{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:WFJIOCJAOVI5ZH3UDI4KIU2QYR","short_pith_number":"pith:WFJIOCJA","canonical_record":{"source":{"id":"1901.01074","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-01-04T12:21:56Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d088868fed970c29e0071a756b65ca38683cf46d59b41e7207af42d924d2d681","abstract_canon_sha256":"26d39777ac0cc167e8f123e52c9f06e8b6ae06ef74fecf5113a58d1f0b9493d8"},"schema_version":"1.0"},"canonical_sha256":"b1528709207551dc9f741a38a45350c46c9c7363c7ad9bd2cd83c3845827f161","source":{"kind":"arxiv","id":"1901.01074","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.01074","created_at":"2026-05-17T23:56:12Z"},{"alias_kind":"arxiv_version","alias_value":"1901.01074v3","created_at":"2026-05-17T23:56:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.01074","created_at":"2026-05-17T23:56:12Z"},{"alias_kind":"pith_short_12","alias_value":"WFJIOCJAOVI5","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"WFJIOCJAOVI5ZH3U","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"WFJIOCJA","created_at":"2026-05-18T12:33:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:WFJIOCJAOVI5ZH3UDI4KIU2QYR","target":"record","payload":{"canonical_record":{"source":{"id":"1901.01074","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-01-04T12:21:56Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d088868fed970c29e0071a756b65ca38683cf46d59b41e7207af42d924d2d681","abstract_canon_sha256":"26d39777ac0cc167e8f123e52c9f06e8b6ae06ef74fecf5113a58d1f0b9493d8"},"schema_version":"1.0"},"canonical_sha256":"b1528709207551dc9f741a38a45350c46c9c7363c7ad9bd2cd83c3845827f161","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:12.363984Z","signature_b64":"UM2H75Ap291zQVTHcLAQhwrDxDFP7VGDgCOvzbEYCfKO4aZaNa9qOu7hwXEoxsm9K6gDvqk+mkoGngl9FVYZCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b1528709207551dc9f741a38a45350c46c9c7363c7ad9bd2cd83c3845827f161","last_reissued_at":"2026-05-17T23:56:12.363257Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:12.363257Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.01074","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":"2026-05-17T23:56:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PLImvFjQBJ5m6tGfAg9hETVxouupUccfCbDt2o2wnLNuoCKSyT4OJmlbIxIwqEpTaP6w7SKurNiOEEkmFhbaDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T09:21:44.532047Z"},"content_sha256":"9a7fb9e393017d1297a5ce8bb0aa9faea18ad807f1641f2bb5fdbb768efa597f","schema_version":"1.0","event_id":"sha256:9a7fb9e393017d1297a5ce8bb0aa9faea18ad807f1641f2bb5fdbb768efa597f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:WFJIOCJAOVI5ZH3UDI4KIU2QYR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Bo Zhang, Hailong Ma, Ruijun Xu, Xiangxiang Chu","submitted_at":"2019-01-04T12:21:56Z","abstract_excerpt":"Fabricating neural models for a wide range of mobile devices demands for a specific design of networks due to highly constrained resources. Both evolution algorithms (EA) and reinforced learning methods (RL) have been dedicated to solve neural architecture search problems. However, these combinations usually concentrate on a single objective such as the error rate of image classification. They also fail to harness the very benefits from both sides. In this paper, we present a new multi-objective oriented algorithm called MoreMNAS (Multi-Objective Reinforced Evolution in Mobile Neural Architect"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.01074","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":""},"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":"2026-05-17T23:56:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ksJGMB/chwaSx5PZWc5Z+WX3vyKf2xQh5IELAVukLGJ5KsVTbQDjGodclgjjm0kIHXA4qIh2MFN66X60nmo4Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T09:21:44.532840Z"},"content_sha256":"e7fb12256ad600b6911d1bde36bac2a86ed901c19b3b95a272dbbbba3224c367","schema_version":"1.0","event_id":"sha256:e7fb12256ad600b6911d1bde36bac2a86ed901c19b3b95a272dbbbba3224c367"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WFJIOCJAOVI5ZH3UDI4KIU2QYR/bundle.json","state_url":"https://pith.science/pith/WFJIOCJAOVI5ZH3UDI4KIU2QYR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WFJIOCJAOVI5ZH3UDI4KIU2QYR/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":"2026-08-15T09:21:44Z","links":{"resolver":"https://pith.science/pith/WFJIOCJAOVI5ZH3UDI4KIU2QYR","bundle":"https://pith.science/pith/WFJIOCJAOVI5ZH3UDI4KIU2QYR/bundle.json","state":"https://pith.science/pith/WFJIOCJAOVI5ZH3UDI4KIU2QYR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WFJIOCJAOVI5ZH3UDI4KIU2QYR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:WFJIOCJAOVI5ZH3UDI4KIU2QYR","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":"26d39777ac0cc167e8f123e52c9f06e8b6ae06ef74fecf5113a58d1f0b9493d8","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-01-04T12:21:56Z","title_canon_sha256":"d088868fed970c29e0071a756b65ca38683cf46d59b41e7207af42d924d2d681"},"schema_version":"1.0","source":{"id":"1901.01074","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.01074","created_at":"2026-05-17T23:56:12Z"},{"alias_kind":"arxiv_version","alias_value":"1901.01074v3","created_at":"2026-05-17T23:56:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.01074","created_at":"2026-05-17T23:56:12Z"},{"alias_kind":"pith_short_12","alias_value":"WFJIOCJAOVI5","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"WFJIOCJAOVI5ZH3U","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"WFJIOCJA","created_at":"2026-05-18T12:33:30Z"}],"graph_snapshots":[{"event_id":"sha256:e7fb12256ad600b6911d1bde36bac2a86ed901c19b3b95a272dbbbba3224c367","target":"graph","created_at":"2026-05-17T23:56:12Z","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"},"paper":{"abstract_excerpt":"Fabricating neural models for a wide range of mobile devices demands for a specific design of networks due to highly constrained resources. Both evolution algorithms (EA) and reinforced learning methods (RL) have been dedicated to solve neural architecture search problems. However, these combinations usually concentrate on a single objective such as the error rate of image classification. They also fail to harness the very benefits from both sides. In this paper, we present a new multi-objective oriented algorithm called MoreMNAS (Multi-Objective Reinforced Evolution in Mobile Neural Architect","authors_text":"Bo Zhang, Hailong Ma, Ruijun Xu, Xiangxiang Chu","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-01-04T12:21:56Z","title":"Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.01074","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":"sha256:9a7fb9e393017d1297a5ce8bb0aa9faea18ad807f1641f2bb5fdbb768efa597f","target":"record","created_at":"2026-05-17T23:56:12Z","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":"26d39777ac0cc167e8f123e52c9f06e8b6ae06ef74fecf5113a58d1f0b9493d8","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-01-04T12:21:56Z","title_canon_sha256":"d088868fed970c29e0071a756b65ca38683cf46d59b41e7207af42d924d2d681"},"schema_version":"1.0","source":{"id":"1901.01074","kind":"arxiv","version":3}},"canonical_sha256":"b1528709207551dc9f741a38a45350c46c9c7363c7ad9bd2cd83c3845827f161","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b1528709207551dc9f741a38a45350c46c9c7363c7ad9bd2cd83c3845827f161","first_computed_at":"2026-05-17T23:56:12.363257Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:56:12.363257Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UM2H75Ap291zQVTHcLAQhwrDxDFP7VGDgCOvzbEYCfKO4aZaNa9qOu7hwXEoxsm9K6gDvqk+mkoGngl9FVYZCg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:56:12.363984Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.01074","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9a7fb9e393017d1297a5ce8bb0aa9faea18ad807f1641f2bb5fdbb768efa597f","sha256:e7fb12256ad600b6911d1bde36bac2a86ed901c19b3b95a272dbbbba3224c367"],"state_sha256":"5ef73760a334cd33c14d63441ede682181669ac37b21c8c6a700da2e5d9a2144"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yk8ibgUrLU0MfrlQZ4FiLLaiC06ei/4JkieesCBmMBBvQld/oUtXJRAOYD1VU7CilMXbPOBNMt8lpmWrQ2wnDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T09:21:44.538514Z","bundle_sha256":"6e0b80bd8cc7eda42c381e425b53d574d21bde6d54ab5f8ea4c0a8a9702b8931"}}