{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:XNLTDDGR6JDTHPKQ5V262GCTO4","short_pith_number":"pith:XNLTDDGR","canonical_record":{"source":{"id":"2103.05861","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-10T03:59:03Z","cross_cats_sorted":[],"title_canon_sha256":"e2f98115793bb57de62ca43ffb19dc291f0ac9c46afb32e8b817b8cf9de70f4e","abstract_canon_sha256":"b196a29f2b2ebe7bbb29f119d77ab03ac426fd6384da81633a98702f96e7d81f"},"schema_version":"1.0"},"canonical_sha256":"bb57318cd1f24733bd50ed75ed185377397446693ce7f7ff49540a43662ab8e4","source":{"kind":"arxiv","id":"2103.05861","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.05861","created_at":"2026-07-05T02:21:59Z"},{"alias_kind":"arxiv_version","alias_value":"2103.05861v1","created_at":"2026-07-05T02:21:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.05861","created_at":"2026-07-05T02:21:59Z"},{"alias_kind":"pith_short_12","alias_value":"XNLTDDGR6JDT","created_at":"2026-07-05T02:21:59Z"},{"alias_kind":"pith_short_16","alias_value":"XNLTDDGR6JDTHPKQ","created_at":"2026-07-05T02:21:59Z"},{"alias_kind":"pith_short_8","alias_value":"XNLTDDGR","created_at":"2026-07-05T02:21:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:XNLTDDGR6JDTHPKQ5V262GCTO4","target":"record","payload":{"canonical_record":{"source":{"id":"2103.05861","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-10T03:59:03Z","cross_cats_sorted":[],"title_canon_sha256":"e2f98115793bb57de62ca43ffb19dc291f0ac9c46afb32e8b817b8cf9de70f4e","abstract_canon_sha256":"b196a29f2b2ebe7bbb29f119d77ab03ac426fd6384da81633a98702f96e7d81f"},"schema_version":"1.0"},"canonical_sha256":"bb57318cd1f24733bd50ed75ed185377397446693ce7f7ff49540a43662ab8e4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:21:59.111167Z","signature_b64":"Dng+zL7+yvs7yJeANkmE3Anpr0iXRP4GoaJW6WktxLGZ7M/6EWjEc+990Dg60Du8VeeOPMyNu0r8O7VmGA9jCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bb57318cd1f24733bd50ed75ed185377397446693ce7f7ff49540a43662ab8e4","last_reissued_at":"2026-07-05T02:21:59.110783Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:21:59.110783Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2103.05861","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":"2026-07-05T02:21:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sl2O09KqdPhS+74263z0uHMKxty1VwJLlpeBeSH/8aHR6+0dOzSkcjENCPeoZz9mg752bui3lsGLEXQE38jbDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T12:24:40.375016Z"},"content_sha256":"76316ad6cf223cd8d4be9817286bf101dc285e05c73b4755b4ddf84073d40c5d","schema_version":"1.0","event_id":"sha256:76316ad6cf223cd8d4be9817286bf101dc285e05c73b4755b4ddf84073d40c5d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:XNLTDDGR6JDTHPKQ5V262GCTO4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Manifold Regularized Dynamic Network Pruning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chang Xu, Chao Xu, Dacheng Tao, Yehui Tang, Yiping Deng, Yixing Xu, Yunhe Wang","submitted_at":"2021-03-10T03:59:03Z","abstract_excerpt":"Neural network pruning is an essential approach for reducing the computational complexity of deep models so that they can be well deployed on resource-limited devices. Compared with conventional methods, the recently developed dynamic pruning methods determine redundant filters variant to each input instance which achieves higher acceleration. Most of the existing methods discover effective sub-networks for each instance independently and do not utilize the relationship between different inputs. To maximally excavate redundancy in the given network architecture, this paper proposes a new parad"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.05861","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/2103.05861/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":"2026-07-05T02:21:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ImqDVoXv8NbQ01JkpK+9ls6ZdQdga7naLCAfNjVJDBZWbjw+nRD++iHQ+TOj29XSwRfxzLf57APcqXDK2LKZAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T12:24:40.375538Z"},"content_sha256":"9163ccb4c3223ac4c8bc5dce854272d1bf254f9aeee5f88d204091b96fde7949","schema_version":"1.0","event_id":"sha256:9163ccb4c3223ac4c8bc5dce854272d1bf254f9aeee5f88d204091b96fde7949"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XNLTDDGR6JDTHPKQ5V262GCTO4/bundle.json","state_url":"https://pith.science/pith/XNLTDDGR6JDTHPKQ5V262GCTO4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XNLTDDGR6JDTHPKQ5V262GCTO4/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":"2026-08-11T12:24:40Z","links":{"resolver":"https://pith.science/pith/XNLTDDGR6JDTHPKQ5V262GCTO4","bundle":"https://pith.science/pith/XNLTDDGR6JDTHPKQ5V262GCTO4/bundle.json","state":"https://pith.science/pith/XNLTDDGR6JDTHPKQ5V262GCTO4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XNLTDDGR6JDTHPKQ5V262GCTO4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:XNLTDDGR6JDTHPKQ5V262GCTO4","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":"b196a29f2b2ebe7bbb29f119d77ab03ac426fd6384da81633a98702f96e7d81f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-10T03:59:03Z","title_canon_sha256":"e2f98115793bb57de62ca43ffb19dc291f0ac9c46afb32e8b817b8cf9de70f4e"},"schema_version":"1.0","source":{"id":"2103.05861","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.05861","created_at":"2026-07-05T02:21:59Z"},{"alias_kind":"arxiv_version","alias_value":"2103.05861v1","created_at":"2026-07-05T02:21:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.05861","created_at":"2026-07-05T02:21:59Z"},{"alias_kind":"pith_short_12","alias_value":"XNLTDDGR6JDT","created_at":"2026-07-05T02:21:59Z"},{"alias_kind":"pith_short_16","alias_value":"XNLTDDGR6JDTHPKQ","created_at":"2026-07-05T02:21:59Z"},{"alias_kind":"pith_short_8","alias_value":"XNLTDDGR","created_at":"2026-07-05T02:21:59Z"}],"graph_snapshots":[{"event_id":"sha256:9163ccb4c3223ac4c8bc5dce854272d1bf254f9aeee5f88d204091b96fde7949","target":"graph","created_at":"2026-07-05T02:21:59Z","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/2103.05861/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Neural network pruning is an essential approach for reducing the computational complexity of deep models so that they can be well deployed on resource-limited devices. Compared with conventional methods, the recently developed dynamic pruning methods determine redundant filters variant to each input instance which achieves higher acceleration. Most of the existing methods discover effective sub-networks for each instance independently and do not utilize the relationship between different inputs. To maximally excavate redundancy in the given network architecture, this paper proposes a new parad","authors_text":"Chang Xu, Chao Xu, Dacheng Tao, Yehui Tang, Yiping Deng, Yixing Xu, Yunhe Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-10T03:59:03Z","title":"Manifold Regularized Dynamic Network Pruning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.05861","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":"sha256:76316ad6cf223cd8d4be9817286bf101dc285e05c73b4755b4ddf84073d40c5d","target":"record","created_at":"2026-07-05T02:21:59Z","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":"b196a29f2b2ebe7bbb29f119d77ab03ac426fd6384da81633a98702f96e7d81f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-10T03:59:03Z","title_canon_sha256":"e2f98115793bb57de62ca43ffb19dc291f0ac9c46afb32e8b817b8cf9de70f4e"},"schema_version":"1.0","source":{"id":"2103.05861","kind":"arxiv","version":1}},"canonical_sha256":"bb57318cd1f24733bd50ed75ed185377397446693ce7f7ff49540a43662ab8e4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bb57318cd1f24733bd50ed75ed185377397446693ce7f7ff49540a43662ab8e4","first_computed_at":"2026-07-05T02:21:59.110783Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:21:59.110783Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Dng+zL7+yvs7yJeANkmE3Anpr0iXRP4GoaJW6WktxLGZ7M/6EWjEc+990Dg60Du8VeeOPMyNu0r8O7VmGA9jCA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:21:59.111167Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.05861","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:76316ad6cf223cd8d4be9817286bf101dc285e05c73b4755b4ddf84073d40c5d","sha256:9163ccb4c3223ac4c8bc5dce854272d1bf254f9aeee5f88d204091b96fde7949"],"state_sha256":"8e7a5192615a5694320fbc15b6a8c884786a7d3125e0db3b752f884fa2175db9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"anHEauO2Qck2neYaHIProeQbvW+cxQbGlM+VMdKdKECMCbZUe4DRtP8vHxTELza4cHDqTVnvX5Jmn6w1riJ1DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T12:24:40.379348Z","bundle_sha256":"39711926db062c8d77a8d651fffde13b1f3669abe38fea2798a8ba71da0a4342"}}