{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:I4XSGWLJBWWTLKKYDLTDNULZEC","short_pith_number":"pith:I4XSGWLJ","canonical_record":{"source":{"id":"1806.10332","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-06-27T08:12:01Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"23abd48a571819bc1291290b71c255aed65bf8e84b433ff5530f7d5364611a7a","abstract_canon_sha256":"d85b580ce536da112766dacb709dfde12f794b55770522959bae07cb5b4cec9a"},"schema_version":"1.0"},"canonical_sha256":"472f2359690dad35a9581ae636d17920a2c6a7de8630934b90bd26a4ffc29f06","source":{"kind":"arxiv","id":"1806.10332","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.10332","created_at":"2026-05-17T23:59:22Z"},{"alias_kind":"arxiv_version","alias_value":"1806.10332v2","created_at":"2026-05-17T23:59:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.10332","created_at":"2026-05-17T23:59:22Z"},{"alias_kind":"pith_short_12","alias_value":"I4XSGWLJBWWT","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"I4XSGWLJBWWTLKKY","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"I4XSGWLJ","created_at":"2026-05-18T12:32:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:I4XSGWLJBWWTLKKYDLTDNULZEC","target":"record","payload":{"canonical_record":{"source":{"id":"1806.10332","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-06-27T08:12:01Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"23abd48a571819bc1291290b71c255aed65bf8e84b433ff5530f7d5364611a7a","abstract_canon_sha256":"d85b580ce536da112766dacb709dfde12f794b55770522959bae07cb5b4cec9a"},"schema_version":"1.0"},"canonical_sha256":"472f2359690dad35a9581ae636d17920a2c6a7de8630934b90bd26a4ffc29f06","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:22.593163Z","signature_b64":"gCVZskzKOzHbBMi/8iBxrUvmkUd2twHNr34SSqLtKAWCrQJiuPZ1FqR9aQduC/fqp7Lak8AhD3Y2nMfctel4Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"472f2359690dad35a9581ae636d17920a2c6a7de8630934b90bd26a4ffc29f06","last_reissued_at":"2026-05-17T23:59:22.592829Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:22.592829Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.10332","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:59:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PoyhnfGJGr5wHGgDHId8P3ikEN/s7kiAkXjVZaIJL4e4Z5AS5CYZL71NIA92tgjgRI9mJOgnmUyfsVcdwXThAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T00:08:17.243633Z"},"content_sha256":"178f22a721f3e852573b3ba38eb04c079696a69da9965f49410642e0b398e576","schema_version":"1.0","event_id":"sha256:178f22a721f3e852573b3ba38eb04c079696a69da9965f49410642e0b398e576"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:I4XSGWLJBWWTLKKYDLTDNULZEC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chi-Hung Hsu, Chun-Hao Liu, Da-Cheng Juan, Hsin-Ping Chou, Jhao-Hong Liang, Jia-Yu Pan, Shih-Chieh Chang, Shu-Huan Chang, Wei Wei, Yu-Ting Chen","submitted_at":"2018-06-27T08:12:01Z","abstract_excerpt":"Recent studies on neural architecture search have shown that automatically designed neural networks perform as good as expert-crafted architectures. While most existing works aim at finding architectures that optimize the prediction accuracy, these architectures may have complexity and is therefore not suitable being deployed on certain computing environment (e.g., with limited power budgets). We propose MONAS, a framework for Multi-Objective Neural Architectural Search that employs reward functions considering both prediction accuracy and other important objectives (e.g., power consumption) w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.10332","kind":"arxiv","version":2},"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":"2026-05-17T23:59:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Nc7hMaG1S2LKlekzpYmFKhPX17haiYBFmRxuu5KzJJiFGM7YmSvWYYu/Pv/XfBdyev6ox68Dfazr9JixJ+/2Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T00:08:17.243993Z"},"content_sha256":"25b4cdfb005e468dfb06f3b7c3a99f12dbc8a1fed929b3ed0144d9b1b4c37cdb","schema_version":"1.0","event_id":"sha256:25b4cdfb005e468dfb06f3b7c3a99f12dbc8a1fed929b3ed0144d9b1b4c37cdb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I4XSGWLJBWWTLKKYDLTDNULZEC/bundle.json","state_url":"https://pith.science/pith/I4XSGWLJBWWTLKKYDLTDNULZEC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I4XSGWLJBWWTLKKYDLTDNULZEC/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":"2026-05-26T00:08:17Z","links":{"resolver":"https://pith.science/pith/I4XSGWLJBWWTLKKYDLTDNULZEC","bundle":"https://pith.science/pith/I4XSGWLJBWWTLKKYDLTDNULZEC/bundle.json","state":"https://pith.science/pith/I4XSGWLJBWWTLKKYDLTDNULZEC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I4XSGWLJBWWTLKKYDLTDNULZEC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:I4XSGWLJBWWTLKKYDLTDNULZEC","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":"d85b580ce536da112766dacb709dfde12f794b55770522959bae07cb5b4cec9a","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-06-27T08:12:01Z","title_canon_sha256":"23abd48a571819bc1291290b71c255aed65bf8e84b433ff5530f7d5364611a7a"},"schema_version":"1.0","source":{"id":"1806.10332","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.10332","created_at":"2026-05-17T23:59:22Z"},{"alias_kind":"arxiv_version","alias_value":"1806.10332v2","created_at":"2026-05-17T23:59:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.10332","created_at":"2026-05-17T23:59:22Z"},{"alias_kind":"pith_short_12","alias_value":"I4XSGWLJBWWT","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"I4XSGWLJBWWTLKKY","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"I4XSGWLJ","created_at":"2026-05-18T12:32:28Z"}],"graph_snapshots":[{"event_id":"sha256:25b4cdfb005e468dfb06f3b7c3a99f12dbc8a1fed929b3ed0144d9b1b4c37cdb","target":"graph","created_at":"2026-05-17T23:59:22Z","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":"Recent studies on neural architecture search have shown that automatically designed neural networks perform as good as expert-crafted architectures. While most existing works aim at finding architectures that optimize the prediction accuracy, these architectures may have complexity and is therefore not suitable being deployed on certain computing environment (e.g., with limited power budgets). We propose MONAS, a framework for Multi-Objective Neural Architectural Search that employs reward functions considering both prediction accuracy and other important objectives (e.g., power consumption) w","authors_text":"Chi-Hung Hsu, Chun-Hao Liu, Da-Cheng Juan, Hsin-Ping Chou, Jhao-Hong Liang, Jia-Yu Pan, Shih-Chieh Chang, Shu-Huan Chang, Wei Wei, Yu-Ting Chen","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-06-27T08:12:01Z","title":"MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.10332","kind":"arxiv","version":2},"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":"sha256:178f22a721f3e852573b3ba38eb04c079696a69da9965f49410642e0b398e576","target":"record","created_at":"2026-05-17T23:59:22Z","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":"d85b580ce536da112766dacb709dfde12f794b55770522959bae07cb5b4cec9a","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-06-27T08:12:01Z","title_canon_sha256":"23abd48a571819bc1291290b71c255aed65bf8e84b433ff5530f7d5364611a7a"},"schema_version":"1.0","source":{"id":"1806.10332","kind":"arxiv","version":2}},"canonical_sha256":"472f2359690dad35a9581ae636d17920a2c6a7de8630934b90bd26a4ffc29f06","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"472f2359690dad35a9581ae636d17920a2c6a7de8630934b90bd26a4ffc29f06","first_computed_at":"2026-05-17T23:59:22.592829Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:59:22.592829Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gCVZskzKOzHbBMi/8iBxrUvmkUd2twHNr34SSqLtKAWCrQJiuPZ1FqR9aQduC/fqp7Lak8AhD3Y2nMfctel4Bg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:59:22.593163Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.10332","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:178f22a721f3e852573b3ba38eb04c079696a69da9965f49410642e0b398e576","sha256:25b4cdfb005e468dfb06f3b7c3a99f12dbc8a1fed929b3ed0144d9b1b4c37cdb"],"state_sha256":"861dc56de87b69d29531a215490e64a34193cb5b61c84d4e3717feed03ee6f57"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ivbE97YQPZM3WWPPekLG05HpX2ivCRiBEq2bX17D56cZwppMiR/dap9BWZSYAPo/KE5Ywwd8R0BnIXAr7pPoDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T00:08:17.246975Z","bundle_sha256":"1607fb1485c038b77494ac368b4e4be4554769bb3122e2143601254741ac5458"}}