{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:ANJ43DGHORWJ7AFWRIWXLG2IOS","short_pith_number":"pith:ANJ43DGH","canonical_record":{"source":{"id":"1809.05989","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-09-17T01:26:57Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"17061a2aaaf3508d19ae3b95c9ae25a8b0bb879d25c51ef1b92ec0399167e9de","abstract_canon_sha256":"f9033d35a97f7b4d7ea664cefffa70d5557d6a25d0aaa1140404bca34d519730"},"schema_version":"1.0"},"canonical_sha256":"0353cd8cc7746c9f80b68a2d759b48748782dc3f77f94a86e0c63fa2ef7ac756","source":{"kind":"arxiv","id":"1809.05989","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.05989","created_at":"2026-05-18T00:00:43Z"},{"alias_kind":"arxiv_version","alias_value":"1809.05989v2","created_at":"2026-05-18T00:00:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.05989","created_at":"2026-05-18T00:00:43Z"},{"alias_kind":"pith_short_12","alias_value":"ANJ43DGHORWJ","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_16","alias_value":"ANJ43DGHORWJ7AFW","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_8","alias_value":"ANJ43DGH","created_at":"2026-05-18T12:32:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:ANJ43DGHORWJ7AFWRIWXLG2IOS","target":"record","payload":{"canonical_record":{"source":{"id":"1809.05989","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-09-17T01:26:57Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"17061a2aaaf3508d19ae3b95c9ae25a8b0bb879d25c51ef1b92ec0399167e9de","abstract_canon_sha256":"f9033d35a97f7b4d7ea664cefffa70d5557d6a25d0aaa1140404bca34d519730"},"schema_version":"1.0"},"canonical_sha256":"0353cd8cc7746c9f80b68a2d759b48748782dc3f77f94a86e0c63fa2ef7ac756","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:43.038327Z","signature_b64":"N9EF38qn8FrPUpvOENSWIp1NfWL5Vv9zmvXjMmPNnVihusOGfFOzV9NBxBrDri+VfIoz+24bIV69WswBmQTJCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0353cd8cc7746c9f80b68a2d759b48748782dc3f77f94a86e0c63fa2ef7ac756","last_reissued_at":"2026-05-18T00:00:43.037961Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:43.037961Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.05989","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":"2026-05-18T00:00:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RmgZVRj3GeETgJu6LKQDyf57TfNMyZFKYp8Se+2+iu0dNM9EDe3rxOowWr7PggmVu8MzwemQyR9s/G7HhbqHAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T00:51:40.316853Z"},"content_sha256":"564783f6f2caab61c3dc9cfbae6d5af433680b6117b2064d58a8a44d1403d4e3","schema_version":"1.0","event_id":"sha256:564783f6f2caab61c3dc9cfbae6d5af433680b6117b2064d58a8a44d1403d4e3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:ANJ43DGHORWJ7AFWRIWXLG2IOS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.NE","authors_text":"Alexander Wong, Brendan Chwyl, Francis Li, Mohammad Javad Shafiee","submitted_at":"2018-09-17T01:26:57Z","abstract_excerpt":"The tremendous potential exhibited by deep learning is often offset by architectural and computational complexity, making widespread deployment a challenge for edge scenarios such as mobile and other consumer devices. To tackle this challenge, we explore the following idea: Can we learn generative machines to automatically generate deep neural networks with efficient network architectures? In this study, we introduce the idea of generative synthesis, which is premised on the intricate interplay between a generator-inquisitor pair that work in tandem to garner insights and learn to generate hig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.05989","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":"2026-05-18T00:00:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vc46ny3zehrPWRyRSsICDyhfcxl0s6b825CYLasKWR9zJ3oYGYxoWYqsR7SIlNl1Csec1uZfhU/2eaYNkW+bAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T00:51:40.317506Z"},"content_sha256":"2f8ff6a315642a8be074950e02c4c8d0ca177e11f9119d4a1edcf2e79148e23b","schema_version":"1.0","event_id":"sha256:2f8ff6a315642a8be074950e02c4c8d0ca177e11f9119d4a1edcf2e79148e23b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ANJ43DGHORWJ7AFWRIWXLG2IOS/bundle.json","state_url":"https://pith.science/pith/ANJ43DGHORWJ7AFWRIWXLG2IOS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ANJ43DGHORWJ7AFWRIWXLG2IOS/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":"2026-05-27T00:51:40Z","links":{"resolver":"https://pith.science/pith/ANJ43DGHORWJ7AFWRIWXLG2IOS","bundle":"https://pith.science/pith/ANJ43DGHORWJ7AFWRIWXLG2IOS/bundle.json","state":"https://pith.science/pith/ANJ43DGHORWJ7AFWRIWXLG2IOS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ANJ43DGHORWJ7AFWRIWXLG2IOS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:ANJ43DGHORWJ7AFWRIWXLG2IOS","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":"f9033d35a97f7b4d7ea664cefffa70d5557d6a25d0aaa1140404bca34d519730","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-09-17T01:26:57Z","title_canon_sha256":"17061a2aaaf3508d19ae3b95c9ae25a8b0bb879d25c51ef1b92ec0399167e9de"},"schema_version":"1.0","source":{"id":"1809.05989","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.05989","created_at":"2026-05-18T00:00:43Z"},{"alias_kind":"arxiv_version","alias_value":"1809.05989v2","created_at":"2026-05-18T00:00:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.05989","created_at":"2026-05-18T00:00:43Z"},{"alias_kind":"pith_short_12","alias_value":"ANJ43DGHORWJ","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_16","alias_value":"ANJ43DGHORWJ7AFW","created_at":"2026-05-18T12:32:13Z"},{"alias_kind":"pith_short_8","alias_value":"ANJ43DGH","created_at":"2026-05-18T12:32:13Z"}],"graph_snapshots":[{"event_id":"sha256:2f8ff6a315642a8be074950e02c4c8d0ca177e11f9119d4a1edcf2e79148e23b","target":"graph","created_at":"2026-05-18T00:00:43Z","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":"The tremendous potential exhibited by deep learning is often offset by architectural and computational complexity, making widespread deployment a challenge for edge scenarios such as mobile and other consumer devices. To tackle this challenge, we explore the following idea: Can we learn generative machines to automatically generate deep neural networks with efficient network architectures? In this study, we introduce the idea of generative synthesis, which is premised on the intricate interplay between a generator-inquisitor pair that work in tandem to garner insights and learn to generate hig","authors_text":"Alexander Wong, Brendan Chwyl, Francis Li, Mohammad Javad Shafiee","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-09-17T01:26:57Z","title":"FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.05989","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":"sha256:564783f6f2caab61c3dc9cfbae6d5af433680b6117b2064d58a8a44d1403d4e3","target":"record","created_at":"2026-05-18T00:00:43Z","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":"f9033d35a97f7b4d7ea664cefffa70d5557d6a25d0aaa1140404bca34d519730","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2018-09-17T01:26:57Z","title_canon_sha256":"17061a2aaaf3508d19ae3b95c9ae25a8b0bb879d25c51ef1b92ec0399167e9de"},"schema_version":"1.0","source":{"id":"1809.05989","kind":"arxiv","version":2}},"canonical_sha256":"0353cd8cc7746c9f80b68a2d759b48748782dc3f77f94a86e0c63fa2ef7ac756","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0353cd8cc7746c9f80b68a2d759b48748782dc3f77f94a86e0c63fa2ef7ac756","first_computed_at":"2026-05-18T00:00:43.037961Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:43.037961Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"N9EF38qn8FrPUpvOENSWIp1NfWL5Vv9zmvXjMmPNnVihusOGfFOzV9NBxBrDri+VfIoz+24bIV69WswBmQTJCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:43.038327Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.05989","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:564783f6f2caab61c3dc9cfbae6d5af433680b6117b2064d58a8a44d1403d4e3","sha256:2f8ff6a315642a8be074950e02c4c8d0ca177e11f9119d4a1edcf2e79148e23b"],"state_sha256":"a007cf62381b84435e4284ce0848408f81a1d7f0b6d3655cef372ee78d3c1e27"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4drqPWUR+XG+tLdky4d3IBeY2yvRYvAq7j7JjpszbazjhzNlnLb++Ek7u8YQJgD3DDDhXyxY7uZYLNcGV3IgDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T00:51:40.320878Z","bundle_sha256":"209e8f31aed3fb390963456b6a4d84adc66419488f81852d36f16dd9360ed118"}}