{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:BXRRQR2DLXIRFZMUKPHETEFAZR","short_pith_number":"pith:BXRRQR2D","canonical_record":{"source":{"id":"1905.07320","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-05-10T02:34:23Z","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"title_canon_sha256":"9773d8511d5966e5950344e7859125729e7caf524e9d31600f1c0d0b3f9e5154","abstract_canon_sha256":"6173b50467226fa6b0aa23b7e9cd66319a5c6da92dec2bf11c83d6eedd590b22"},"schema_version":"1.0"},"canonical_sha256":"0de31847435dd112e59453ce4990a0cc64a7f924e1ab0860423d6362f281607c","source":{"kind":"arxiv","id":"1905.07320","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.07320","created_at":"2026-07-04T23:59:49Z"},{"alias_kind":"arxiv_version","alias_value":"1905.07320v3","created_at":"2026-07-04T23:59:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.07320","created_at":"2026-07-04T23:59:49Z"},{"alias_kind":"pith_short_12","alias_value":"BXRRQR2DLXIR","created_at":"2026-07-04T23:59:49Z"},{"alias_kind":"pith_short_16","alias_value":"BXRRQR2DLXIRFZMU","created_at":"2026-07-04T23:59:49Z"},{"alias_kind":"pith_short_8","alias_value":"BXRRQR2D","created_at":"2026-07-04T23:59:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:BXRRQR2DLXIRFZMUKPHETEFAZR","target":"record","payload":{"canonical_record":{"source":{"id":"1905.07320","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-05-10T02:34:23Z","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"title_canon_sha256":"9773d8511d5966e5950344e7859125729e7caf524e9d31600f1c0d0b3f9e5154","abstract_canon_sha256":"6173b50467226fa6b0aa23b7e9cd66319a5c6da92dec2bf11c83d6eedd590b22"},"schema_version":"1.0"},"canonical_sha256":"0de31847435dd112e59453ce4990a0cc64a7f924e1ab0860423d6362f281607c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:59:49.276284Z","signature_b64":"ZUwsQE2A/imvIQY6UQCVpOwQje9DpAjd62vUPWEhLGsc/30vQrCtodfedVgrLudStRxq/6a7A9v20fTrISRwCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0de31847435dd112e59453ce4990a0cc64a7f924e1ab0860423d6362f281607c","last_reissued_at":"2026-07-04T23:59:49.275808Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:59:49.275808Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.07320","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":"2026-07-04T23:59:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1VG2HEqk8P2qPtfPKFlNJ6optzMlpWa4WiwHN6akZYN/sxkNzYR5g9Bp7y/iQ6/TQ8IdUWdSfqbOEZErkvLsCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T19:50:39.543831Z"},"content_sha256":"986549c9c1229c6a47510636ad03d1fb923bbc58b8e998dfc16cc0488e378e09","schema_version":"1.0","event_id":"sha256:986549c9c1229c6a47510636ad03d1fb923bbc58b8e998dfc16cc0488e378e09"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:BXRRQR2DLXIRFZMUKPHETEFAZR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"EENA: Efficient Evolution of Neural Architecture","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","stat.ML"],"primary_cat":"cs.NE","authors_text":"Chuanguang Yang, Erhu Zhao, Hui Zhu, Kaiqiang Xu, Yongjun Xu, Zhulin An","submitted_at":"2019-05-10T02:34:23Z","abstract_excerpt":"Latest algorithms for automatic neural architecture search perform remarkable but are basically directionless in search space and computational expensive in training of every intermediate architecture. In this paper, we propose a method for efficient architecture search called EENA (Efficient Evolution of Neural Architecture). Due to the elaborately designed mutation and crossover operations, the evolution process can be guided by the information have already been learned. Therefore, less computational effort will be required while the searching and training time can be reduced significantly. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.07320","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/1905.07320/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":"2026-07-04T23:59:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5q5c0MQkkaSWyqTOyl5icf1Q7GfU4EC/nSY0e3vT/HBDLM+ax4ccMLf+QgmxjjWxEGc88pE6/2MQUuDpBJFECQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T19:50:39.544476Z"},"content_sha256":"6f5da1ac79397845adf2926172c6cea3e8720dd458424dc10e5a5f276adcdc8f","schema_version":"1.0","event_id":"sha256:6f5da1ac79397845adf2926172c6cea3e8720dd458424dc10e5a5f276adcdc8f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BXRRQR2DLXIRFZMUKPHETEFAZR/bundle.json","state_url":"https://pith.science/pith/BXRRQR2DLXIRFZMUKPHETEFAZR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BXRRQR2DLXIRFZMUKPHETEFAZR/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":"2026-08-17T19:50:39Z","links":{"resolver":"https://pith.science/pith/BXRRQR2DLXIRFZMUKPHETEFAZR","bundle":"https://pith.science/pith/BXRRQR2DLXIRFZMUKPHETEFAZR/bundle.json","state":"https://pith.science/pith/BXRRQR2DLXIRFZMUKPHETEFAZR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BXRRQR2DLXIRFZMUKPHETEFAZR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:BXRRQR2DLXIRFZMUKPHETEFAZR","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":"6173b50467226fa6b0aa23b7e9cd66319a5c6da92dec2bf11c83d6eedd590b22","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-05-10T02:34:23Z","title_canon_sha256":"9773d8511d5966e5950344e7859125729e7caf524e9d31600f1c0d0b3f9e5154"},"schema_version":"1.0","source":{"id":"1905.07320","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.07320","created_at":"2026-07-04T23:59:49Z"},{"alias_kind":"arxiv_version","alias_value":"1905.07320v3","created_at":"2026-07-04T23:59:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.07320","created_at":"2026-07-04T23:59:49Z"},{"alias_kind":"pith_short_12","alias_value":"BXRRQR2DLXIR","created_at":"2026-07-04T23:59:49Z"},{"alias_kind":"pith_short_16","alias_value":"BXRRQR2DLXIRFZMU","created_at":"2026-07-04T23:59:49Z"},{"alias_kind":"pith_short_8","alias_value":"BXRRQR2D","created_at":"2026-07-04T23:59:49Z"}],"graph_snapshots":[{"event_id":"sha256:6f5da1ac79397845adf2926172c6cea3e8720dd458424dc10e5a5f276adcdc8f","target":"graph","created_at":"2026-07-04T23:59:49Z","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/1905.07320/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Latest algorithms for automatic neural architecture search perform remarkable but are basically directionless in search space and computational expensive in training of every intermediate architecture. In this paper, we propose a method for efficient architecture search called EENA (Efficient Evolution of Neural Architecture). Due to the elaborately designed mutation and crossover operations, the evolution process can be guided by the information have already been learned. Therefore, less computational effort will be required while the searching and training time can be reduced significantly. ","authors_text":"Chuanguang Yang, Erhu Zhao, Hui Zhu, Kaiqiang Xu, Yongjun Xu, Zhulin An","cross_cats":["cs.CV","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-05-10T02:34:23Z","title":"EENA: Efficient Evolution of Neural Architecture"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.07320","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":"sha256:986549c9c1229c6a47510636ad03d1fb923bbc58b8e998dfc16cc0488e378e09","target":"record","created_at":"2026-07-04T23:59:49Z","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":"6173b50467226fa6b0aa23b7e9cd66319a5c6da92dec2bf11c83d6eedd590b22","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-05-10T02:34:23Z","title_canon_sha256":"9773d8511d5966e5950344e7859125729e7caf524e9d31600f1c0d0b3f9e5154"},"schema_version":"1.0","source":{"id":"1905.07320","kind":"arxiv","version":3}},"canonical_sha256":"0de31847435dd112e59453ce4990a0cc64a7f924e1ab0860423d6362f281607c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0de31847435dd112e59453ce4990a0cc64a7f924e1ab0860423d6362f281607c","first_computed_at":"2026-07-04T23:59:49.275808Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:59:49.275808Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZUwsQE2A/imvIQY6UQCVpOwQje9DpAjd62vUPWEhLGsc/30vQrCtodfedVgrLudStRxq/6a7A9v20fTrISRwCA==","signature_status":"signed_v1","signed_at":"2026-07-04T23:59:49.276284Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.07320","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:986549c9c1229c6a47510636ad03d1fb923bbc58b8e998dfc16cc0488e378e09","sha256:6f5da1ac79397845adf2926172c6cea3e8720dd458424dc10e5a5f276adcdc8f"],"state_sha256":"b8370b7b246b0381a0d70df71d4fe6c794b30c218571cfdad73aaedfabf11fda"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fIXvpoqghL/nKZFalVUZslTCoD6cUnImNyXfnplSG/26wfVfshYW80v2crvErfsI6FjPKmYzDc/LmH6MSLp+DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T19:50:39.553023Z","bundle_sha256":"01abf80ffcdc9c40a1c81676164df6c218fb6d49c170779b3e7e6d5a490c500d"}}