{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:H4PQSPTI33XEWM5ATNHDO3C7SE","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":"7a6693765fde06f23c4951fdf82cbbcb6ab14425f0d8fd29562c17e5040f917e","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-08-08T15:01:55Z","title_canon_sha256":"d7f476221c384deccc5063084c08ac4ccd998d7ec5eeb36c940cdf0fb6e71051"},"schema_version":"1.0","source":{"id":"1708.02511","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.02511","created_at":"2026-07-05T03:24:25Z"},{"alias_kind":"arxiv_version","alias_value":"1708.02511v4","created_at":"2026-07-05T03:24:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.02511","created_at":"2026-07-05T03:24:25Z"},{"alias_kind":"pith_short_12","alias_value":"H4PQSPTI33XE","created_at":"2026-07-05T03:24:25Z"},{"alias_kind":"pith_short_16","alias_value":"H4PQSPTI33XEWM5A","created_at":"2026-07-05T03:24:25Z"},{"alias_kind":"pith_short_8","alias_value":"H4PQSPTI","created_at":"2026-07-05T03:24:25Z"}],"graph_snapshots":[{"event_id":"sha256:d76290c79a714f59931f5a7b57b6503228d5579365e84428f798a3d6df2872c1","target":"graph","created_at":"2026-07-05T03:24:25Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1708.02511/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Parametric adversarial divergences, which are a generalization of the losses used to train generative adversarial networks (GANs), have often been described as being approximations of their nonparametric counterparts, such as the Jensen-Shannon divergence, which can be derived under the so-called optimal discriminator assumption. In this position paper, we argue that despite being \"non-optimal\", parametric divergences have distinct properties from their nonparametric counterparts which can make them more suitable for learning high-dimensional distributions. A key property is that parametric di","authors_text":"Ahmed Touati, Gabriel Huang, Gauthier Gidel, Hugo Berard, Pascal Vincent, Simon Lacoste-Julien","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-08-08T15:01:55Z","title":"Parametric Adversarial Divergences are Good Losses for Generative Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.02511","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":"sha256:38f2884e157eb7985ae020191b3ff758838a51c244c46918e8e1323555951c33","target":"record","created_at":"2026-07-05T03:24:25Z","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":"7a6693765fde06f23c4951fdf82cbbcb6ab14425f0d8fd29562c17e5040f917e","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-08-08T15:01:55Z","title_canon_sha256":"d7f476221c384deccc5063084c08ac4ccd998d7ec5eeb36c940cdf0fb6e71051"},"schema_version":"1.0","source":{"id":"1708.02511","kind":"arxiv","version":4}},"canonical_sha256":"3f1f093e68deee4b33a09b4e376c5f9133186e9335c236fd3ba40e300a9de312","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3f1f093e68deee4b33a09b4e376c5f9133186e9335c236fd3ba40e300a9de312","first_computed_at":"2026-07-05T03:24:25.440294Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:24:25.440294Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ef6J3Mf0KF+ZzKkeko9ksMMr0RC95UhfGTATCjK6031eEK424ux3JIOfpv4Xwx/IswWvDmK4DWCWU5Fe4TflCg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:24:25.440761Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.02511","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:38f2884e157eb7985ae020191b3ff758838a51c244c46918e8e1323555951c33","sha256:d76290c79a714f59931f5a7b57b6503228d5579365e84428f798a3d6df2872c1"],"state_sha256":"6c0a441943fd0a6f5ab40a39254b6e60990110deb0134e0bc1b9096890b0538b"}