{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:BWCKIVZ5BQJZFDREVKQJYEMIM5","short_pith_number":"pith:BWCKIVZ5","schema_version":"1.0","canonical_sha256":"0d84a4573d0c13928e24aaa09c11886748b6274835f4523c72677096a55d9a51","source":{"kind":"arxiv","id":"1907.03361","version":1},"attestation_state":"computed","paper":{"title":"Copula & Marginal Flows: Disentangling the Marginal from its Joint","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Magnus Wiese, Ralf Korn, Robert Knobloch","submitted_at":"2019-07-07T22:45:26Z","abstract_excerpt":"Deep generative networks such as GANs and normalizing flows flourish in the context of high-dimensional tasks such as image generation. However, so far exact modeling or extrapolation of distributional properties such as the tail asymptotics generated by a generative network is not available. In this paper, we address this issue for the first time in the deep learning literature by making two novel contributions. First, we derive upper bounds for the tails that can be expressed by a generative network and demonstrate Lp-space related properties. There we show specifically that in various situa"},"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":"1907.03361","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-07T22:45:26Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"791e24abcd9609f6c15454eddc209109a4b5ecd97e3f355377279f87a5b9b0a8","abstract_canon_sha256":"5e89b5af8b8ef63d83b0396ac0131d0fa20cde72e396df7c47f871b7eecf77b5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:16.444742Z","signature_b64":"p7a2DzOAB95o8cuUkzcWJPoGOiAzfTQiq5nrFON1oZhK/2cq+o3lDT+8y1QyQjMCA3akGJH2jizddV3SdYJbBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0d84a4573d0c13928e24aaa09c11886748b6274835f4523c72677096a55d9a51","last_reissued_at":"2026-05-17T23:41:16.444160Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:16.444160Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Copula & Marginal Flows: Disentangling the Marginal from its Joint","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Magnus Wiese, Ralf Korn, Robert Knobloch","submitted_at":"2019-07-07T22:45:26Z","abstract_excerpt":"Deep generative networks such as GANs and normalizing flows flourish in the context of high-dimensional tasks such as image generation. However, so far exact modeling or extrapolation of distributional properties such as the tail asymptotics generated by a generative network is not available. In this paper, we address this issue for the first time in the deep learning literature by making two novel contributions. First, we derive upper bounds for the tails that can be expressed by a generative network and demonstrate Lp-space related properties. There we show specifically that in various situa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.03361","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1907.03361","created_at":"2026-05-17T23:41:16.444247+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.03361v1","created_at":"2026-05-17T23:41:16.444247+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.03361","created_at":"2026-05-17T23:41:16.444247+00:00"},{"alias_kind":"pith_short_12","alias_value":"BWCKIVZ5BQJZ","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"BWCKIVZ5BQJZFDRE","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"BWCKIVZ5","created_at":"2026-05-18T12:33:12.712433+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.01909","citing_title":"Extrapolation in Statistical Learning with Extreme Value Theory","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BWCKIVZ5BQJZFDREVKQJYEMIM5","json":"https://pith.science/pith/BWCKIVZ5BQJZFDREVKQJYEMIM5.json","graph_json":"https://pith.science/api/pith-number/BWCKIVZ5BQJZFDREVKQJYEMIM5/graph.json","events_json":"https://pith.science/api/pith-number/BWCKIVZ5BQJZFDREVKQJYEMIM5/events.json","paper":"https://pith.science/paper/BWCKIVZ5"},"agent_actions":{"view_html":"https://pith.science/pith/BWCKIVZ5BQJZFDREVKQJYEMIM5","download_json":"https://pith.science/pith/BWCKIVZ5BQJZFDREVKQJYEMIM5.json","view_paper":"https://pith.science/paper/BWCKIVZ5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.03361&json=true","fetch_graph":"https://pith.science/api/pith-number/BWCKIVZ5BQJZFDREVKQJYEMIM5/graph.json","fetch_events":"https://pith.science/api/pith-number/BWCKIVZ5BQJZFDREVKQJYEMIM5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BWCKIVZ5BQJZFDREVKQJYEMIM5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BWCKIVZ5BQJZFDREVKQJYEMIM5/action/storage_attestation","attest_author":"https://pith.science/pith/BWCKIVZ5BQJZFDREVKQJYEMIM5/action/author_attestation","sign_citation":"https://pith.science/pith/BWCKIVZ5BQJZFDREVKQJYEMIM5/action/citation_signature","submit_replication":"https://pith.science/pith/BWCKIVZ5BQJZFDREVKQJYEMIM5/action/replication_record"}},"created_at":"2026-05-17T23:41:16.444247+00:00","updated_at":"2026-05-17T23:41:16.444247+00:00"}