{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:4JBANNCLIN3YBKZPF3OKZX7CP6","short_pith_number":"pith:4JBANNCL","schema_version":"1.0","canonical_sha256":"e24206b44b437780ab2f2edcacdfe27fb0ca71c408eecac7923447fd08c9fbbf","source":{"kind":"arxiv","id":"1903.00954","version":2},"attestation_state":"computed","paper":{"title":"Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","q-fin.CP","q-fin.ST"],"primary_cat":"stat.ML","authors_text":"Fabio Ferreira, Jonas Rothfuss, Maxim Ulrich, Simon Walther","submitted_at":"2019-03-03T18:15:20Z","abstract_excerpt":"Given a set of empirical observations, conditional density estimation aims to capture the statistical relationship between a conditional variable $\\mathbf{x}$ and a dependent variable $\\mathbf{y}$ by modeling their conditional probability $p(\\mathbf{y}|\\mathbf{x})$. The paper develops best practices for conditional density estimation for finance applications with neural networks, grounded on mathematical insights and empirical evaluations. In particular, we introduce a noise regularization and data normalization scheme, alleviating problems with over-fitting, initialization and hyper-parameter"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1903.00954","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2019-03-03T18:15:20Z","cross_cats_sorted":["cs.LG","q-fin.CP","q-fin.ST"],"title_canon_sha256":"43090dd522d9e4b92b6e914c0d62b4a63518a3bfa3f52038edfaad4e133ec737","abstract_canon_sha256":"a4ca6a4eae7925ce39468c97717d85b85ebc20254a3f7d7490a5fc32e87fccf7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:39.423527Z","signature_b64":"4xbAot2L1N54LJuK3V/0b5do9z8jmcExfnSNldmDvoqzpSOUI7qft6u1iks2SchE8yTKlTRQkRUkxI9h3XOwBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e24206b44b437780ab2f2edcacdfe27fb0ca71c408eecac7923447fd08c9fbbf","last_reissued_at":"2026-05-17T23:48:39.423048Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:39.423048Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","q-fin.CP","q-fin.ST"],"primary_cat":"stat.ML","authors_text":"Fabio Ferreira, Jonas Rothfuss, Maxim Ulrich, Simon Walther","submitted_at":"2019-03-03T18:15:20Z","abstract_excerpt":"Given a set of empirical observations, conditional density estimation aims to capture the statistical relationship between a conditional variable $\\mathbf{x}$ and a dependent variable $\\mathbf{y}$ by modeling their conditional probability $p(\\mathbf{y}|\\mathbf{x})$. The paper develops best practices for conditional density estimation for finance applications with neural networks, grounded on mathematical insights and empirical evaluations. In particular, we introduce a noise regularization and data normalization scheme, alleviating problems with over-fitting, initialization and hyper-parameter"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.00954","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1903.00954","created_at":"2026-05-17T23:48:39.423118+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.00954v2","created_at":"2026-05-17T23:48:39.423118+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.00954","created_at":"2026-05-17T23:48:39.423118+00:00"},{"alias_kind":"pith_short_12","alias_value":"4JBANNCLIN3Y","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"4JBANNCLIN3YBKZP","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"4JBANNCL","created_at":"2026-05-18T12:33:10.108867+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.09839","citing_title":"Free Energy Manifold: Score-Based Inference for Hybrid Bayesian Networks","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21647","citing_title":"Exploring climate change effects on concurrent floods and concurrent droughts via statistical deep learning","ref_index":73,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4JBANNCLIN3YBKZPF3OKZX7CP6","json":"https://pith.science/pith/4JBANNCLIN3YBKZPF3OKZX7CP6.json","graph_json":"https://pith.science/api/pith-number/4JBANNCLIN3YBKZPF3OKZX7CP6/graph.json","events_json":"https://pith.science/api/pith-number/4JBANNCLIN3YBKZPF3OKZX7CP6/events.json","paper":"https://pith.science/paper/4JBANNCL"},"agent_actions":{"view_html":"https://pith.science/pith/4JBANNCLIN3YBKZPF3OKZX7CP6","download_json":"https://pith.science/pith/4JBANNCLIN3YBKZPF3OKZX7CP6.json","view_paper":"https://pith.science/paper/4JBANNCL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.00954&json=true","fetch_graph":"https://pith.science/api/pith-number/4JBANNCLIN3YBKZPF3OKZX7CP6/graph.json","fetch_events":"https://pith.science/api/pith-number/4JBANNCLIN3YBKZPF3OKZX7CP6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4JBANNCLIN3YBKZPF3OKZX7CP6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4JBANNCLIN3YBKZPF3OKZX7CP6/action/storage_attestation","attest_author":"https://pith.science/pith/4JBANNCLIN3YBKZPF3OKZX7CP6/action/author_attestation","sign_citation":"https://pith.science/pith/4JBANNCLIN3YBKZPF3OKZX7CP6/action/citation_signature","submit_replication":"https://pith.science/pith/4JBANNCLIN3YBKZPF3OKZX7CP6/action/replication_record"}},"created_at":"2026-05-17T23:48:39.423118+00:00","updated_at":"2026-05-17T23:48:39.423118+00:00"}