{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:ZSIWYG44WOD4PNTYISZYO5QNM2","short_pith_number":"pith:ZSIWYG44","canonical_record":{"source":{"id":"1805.07500","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-19T03:16:44Z","cross_cats_sorted":["cs.NE","stat.ML"],"title_canon_sha256":"e9b7a428dfaf69cbc6dc08ae1ada6769a0b4fcdd7539a28220f809234d9b4ea5","abstract_canon_sha256":"54aeeb0b0671f1f6376b4f5d76e4c698dcb2133ac4833a616b807dc95485b243"},"schema_version":"1.0"},"canonical_sha256":"cc916c1b9cb387c7b67844b387760d66abd97bda540289be6e7174c8c6bd289d","source":{"kind":"arxiv","id":"1805.07500","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.07500","created_at":"2026-05-17T23:51:44Z"},{"alias_kind":"arxiv_version","alias_value":"1805.07500v2","created_at":"2026-05-17T23:51:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.07500","created_at":"2026-05-17T23:51:44Z"},{"alias_kind":"pith_short_12","alias_value":"ZSIWYG44WOD4","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"ZSIWYG44WOD4PNTY","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"ZSIWYG44","created_at":"2026-05-18T12:33:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:ZSIWYG44WOD4PNTYISZYO5QNM2","target":"record","payload":{"canonical_record":{"source":{"id":"1805.07500","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-19T03:16:44Z","cross_cats_sorted":["cs.NE","stat.ML"],"title_canon_sha256":"e9b7a428dfaf69cbc6dc08ae1ada6769a0b4fcdd7539a28220f809234d9b4ea5","abstract_canon_sha256":"54aeeb0b0671f1f6376b4f5d76e4c698dcb2133ac4833a616b807dc95485b243"},"schema_version":"1.0"},"canonical_sha256":"cc916c1b9cb387c7b67844b387760d66abd97bda540289be6e7174c8c6bd289d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:44.186337Z","signature_b64":"SwzwLlgDevM7JOuAgJyg4wV8zvlITlfN1Vs0eKBHDBzYLFHKK+b2IXcKn5C1Il34U8EF7P6ccWhYRbjQl/25Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc916c1b9cb387c7b67844b387760d66abd97bda540289be6e7174c8c6bd289d","last_reissued_at":"2026-05-17T23:51:44.185572Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:44.185572Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.07500","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":"2026-05-17T23:51:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LI6yVyUvejnKYmL+RSQZ5KIErCF1Wdi93RG7RYcxcCM72t2vW9q3Xg8g713Pkoi48n5K2824X4T7vvYytzgJBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T03:20:43.412481Z"},"content_sha256":"64e041ac879827e35dd8a67cbcd76e79dc559d8d8e038e3213ebb3998b4e0900","schema_version":"1.0","event_id":"sha256:64e041ac879827e35dd8a67cbcd76e79dc559d8d8e038e3213ebb3998b4e0900"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:ZSIWYG44WOD4PNTYISZYO5QNM2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GADAM: Genetic-Evolutionary ADAM for Deep Neural Network Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE","stat.ML"],"primary_cat":"cs.LG","authors_text":"Fisher B. Gouza, Jiawei Zhang","submitted_at":"2018-05-19T03:16:44Z","abstract_excerpt":"Deep neural network learning can be formulated as a non-convex optimization problem. Existing optimization algorithms, e.g., Adam, can learn the models fast, but may get stuck in local optima easily. In this paper, we introduce a novel optimization algorithm, namely GADAM (Genetic-Evolutionary Adam). GADAM learns deep neural network models based on a number of unit models generations by generations: it trains the unit models with Adam, and evolves them to the new generations with genetic algorithm. We will show that GADAM can effectively jump out of the local optima in the learning process to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.07500","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":"2026-05-17T23:51:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QoSFzsa4noYlIeeDq9zHV+8s1fxN8gbqYp7KVv/II0VHDNc7BC4mfBaXXPf5N/jpP9tAKaZeEOzmxfyqYh2DCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T03:20:43.413175Z"},"content_sha256":"24f3427980722e1dd1aae3cd9055bbe77aa182a9615be9a8fd56077e5136d051","schema_version":"1.0","event_id":"sha256:24f3427980722e1dd1aae3cd9055bbe77aa182a9615be9a8fd56077e5136d051"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZSIWYG44WOD4PNTYISZYO5QNM2/bundle.json","state_url":"https://pith.science/pith/ZSIWYG44WOD4PNTYISZYO5QNM2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZSIWYG44WOD4PNTYISZYO5QNM2/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":"2026-06-11T03:20:43Z","links":{"resolver":"https://pith.science/pith/ZSIWYG44WOD4PNTYISZYO5QNM2","bundle":"https://pith.science/pith/ZSIWYG44WOD4PNTYISZYO5QNM2/bundle.json","state":"https://pith.science/pith/ZSIWYG44WOD4PNTYISZYO5QNM2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZSIWYG44WOD4PNTYISZYO5QNM2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:ZSIWYG44WOD4PNTYISZYO5QNM2","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":"54aeeb0b0671f1f6376b4f5d76e4c698dcb2133ac4833a616b807dc95485b243","cross_cats_sorted":["cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-19T03:16:44Z","title_canon_sha256":"e9b7a428dfaf69cbc6dc08ae1ada6769a0b4fcdd7539a28220f809234d9b4ea5"},"schema_version":"1.0","source":{"id":"1805.07500","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.07500","created_at":"2026-05-17T23:51:44Z"},{"alias_kind":"arxiv_version","alias_value":"1805.07500v2","created_at":"2026-05-17T23:51:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.07500","created_at":"2026-05-17T23:51:44Z"},{"alias_kind":"pith_short_12","alias_value":"ZSIWYG44WOD4","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"ZSIWYG44WOD4PNTY","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"ZSIWYG44","created_at":"2026-05-18T12:33:07Z"}],"graph_snapshots":[{"event_id":"sha256:24f3427980722e1dd1aae3cd9055bbe77aa182a9615be9a8fd56077e5136d051","target":"graph","created_at":"2026-05-17T23:51:44Z","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":"Deep neural network learning can be formulated as a non-convex optimization problem. Existing optimization algorithms, e.g., Adam, can learn the models fast, but may get stuck in local optima easily. In this paper, we introduce a novel optimization algorithm, namely GADAM (Genetic-Evolutionary Adam). GADAM learns deep neural network models based on a number of unit models generations by generations: it trains the unit models with Adam, and evolves them to the new generations with genetic algorithm. We will show that GADAM can effectively jump out of the local optima in the learning process to ","authors_text":"Fisher B. Gouza, Jiawei Zhang","cross_cats":["cs.NE","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-19T03:16:44Z","title":"GADAM: Genetic-Evolutionary ADAM for Deep Neural Network Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.07500","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":"sha256:64e041ac879827e35dd8a67cbcd76e79dc559d8d8e038e3213ebb3998b4e0900","target":"record","created_at":"2026-05-17T23:51:44Z","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":"54aeeb0b0671f1f6376b4f5d76e4c698dcb2133ac4833a616b807dc95485b243","cross_cats_sorted":["cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-19T03:16:44Z","title_canon_sha256":"e9b7a428dfaf69cbc6dc08ae1ada6769a0b4fcdd7539a28220f809234d9b4ea5"},"schema_version":"1.0","source":{"id":"1805.07500","kind":"arxiv","version":2}},"canonical_sha256":"cc916c1b9cb387c7b67844b387760d66abd97bda540289be6e7174c8c6bd289d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cc916c1b9cb387c7b67844b387760d66abd97bda540289be6e7174c8c6bd289d","first_computed_at":"2026-05-17T23:51:44.185572Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:51:44.185572Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SwzwLlgDevM7JOuAgJyg4wV8zvlITlfN1Vs0eKBHDBzYLFHKK+b2IXcKn5C1Il34U8EF7P6ccWhYRbjQl/25Cg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:51:44.186337Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.07500","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:64e041ac879827e35dd8a67cbcd76e79dc559d8d8e038e3213ebb3998b4e0900","sha256:24f3427980722e1dd1aae3cd9055bbe77aa182a9615be9a8fd56077e5136d051"],"state_sha256":"3bea5c8bac0e8d49d47830138455cdbe5219e5f56cd48c01e5cbff6196f3b7c6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pUrhLoauHaNdIete8cl1IaT9v8vX+Q8nNXGHVR6WaQvGSfLPK2KWwmsVMJVVQ15deznb7FAYuhXDYZhOMc2QCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T03:20:43.416874Z","bundle_sha256":"d132d80b4c2b3f903c64cc025a07adba57b2c27ecfc91325b90bb355c818cd40"}}