2022-06-02T17:58:54Z","abstract_excerpt":"Neural architecture search (NAS) and network pruning are widely studied efficient AI techniques, but not yet perfect. NAS performs exhaustive candidate architecture search, incurring tremendous search cost. Though (structured) pruning can simply shrink model dimension, it remains unclear how to decide the per-layer sparsity automatically and optimally. In this work, we revisit the problem of layer-width optimization and propose Pruning-as-Search (PaS), an end-to-end channel pruning method to search out desired sub-network automatically and efficiently. Specifically, we add a depth-wise binary "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.01198","kind":"arxiv","version":1},"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/2206.01198/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":"2026-07-05T04:28:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FAXJa4vh9dYtJ1HtcHzynV/MLckFln8BR501jfttgbHVbA5Xoyz9MFBuNz17PMQ0sPsi2NN/i8KP4qYMO9lWCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T04:10:02.724229Z"},"content_sha256":"7e947e4cfae291e2e17ed8861ff895dacb7f82f4c6d94ce9665427c438cabf79","schema_version":"1.0","event_id":"sha256:7e947e4cfae291e2e17ed8861ff895dacb7f82f4c6d94ce9665427c438cabf79"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZTAM75AFZGZ5V4SZMVXGBZLGIH/bundle.json","state_url":"https://pith.science/pith/ZTAM75AFZGZ5V4SZMVXGBZLGIH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZTAM75AFZGZ5V4SZMVXGBZLGIH/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":"2026-08-13T04:10:02Z","links":{"resolver":"https://pith.science/pith/ZTAM75AFZGZ5V4SZMVXGBZLGIH","bundle":"https://pith.science/pith/ZTAM75AFZGZ5V4SZMVXGBZLGIH/bundle.json","state":"https://pith.science/pith/ZTAM75AFZGZ5V4SZMVXGBZLGIH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZTAM75AFZGZ5V4SZMVXGBZLGIH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ZTAM75AFZGZ5V4SZMVXGBZLGIH","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":"c257a387c3fc75ecb6e8af61b58ad4ef21f5d90de73a4e9f3bcddccbc44591e6","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-06-02T17:58:54Z","title_canon_sha256":"bb077d251f4ced668ed5a1427767bce4a799ab35351945290da69c4b7f42accd"},"schema_version":"1.0","source":{"id":"2206.01198","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.01198","created_at":"2026-07-05T04:28:37Z"},{"alias_kind":"arxiv_version","alias_value":"2206.01198v1","created_at":"2026-07-05T04:28:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.01198","created_at":"2026-07-05T04:28:37Z"},{"alias_kind":"pith_short_12","alias_value":"ZTAM75AFZGZ5","created_at":"2026-07-05T04:28:37Z"},{"alias_kind":"pith_short_16","alias_value":"ZTAM75AFZGZ5V4SZ","created_at":"2026-07-05T04:28:37Z"},{"alias_kind":"pith_short_8","alias_value":"ZTAM75AF","created_at":"2026-07-05T04:28:37Z"}],"graph_snapshots":[{"event_id":"sha256:7e947e4cfae291e2e17ed8861ff895dacb7f82f4c6d94ce9665427c438cabf79","target":"graph","created_at":"2026-07-05T04:28:37Z","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/2206.01198/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Neural architecture search (NAS) and network pruning are widely studied efficient AI techniques, but not yet perfect. NAS performs exhaustive candidate architecture search, incurring tremendous search cost. Though (structured) pruning can simply shrink model dimension, it remains unclear how to decide the per-layer sparsity automatically and optimally. In this work, we revisit the problem of layer-width optimization and propose Pruning-as-Search (PaS), an end-to-end channel pruning method to search out desired sub-network automatically and efficiently. Specifically, we add a depth-wise binary ","authors_text":"Geng Yuan, Pu Zhao, Xin Chen, Xue Lin, Yanyu Li, Yanzhi Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-06-02T17:58:54Z","title":"Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.01198","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:a0a4635829e021669c74ef9856b9f90a7e350eff4923c8d34a9bd6ca2258acfe","target":"record","created_at":"2026-07-05T04:28:37Z","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":"c257a387c3fc75ecb6e8af61b58ad4ef21f5d90de73a4e9f3bcddccbc44591e6","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-06-02T17:58:54Z","title_canon_sha256":"bb077d251f4ced668ed5a1427767bce4a799ab35351945290da69c4b7f42accd"},"schema_version":"1.0","source":{"id":"2206.01198","kind":"arxiv","version":1}},"canonical_sha256":"ccc0cff405c9b3daf259656e60e56641db8e17d186b3293fac4b6d6d140a864d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ccc0cff405c9b3daf259656e60e56641db8e17d186b3293fac4b6d6d140a864d","first_computed_at":"2026-07-05T04:28:37.931836Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:28:37.931836Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EZ6JIwKuU+6uuX7GxngHc6s9xcIZ0dQxymCGL1IKkXKtpKfA0bvmtNJ3auuMvuYNpq4QYWr8PNoIMCQmBrhjCw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:28:37.932354Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.01198","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a0a4635829e021669c74ef9856b9f90a7e350eff4923c8d34a9bd6ca2258acfe","sha256:7e947e4cfae291e2e17ed8861ff895dacb7f82f4c6d94ce9665427c438cabf79"],"state_sha256":"4a20ab7ca7ea04492272c085676e06e738ab8e97c500dc9718157bfcf055e430"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"61KBW7Ud/5tnNLRs7JyLhyn5GEXP1p1tQhQxGzhJw67YLgv9ruWv5hqAFuth5CayfSZGcHFe9dHaT3RKYVeqBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T04:10:02.728383Z","bundle_sha256":"91e248aef78646cd48db812584a80ba965a7ee93439adbe2b7f17f9bea0cc754"}}