{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:UT3FI73A6O5UVQPUMA3RPAEJ6U","short_pith_number":"pith:UT3FI73A","canonical_record":{"source":{"id":"1611.02120","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-11-07T15:38:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"bea1ae672d322e2b951a8e417b4762faa76c47ababc2940ffb09683aa4bff261","abstract_canon_sha256":"056c74a537e76df910b22345ef1486e9c524c42bf947dc90ab57e7ebd0e9d378"},"schema_version":"1.0"},"canonical_sha256":"a4f6547f60f3bb4ac1f46037178089f5229c88fbf83db88b2e9cb538d5008ead","source":{"kind":"arxiv","id":"1611.02120","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.02120","created_at":"2026-05-18T01:00:03Z"},{"alias_kind":"arxiv_version","alias_value":"1611.02120v1","created_at":"2026-05-18T01:00:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.02120","created_at":"2026-05-18T01:00:03Z"},{"alias_kind":"pith_short_12","alias_value":"UT3FI73A6O5U","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_16","alias_value":"UT3FI73A6O5UVQPU","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_8","alias_value":"UT3FI73A","created_at":"2026-05-18T12:30:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:UT3FI73A6O5UVQPUMA3RPAEJ6U","target":"record","payload":{"canonical_record":{"source":{"id":"1611.02120","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-11-07T15:38:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"bea1ae672d322e2b951a8e417b4762faa76c47ababc2940ffb09683aa4bff261","abstract_canon_sha256":"056c74a537e76df910b22345ef1486e9c524c42bf947dc90ab57e7ebd0e9d378"},"schema_version":"1.0"},"canonical_sha256":"a4f6547f60f3bb4ac1f46037178089f5229c88fbf83db88b2e9cb538d5008ead","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:00:03.671642Z","signature_b64":"mKk3Jn5zk/8l9/rXEEASS5Vfudq4RWeBl5bmYCm4LBmXYFIhvfHL04YyPB45gRH2ILJ/uZH1MX5C6QcNOsWpBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a4f6547f60f3bb4ac1f46037178089f5229c88fbf83db88b2e9cb538d5008ead","last_reissued_at":"2026-05-18T01:00:03.671008Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:00:03.671008Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.02120","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":"2026-05-18T01:00:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z/jaj78XIeDO2BEWRvT6hE2r9JZIvPK+kbJ+BZ6yPYjn263khTDEeuHTZjZEdAfgwajPfFiBg5VtWWL2KCOjDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T05:57:29.756568Z"},"content_sha256":"ff988e5a34b5c786edf558282a7503553aeab2a217553fde746ac67cbba345bc","schema_version":"1.0","event_id":"sha256:ff988e5a34b5c786edf558282a7503553aeab2a217553fde746ac67cbba345bc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:UT3FI73A6O5UVQPUMA3RPAEJ6U","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Brett H. Meyer, Guang Yang, Sean C. Smithson, Warren J. Gross","submitted_at":"2016-11-07T15:38:39Z","abstract_excerpt":"Artificial neural networks have gone through a recent rise in popularity, achieving state-of-the-art results in various fields, including image classification, speech recognition, and automated control. Both the performance and computational complexity of such models are heavily dependant on the design of characteristic hyper-parameters (e.g., number of hidden layers, nodes per layer, or choice of activation functions), which have traditionally been optimized manually. With machine learning penetrating low-power mobile and embedded areas, the need to optimize not only for performance (accuracy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.02120","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":""},"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":"2026-05-18T01:00:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/jNwWvNyK9BsBNMqkbzYs3vC6VsiTPQx8bW4NycQGCWIPUjoM0zovaB8+sPmvHIsPzxGCsabsxqDeEqyO46rCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T05:57:29.756918Z"},"content_sha256":"acace9a570ce4a9d8e418279fea0c14b2d895016c33ef87af44499d9a9a54693","schema_version":"1.0","event_id":"sha256:acace9a570ce4a9d8e418279fea0c14b2d895016c33ef87af44499d9a9a54693"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UT3FI73A6O5UVQPUMA3RPAEJ6U/bundle.json","state_url":"https://pith.science/pith/UT3FI73A6O5UVQPUMA3RPAEJ6U/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UT3FI73A6O5UVQPUMA3RPAEJ6U/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":"2026-05-28T05:57:29Z","links":{"resolver":"https://pith.science/pith/UT3FI73A6O5UVQPUMA3RPAEJ6U","bundle":"https://pith.science/pith/UT3FI73A6O5UVQPUMA3RPAEJ6U/bundle.json","state":"https://pith.science/pith/UT3FI73A6O5UVQPUMA3RPAEJ6U/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UT3FI73A6O5UVQPUMA3RPAEJ6U/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:UT3FI73A6O5UVQPUMA3RPAEJ6U","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":"056c74a537e76df910b22345ef1486e9c524c42bf947dc90ab57e7ebd0e9d378","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-11-07T15:38:39Z","title_canon_sha256":"bea1ae672d322e2b951a8e417b4762faa76c47ababc2940ffb09683aa4bff261"},"schema_version":"1.0","source":{"id":"1611.02120","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.02120","created_at":"2026-05-18T01:00:03Z"},{"alias_kind":"arxiv_version","alias_value":"1611.02120v1","created_at":"2026-05-18T01:00:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.02120","created_at":"2026-05-18T01:00:03Z"},{"alias_kind":"pith_short_12","alias_value":"UT3FI73A6O5U","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_16","alias_value":"UT3FI73A6O5UVQPU","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_8","alias_value":"UT3FI73A","created_at":"2026-05-18T12:30:46Z"}],"graph_snapshots":[{"event_id":"sha256:acace9a570ce4a9d8e418279fea0c14b2d895016c33ef87af44499d9a9a54693","target":"graph","created_at":"2026-05-18T01:00:03Z","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":"Artificial neural networks have gone through a recent rise in popularity, achieving state-of-the-art results in various fields, including image classification, speech recognition, and automated control. Both the performance and computational complexity of such models are heavily dependant on the design of characteristic hyper-parameters (e.g., number of hidden layers, nodes per layer, or choice of activation functions), which have traditionally been optimized manually. With machine learning penetrating low-power mobile and embedded areas, the need to optimize not only for performance (accuracy","authors_text":"Brett H. Meyer, Guang Yang, Sean C. Smithson, Warren J. Gross","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-11-07T15:38:39Z","title":"Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.02120","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:ff988e5a34b5c786edf558282a7503553aeab2a217553fde746ac67cbba345bc","target":"record","created_at":"2026-05-18T01:00:03Z","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":"056c74a537e76df910b22345ef1486e9c524c42bf947dc90ab57e7ebd0e9d378","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2016-11-07T15:38:39Z","title_canon_sha256":"bea1ae672d322e2b951a8e417b4762faa76c47ababc2940ffb09683aa4bff261"},"schema_version":"1.0","source":{"id":"1611.02120","kind":"arxiv","version":1}},"canonical_sha256":"a4f6547f60f3bb4ac1f46037178089f5229c88fbf83db88b2e9cb538d5008ead","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a4f6547f60f3bb4ac1f46037178089f5229c88fbf83db88b2e9cb538d5008ead","first_computed_at":"2026-05-18T01:00:03.671008Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:00:03.671008Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mKk3Jn5zk/8l9/rXEEASS5Vfudq4RWeBl5bmYCm4LBmXYFIhvfHL04YyPB45gRH2ILJ/uZH1MX5C6QcNOsWpBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:00:03.671642Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.02120","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ff988e5a34b5c786edf558282a7503553aeab2a217553fde746ac67cbba345bc","sha256:acace9a570ce4a9d8e418279fea0c14b2d895016c33ef87af44499d9a9a54693"],"state_sha256":"bc3f11e863ee1e537d9ca9513aa115b2fc4546fad194d80f68d4c0369e31b0f5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UnTe5965yE1je/sA3j3sQ2LXkpw8c0xsSarQlO3a3oyAZIGob/k1VcXCVkcyBrsCGq6S3dlWnKykQ47+ELZODg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T05:57:29.758907Z","bundle_sha256":"7de16f71fb407b27c00864edf360c00ebf263f45385cf436d9c5338b9e5c2654"}}