{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:UNLZ3OZHXVIQLPYSUOJC6PFIBC","short_pith_number":"pith:UNLZ3OZH","canonical_record":{"source":{"id":"1905.11530","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-05-27T22:39:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"dda5f4820db45dd203416253d888fdbade5374a9d021ff50b6566b1e688e07b3","abstract_canon_sha256":"402750283da2c695b75e1ab1dc3ac2593097246f60e1f7c0a97745cb72808ee2"},"schema_version":"1.0"},"canonical_sha256":"a3579dbb27bd5105bf12a3922f3ca808ab5f9cdbf1287c92c2c77e3e98ef0cb0","source":{"kind":"arxiv","id":"1905.11530","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.11530","created_at":"2026-07-05T00:26:57Z"},{"alias_kind":"arxiv_version","alias_value":"1905.11530v3","created_at":"2026-07-05T00:26:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.11530","created_at":"2026-07-05T00:26:57Z"},{"alias_kind":"pith_short_12","alias_value":"UNLZ3OZHXVIQ","created_at":"2026-07-05T00:26:57Z"},{"alias_kind":"pith_short_16","alias_value":"UNLZ3OZHXVIQLPYS","created_at":"2026-07-05T00:26:57Z"},{"alias_kind":"pith_short_8","alias_value":"UNLZ3OZH","created_at":"2026-07-05T00:26:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:UNLZ3OZHXVIQLPYSUOJC6PFIBC","target":"record","payload":{"canonical_record":{"source":{"id":"1905.11530","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-05-27T22:39:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"dda5f4820db45dd203416253d888fdbade5374a9d021ff50b6566b1e688e07b3","abstract_canon_sha256":"402750283da2c695b75e1ab1dc3ac2593097246f60e1f7c0a97745cb72808ee2"},"schema_version":"1.0"},"canonical_sha256":"a3579dbb27bd5105bf12a3922f3ca808ab5f9cdbf1287c92c2c77e3e98ef0cb0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:26:57.424260Z","signature_b64":"15kb+RJPKqtaVjGeK/EXIhWZJQM7YPMzTRZ5m3xsKJIwPQDrw0MbEwDmxy2zKuzjV8n+ev3O1TWyYecmIPyWAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a3579dbb27bd5105bf12a3922f3ca808ab5f9cdbf1287c92c2c77e3e98ef0cb0","last_reissued_at":"2026-07-05T00:26:57.423706Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:26:57.423706Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.11530","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":"2026-07-05T00:26:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nSvGMlPhiesj72TDqiZ59IEEIa1gzyofNm76HOFxvAaMtXwDWizBgYuFBvaO6RWf3XT8y0L5swssZzj6ii0/DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T05:05:44.947136Z"},"content_sha256":"9a13414f2362cb2c5d17abb146e878462495c8d6cb6dc0e8f97ccf07d67a2c9b","schema_version":"1.0","event_id":"sha256:9a13414f2362cb2c5d17abb146e878462495c8d6cb6dc0e8f97ccf07d67a2c9b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:UNLZ3OZHXVIQLPYSUOJC6PFIBC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Network Construction through Structural Plasticity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Xiaocong Du, Yu Cao, Yufei Ma, Zheng Li","submitted_at":"2019-05-27T22:39:18Z","abstract_excerpt":"Deep Neural Networks (DNNs) on hardware is facing excessive computation cost due to the massive number of parameters. A typical training pipeline to mitigate over-parameterization is to pre-define a DNN structure first with redundant learning units (filters and neurons) under the goal of high accuracy, then to prune redundant learning units after training with the purpose of efficient inference. We argue that it is sub-optimal to introduce redundancy into training for the purpose of reducing redundancy later in inference. Moreover, the fixed network structure further results in poor adaption t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.11530","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/1905.11530/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":"2026-07-05T00:26:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JkJM6IxeLM+PucbFg896bbEe4w0k1QCfcuC4O8G4SH2uByJU+wwfaHQ5a7UE7Kh9OEpmmOsxeZI5tXGwMulqCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T05:05:44.947567Z"},"content_sha256":"6ebb6432c5211cf764796271c9e61cf814defa778662a3b213bcf71974749d3b","schema_version":"1.0","event_id":"sha256:6ebb6432c5211cf764796271c9e61cf814defa778662a3b213bcf71974749d3b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UNLZ3OZHXVIQLPYSUOJC6PFIBC/bundle.json","state_url":"https://pith.science/pith/UNLZ3OZHXVIQLPYSUOJC6PFIBC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UNLZ3OZHXVIQLPYSUOJC6PFIBC/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":"2026-07-06T05:05:44Z","links":{"resolver":"https://pith.science/pith/UNLZ3OZHXVIQLPYSUOJC6PFIBC","bundle":"https://pith.science/pith/UNLZ3OZHXVIQLPYSUOJC6PFIBC/bundle.json","state":"https://pith.science/pith/UNLZ3OZHXVIQLPYSUOJC6PFIBC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UNLZ3OZHXVIQLPYSUOJC6PFIBC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:UNLZ3OZHXVIQLPYSUOJC6PFIBC","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":"402750283da2c695b75e1ab1dc3ac2593097246f60e1f7c0a97745cb72808ee2","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-05-27T22:39:18Z","title_canon_sha256":"dda5f4820db45dd203416253d888fdbade5374a9d021ff50b6566b1e688e07b3"},"schema_version":"1.0","source":{"id":"1905.11530","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.11530","created_at":"2026-07-05T00:26:57Z"},{"alias_kind":"arxiv_version","alias_value":"1905.11530v3","created_at":"2026-07-05T00:26:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.11530","created_at":"2026-07-05T00:26:57Z"},{"alias_kind":"pith_short_12","alias_value":"UNLZ3OZHXVIQ","created_at":"2026-07-05T00:26:57Z"},{"alias_kind":"pith_short_16","alias_value":"UNLZ3OZHXVIQLPYS","created_at":"2026-07-05T00:26:57Z"},{"alias_kind":"pith_short_8","alias_value":"UNLZ3OZH","created_at":"2026-07-05T00:26:57Z"}],"graph_snapshots":[{"event_id":"sha256:6ebb6432c5211cf764796271c9e61cf814defa778662a3b213bcf71974749d3b","target":"graph","created_at":"2026-07-05T00:26:57Z","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/1905.11530/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep Neural Networks (DNNs) on hardware is facing excessive computation cost due to the massive number of parameters. A typical training pipeline to mitigate over-parameterization is to pre-define a DNN structure first with redundant learning units (filters and neurons) under the goal of high accuracy, then to prune redundant learning units after training with the purpose of efficient inference. We argue that it is sub-optimal to introduce redundancy into training for the purpose of reducing redundancy later in inference. Moreover, the fixed network structure further results in poor adaption t","authors_text":"Xiaocong Du, Yu Cao, Yufei Ma, Zheng Li","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-05-27T22:39:18Z","title":"Efficient Network Construction through Structural Plasticity"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.11530","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":"sha256:9a13414f2362cb2c5d17abb146e878462495c8d6cb6dc0e8f97ccf07d67a2c9b","target":"record","created_at":"2026-07-05T00:26:57Z","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":"402750283da2c695b75e1ab1dc3ac2593097246f60e1f7c0a97745cb72808ee2","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-05-27T22:39:18Z","title_canon_sha256":"dda5f4820db45dd203416253d888fdbade5374a9d021ff50b6566b1e688e07b3"},"schema_version":"1.0","source":{"id":"1905.11530","kind":"arxiv","version":3}},"canonical_sha256":"a3579dbb27bd5105bf12a3922f3ca808ab5f9cdbf1287c92c2c77e3e98ef0cb0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a3579dbb27bd5105bf12a3922f3ca808ab5f9cdbf1287c92c2c77e3e98ef0cb0","first_computed_at":"2026-07-05T00:26:57.423706Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:26:57.423706Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"15kb+RJPKqtaVjGeK/EXIhWZJQM7YPMzTRZ5m3xsKJIwPQDrw0MbEwDmxy2zKuzjV8n+ev3O1TWyYecmIPyWAw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:26:57.424260Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.11530","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9a13414f2362cb2c5d17abb146e878462495c8d6cb6dc0e8f97ccf07d67a2c9b","sha256:6ebb6432c5211cf764796271c9e61cf814defa778662a3b213bcf71974749d3b"],"state_sha256":"0717e1400a2a3c2eb12c800572e5ad88ea32ccf166d6ab8d84ae9a97bed2d6ea"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xyOvDXV4ows4yD6s73KjVG/g8CXe0m7CzRucZ1p9b34MYC9/Vk1rllQe38EnKWqI6847WlwXETjCwHhUtST9DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T05:05:44.949684Z","bundle_sha256":"e2785cdf300abaf8fedfde19613edb888bdabbbaef7982caa732a65fa69022d5"}}