{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:2IBSK73M4F6ECYFLGZPEECP45I","short_pith_number":"pith:2IBSK73M","canonical_record":{"source":{"id":"1809.03242","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-09-10T11:36:22Z","cross_cats_sorted":[],"title_canon_sha256":"373a37ec6984dee2f7e5b29976886df0755da5815f4d11cff771701890f88e01","abstract_canon_sha256":"9b790052f19e56cf60c12541c442bbf25f214435ba496b51736894612edcad35"},"schema_version":"1.0"},"canonical_sha256":"d203257f6ce17c4160ab365e4209fcea1460bfcd8e7b5b207d842654a483f7ac","source":{"kind":"arxiv","id":"1809.03242","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.03242","created_at":"2026-05-18T00:06:09Z"},{"alias_kind":"arxiv_version","alias_value":"1809.03242v1","created_at":"2026-05-18T00:06:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.03242","created_at":"2026-05-18T00:06:09Z"},{"alias_kind":"pith_short_12","alias_value":"2IBSK73M4F6E","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"2IBSK73M4F6ECYFL","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"2IBSK73M","created_at":"2026-05-18T12:32:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:2IBSK73M4F6ECYFLGZPEECP45I","target":"record","payload":{"canonical_record":{"source":{"id":"1809.03242","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-09-10T11:36:22Z","cross_cats_sorted":[],"title_canon_sha256":"373a37ec6984dee2f7e5b29976886df0755da5815f4d11cff771701890f88e01","abstract_canon_sha256":"9b790052f19e56cf60c12541c442bbf25f214435ba496b51736894612edcad35"},"schema_version":"1.0"},"canonical_sha256":"d203257f6ce17c4160ab365e4209fcea1460bfcd8e7b5b207d842654a483f7ac","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:09.517499Z","signature_b64":"1hdbpMQ58H0DchEScwat5K//cFiVi6I13010MDSyV+A7Rn1c67leAxuLILWIf3eMznl81N/I7jVVLmMy0x9rCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d203257f6ce17c4160ab365e4209fcea1460bfcd8e7b5b207d842654a483f7ac","last_reissued_at":"2026-05-18T00:06:09.516705Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:09.516705Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.03242","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":"2026-05-18T00:06:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2FOz8JzPb15TmyenTq1DDVrUAgy/8yvEi/RwRvhZGgwpRt3xUc/2Sn6uDMVBnYUGZ2mmaY8/46mH5O2SyKbWAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T06:12:32.924243Z"},"content_sha256":"2380d3a53900a12a743b3be1f5454e90291226381bcbd5f36fd56bceaffae72f","schema_version":"1.0","event_id":"sha256:2380d3a53900a12a743b3be1f5454e90291226381bcbd5f36fd56bceaffae72f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:2IBSK73M4F6ECYFLGZPEECP45I","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Finding Better Topologies for Deep Convolutional Neural Networks by Evolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Honglei Zhang, Moncef Gabbouj, Serkan Kiranyaz","submitted_at":"2018-09-10T11:36:22Z","abstract_excerpt":"Due to the nonlinearity of artificial neural networks, designing topologies for deep convolutional neural networks (CNN) is a challenging task and often only heuristic approach, such as trial and error, can be applied. An evolutionary algorithm can solve optimization problems where the fitness landscape is unknown. However, evolutionary algorithms are computing resource intensive, which makes it difficult for problems when deep CNNs are involved. In this paper, we propose an evolutionary strategy to find better topologies for deep CNNs. Incorporating the concept of knowledge inheritance and kn"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.03242","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":"2026-05-18T00:06:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ikwAsHDgS+CO6+UjjZDeuWhztFsB57sOqUKIFCB0qCAYFOacll9QMhj0lTn1TIV3QODO5RFg3lRxz+8fjjNYCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T06:12:32.924922Z"},"content_sha256":"0f9cd742d7cf92fae0f3c86164ee98f1208c2ce54e042d95025ba96bbe56264a","schema_version":"1.0","event_id":"sha256:0f9cd742d7cf92fae0f3c86164ee98f1208c2ce54e042d95025ba96bbe56264a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2IBSK73M4F6ECYFLGZPEECP45I/bundle.json","state_url":"https://pith.science/pith/2IBSK73M4F6ECYFLGZPEECP45I/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2IBSK73M4F6ECYFLGZPEECP45I/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":"2026-06-09T06:12:32Z","links":{"resolver":"https://pith.science/pith/2IBSK73M4F6ECYFLGZPEECP45I","bundle":"https://pith.science/pith/2IBSK73M4F6ECYFLGZPEECP45I/bundle.json","state":"https://pith.science/pith/2IBSK73M4F6ECYFLGZPEECP45I/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2IBSK73M4F6ECYFLGZPEECP45I/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:2IBSK73M4F6ECYFLGZPEECP45I","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":"9b790052f19e56cf60c12541c442bbf25f214435ba496b51736894612edcad35","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-09-10T11:36:22Z","title_canon_sha256":"373a37ec6984dee2f7e5b29976886df0755da5815f4d11cff771701890f88e01"},"schema_version":"1.0","source":{"id":"1809.03242","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.03242","created_at":"2026-05-18T00:06:09Z"},{"alias_kind":"arxiv_version","alias_value":"1809.03242v1","created_at":"2026-05-18T00:06:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.03242","created_at":"2026-05-18T00:06:09Z"},{"alias_kind":"pith_short_12","alias_value":"2IBSK73M4F6E","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"2IBSK73M4F6ECYFL","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"2IBSK73M","created_at":"2026-05-18T12:32:02Z"}],"graph_snapshots":[{"event_id":"sha256:0f9cd742d7cf92fae0f3c86164ee98f1208c2ce54e042d95025ba96bbe56264a","target":"graph","created_at":"2026-05-18T00:06:09Z","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":"Due to the nonlinearity of artificial neural networks, designing topologies for deep convolutional neural networks (CNN) is a challenging task and often only heuristic approach, such as trial and error, can be applied. An evolutionary algorithm can solve optimization problems where the fitness landscape is unknown. However, evolutionary algorithms are computing resource intensive, which makes it difficult for problems when deep CNNs are involved. In this paper, we propose an evolutionary strategy to find better topologies for deep CNNs. Incorporating the concept of knowledge inheritance and kn","authors_text":"Honglei Zhang, Moncef Gabbouj, Serkan Kiranyaz","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-09-10T11:36:22Z","title":"Finding Better Topologies for Deep Convolutional Neural Networks by Evolution"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.03242","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":"sha256:2380d3a53900a12a743b3be1f5454e90291226381bcbd5f36fd56bceaffae72f","target":"record","created_at":"2026-05-18T00:06:09Z","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":"9b790052f19e56cf60c12541c442bbf25f214435ba496b51736894612edcad35","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-09-10T11:36:22Z","title_canon_sha256":"373a37ec6984dee2f7e5b29976886df0755da5815f4d11cff771701890f88e01"},"schema_version":"1.0","source":{"id":"1809.03242","kind":"arxiv","version":1}},"canonical_sha256":"d203257f6ce17c4160ab365e4209fcea1460bfcd8e7b5b207d842654a483f7ac","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d203257f6ce17c4160ab365e4209fcea1460bfcd8e7b5b207d842654a483f7ac","first_computed_at":"2026-05-18T00:06:09.516705Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:06:09.516705Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1hdbpMQ58H0DchEScwat5K//cFiVi6I13010MDSyV+A7Rn1c67leAxuLILWIf3eMznl81N/I7jVVLmMy0x9rCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:06:09.517499Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.03242","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2380d3a53900a12a743b3be1f5454e90291226381bcbd5f36fd56bceaffae72f","sha256:0f9cd742d7cf92fae0f3c86164ee98f1208c2ce54e042d95025ba96bbe56264a"],"state_sha256":"b50859a852fe7c82c08c09513d2a497182d0ae2f3efd09104330518cef9e3ccf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Jpgerl6qyqHNraLr/Fq1SfIHs6w48rGLYW492y9/nnWPgthM6NZRJjqY/IIg1o1PX4lF7mSuUKUfXarbkrm9Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T06:12:32.928702Z","bundle_sha256":"af0b3b2046dda0bb00cd74e3132de08e40bb2a4fc4de223cca70ac1805e730e7"}}