{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:7HZ6HXNA6X3VBENCK3KKJICCRV","short_pith_number":"pith:7HZ6HXNA","canonical_record":{"source":{"id":"1809.02233","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.CP","submitted_at":"2018-09-06T21:59:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"82bd1677d052d46d5d3b65260d1c4fc9a24a42edc099c7f9921be6b7e949eeaa","abstract_canon_sha256":"a114afb7bef2238d68311fddb3d82d32005204c98a2fe0752c10327e25bd57ea"},"schema_version":"1.0"},"canonical_sha256":"f9f3e3dda0f5f75091a256d4a4a0428d58de994dba7f90bf452e0592491f2578","source":{"kind":"arxiv","id":"1809.02233","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.02233","created_at":"2026-05-18T00:02:54Z"},{"alias_kind":"arxiv_version","alias_value":"1809.02233v4","created_at":"2026-05-18T00:02:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.02233","created_at":"2026-05-18T00:02:54Z"},{"alias_kind":"pith_short_12","alias_value":"7HZ6HXNA6X3V","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"7HZ6HXNA6X3VBENC","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"7HZ6HXNA","created_at":"2026-05-18T12:32:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:7HZ6HXNA6X3VBENCK3KKJICCRV","target":"record","payload":{"canonical_record":{"source":{"id":"1809.02233","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.CP","submitted_at":"2018-09-06T21:59:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"82bd1677d052d46d5d3b65260d1c4fc9a24a42edc099c7f9921be6b7e949eeaa","abstract_canon_sha256":"a114afb7bef2238d68311fddb3d82d32005204c98a2fe0752c10327e25bd57ea"},"schema_version":"1.0"},"canonical_sha256":"f9f3e3dda0f5f75091a256d4a4a0428d58de994dba7f90bf452e0592491f2578","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:54.637992Z","signature_b64":"FPtKvlSdesbuiYDQo0VJeCfIXxIfwh5kb+FbqnZbUrsJQv/ug4ag2sZKGFBIsx7rPs3u57yJMLlghLX1OHHGDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f9f3e3dda0f5f75091a256d4a4a0428d58de994dba7f90bf452e0592491f2578","last_reissued_at":"2026-05-18T00:02:54.637356Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:54.637356Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.02233","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:02:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/YLNqNaT8jODPhY6HKhgdvBldvNxGV0Vd9e7hIkJ5LgqVwafTXwDhTYkwKWPborF910Euzh7aeEmNrU1kHvoCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T18:23:25.360556Z"},"content_sha256":"251fc5aa4891a6a71b2e2058bd9797a5de13df806e316874fd28bb22a891016f","schema_version":"1.0","event_id":"sha256:251fc5aa4891a6a71b2e2058bd9797a5de13df806e316874fd28bb22a891016f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:7HZ6HXNA6X3VBENCK3KKJICCRV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deeply Learning Derivatives","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-fin.CP","authors_text":"Andrew Green, Ryan Ferguson","submitted_at":"2018-09-06T21:59:46Z","abstract_excerpt":"This paper uses deep learning to value derivatives. The approach is broadly applicable, and we use a call option on a basket of stocks as an example. We show that the deep learning model is accurate and very fast, capable of producing valuations a million times faster than traditional models. We develop a methodology to randomly generate appropriate training data and explore the impact of several parameters including layer width and depth, training data quality and quantity on model speed and accuracy."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.02233","kind":"arxiv","version":4},"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":"2026-05-18T00:02:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vyElIA1xCLKXrGF9kCD1AzZqkeJsZqFpqqDalZtn43avJTZRn8fpsyb7izfcFPp1Gu16OS88SZom5BzUPQMGBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T18:23:25.360923Z"},"content_sha256":"aa4e7b4bc0491c0fc8a5cc3d25638ce8da39767a9082ad27e9c901ece824f34a","schema_version":"1.0","event_id":"sha256:aa4e7b4bc0491c0fc8a5cc3d25638ce8da39767a9082ad27e9c901ece824f34a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7HZ6HXNA6X3VBENCK3KKJICCRV/bundle.json","state_url":"https://pith.science/pith/7HZ6HXNA6X3VBENCK3KKJICCRV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7HZ6HXNA6X3VBENCK3KKJICCRV/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":"2026-05-26T18:23:25Z","links":{"resolver":"https://pith.science/pith/7HZ6HXNA6X3VBENCK3KKJICCRV","bundle":"https://pith.science/pith/7HZ6HXNA6X3VBENCK3KKJICCRV/bundle.json","state":"https://pith.science/pith/7HZ6HXNA6X3VBENCK3KKJICCRV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7HZ6HXNA6X3VBENCK3KKJICCRV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:7HZ6HXNA6X3VBENCK3KKJICCRV","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":"a114afb7bef2238d68311fddb3d82d32005204c98a2fe0752c10327e25bd57ea","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.CP","submitted_at":"2018-09-06T21:59:46Z","title_canon_sha256":"82bd1677d052d46d5d3b65260d1c4fc9a24a42edc099c7f9921be6b7e949eeaa"},"schema_version":"1.0","source":{"id":"1809.02233","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.02233","created_at":"2026-05-18T00:02:54Z"},{"alias_kind":"arxiv_version","alias_value":"1809.02233v4","created_at":"2026-05-18T00:02:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.02233","created_at":"2026-05-18T00:02:54Z"},{"alias_kind":"pith_short_12","alias_value":"7HZ6HXNA6X3V","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"7HZ6HXNA6X3VBENC","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"7HZ6HXNA","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:aa4e7b4bc0491c0fc8a5cc3d25638ce8da39767a9082ad27e9c901ece824f34a","target":"graph","created_at":"2026-05-18T00:02:54Z","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":"This paper uses deep learning to value derivatives. The approach is broadly applicable, and we use a call option on a basket of stocks as an example. We show that the deep learning model is accurate and very fast, capable of producing valuations a million times faster than traditional models. We develop a methodology to randomly generate appropriate training data and explore the impact of several parameters including layer width and depth, training data quality and quantity on model speed and accuracy.","authors_text":"Andrew Green, Ryan Ferguson","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.CP","submitted_at":"2018-09-06T21:59:46Z","title":"Deeply Learning Derivatives"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.02233","kind":"arxiv","version":4},"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":"sha256:251fc5aa4891a6a71b2e2058bd9797a5de13df806e316874fd28bb22a891016f","target":"record","created_at":"2026-05-18T00:02:54Z","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":"a114afb7bef2238d68311fddb3d82d32005204c98a2fe0752c10327e25bd57ea","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.CP","submitted_at":"2018-09-06T21:59:46Z","title_canon_sha256":"82bd1677d052d46d5d3b65260d1c4fc9a24a42edc099c7f9921be6b7e949eeaa"},"schema_version":"1.0","source":{"id":"1809.02233","kind":"arxiv","version":4}},"canonical_sha256":"f9f3e3dda0f5f75091a256d4a4a0428d58de994dba7f90bf452e0592491f2578","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f9f3e3dda0f5f75091a256d4a4a0428d58de994dba7f90bf452e0592491f2578","first_computed_at":"2026-05-18T00:02:54.637356Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:02:54.637356Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FPtKvlSdesbuiYDQo0VJeCfIXxIfwh5kb+FbqnZbUrsJQv/ug4ag2sZKGFBIsx7rPs3u57yJMLlghLX1OHHGDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:02:54.637992Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.02233","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:251fc5aa4891a6a71b2e2058bd9797a5de13df806e316874fd28bb22a891016f","sha256:aa4e7b4bc0491c0fc8a5cc3d25638ce8da39767a9082ad27e9c901ece824f34a"],"state_sha256":"5206b5361c5856704aa3aada9779346f0699dad8f53b9f11704846029345e553"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NOhFsoyWtXnMmP1D30jLUgoH0Y1xJRrYrUizjwOGw4F3GlflcX7MdDULTuIXgQjtyRzO/O25lsP0fqujahjMAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T18:23:25.363728Z","bundle_sha256":"fa93a0ae6d0c012112bb63a5b4e9a88d53e5297912e3a7cdce3cf167b7da0a4c"}}