{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:7QKDCMJFPQBEUTUXZ46JZQGWUU","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":"10deb4886eca5368d806e858fcabdb543eb7e464d273ed3150c675bdde63e5f5","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-09T17:27:20Z","title_canon_sha256":"98ac2b350f7e2a7e9bc566173fee572891ac10f0b47dd37323b08941c920afde"},"schema_version":"1.0","source":{"id":"1810.04147","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.04147","created_at":"2026-05-17T23:44:07Z"},{"alias_kind":"arxiv_version","alias_value":"1810.04147v2","created_at":"2026-05-17T23:44:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.04147","created_at":"2026-05-17T23:44:07Z"},{"alias_kind":"pith_short_12","alias_value":"7QKDCMJFPQBE","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"7QKDCMJFPQBEUTUX","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"7QKDCMJF","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:b8585e2887a3d4d834c2c693969736c422a6bfc3e8aa80b91bd5b67de2e2c46b","target":"graph","created_at":"2026-05-17T23:44:07Z","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":"Building on the success of deep learning, two modern approaches to learn a probability model from the data are Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs). VAEs consider an explicit probability model for the data and compute a generative distribution by maximizing a variational lower-bound on the log-likelihood function. GANs, however, compute a generative model by minimizing a distance between observed and generated probability distributions without considering an explicit model for the observed data. The lack of having explicit probability models in GANs prohib","authors_text":"Hamed Hassani, Rama Chellappa, Soheil Feizi, Yogesh Balaji","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-09T17:27:20Z","title":"Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.04147","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":"sha256:35feff1740b0eb101ca688096cf700fefa08e16a5b8119d53ee68f0aff619312","target":"record","created_at":"2026-05-17T23:44:07Z","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":"10deb4886eca5368d806e858fcabdb543eb7e464d273ed3150c675bdde63e5f5","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-09T17:27:20Z","title_canon_sha256":"98ac2b350f7e2a7e9bc566173fee572891ac10f0b47dd37323b08941c920afde"},"schema_version":"1.0","source":{"id":"1810.04147","kind":"arxiv","version":2}},"canonical_sha256":"fc143131257c024a4e97cf3c9cc0d6a50c6c9046b3703098fa045a7a8746e39e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fc143131257c024a4e97cf3c9cc0d6a50c6c9046b3703098fa045a7a8746e39e","first_computed_at":"2026-05-17T23:44:07.537513Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:07.537513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LPy7u2aiPjTbK4VJKqtVyrWTmk5Oiav6VSCsYREq5WSm0stLkxAoXPUiDzc+jCGlBhvWIFvV5FoUzBBMFk3zAw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:07.538060Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.04147","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:35feff1740b0eb101ca688096cf700fefa08e16a5b8119d53ee68f0aff619312","sha256:b8585e2887a3d4d834c2c693969736c422a6bfc3e8aa80b91bd5b67de2e2c46b"],"state_sha256":"490d6826683d020795cf10743d4fd1dd42a23dc9e100608d0c3430b356c62844"}