{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:7QKDCMJFPQBEUTUXZ46JZQGWUU","short_pith_number":"pith:7QKDCMJF","schema_version":"1.0","canonical_sha256":"fc143131257c024a4e97cf3c9cc0d6a50c6c9046b3703098fa045a7a8746e39e","source":{"kind":"arxiv","id":"1810.04147","version":2},"attestation_state":"computed","paper":{"title":"Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Hamed Hassani, Rama Chellappa, Soheil Feizi, Yogesh Balaji","submitted_at":"2018-10-09T17:27:20Z","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"},"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":"1810.04147","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-09T17:27:20Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"98ac2b350f7e2a7e9bc566173fee572891ac10f0b47dd37323b08941c920afde","abstract_canon_sha256":"10deb4886eca5368d806e858fcabdb543eb7e464d273ed3150c675bdde63e5f5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:07.538060Z","signature_b64":"LPy7u2aiPjTbK4VJKqtVyrWTmk5Oiav6VSCsYREq5WSm0stLkxAoXPUiDzc+jCGlBhvWIFvV5FoUzBBMFk3zAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc143131257c024a4e97cf3c9cc0d6a50c6c9046b3703098fa045a7a8746e39e","last_reissued_at":"2026-05-17T23:44:07.537513Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:07.537513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Hamed Hassani, Rama Chellappa, Soheil Feizi, Yogesh Balaji","submitted_at":"2018-10-09T17:27:20Z","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"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.04147","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":"1810.04147","created_at":"2026-05-17T23:44:07.537611+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.04147v2","created_at":"2026-05-17T23:44:07.537611+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.04147","created_at":"2026-05-17T23:44:07.537611+00:00"},{"alias_kind":"pith_short_12","alias_value":"7QKDCMJFPQBE","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_16","alias_value":"7QKDCMJFPQBEUTUX","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_8","alias_value":"7QKDCMJF","created_at":"2026-05-18T12:32:11.075285+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7QKDCMJFPQBEUTUXZ46JZQGWUU","json":"https://pith.science/pith/7QKDCMJFPQBEUTUXZ46JZQGWUU.json","graph_json":"https://pith.science/api/pith-number/7QKDCMJFPQBEUTUXZ46JZQGWUU/graph.json","events_json":"https://pith.science/api/pith-number/7QKDCMJFPQBEUTUXZ46JZQGWUU/events.json","paper":"https://pith.science/paper/7QKDCMJF"},"agent_actions":{"view_html":"https://pith.science/pith/7QKDCMJFPQBEUTUXZ46JZQGWUU","download_json":"https://pith.science/pith/7QKDCMJFPQBEUTUXZ46JZQGWUU.json","view_paper":"https://pith.science/paper/7QKDCMJF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.04147&json=true","fetch_graph":"https://pith.science/api/pith-number/7QKDCMJFPQBEUTUXZ46JZQGWUU/graph.json","fetch_events":"https://pith.science/api/pith-number/7QKDCMJFPQBEUTUXZ46JZQGWUU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7QKDCMJFPQBEUTUXZ46JZQGWUU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7QKDCMJFPQBEUTUXZ46JZQGWUU/action/storage_attestation","attest_author":"https://pith.science/pith/7QKDCMJFPQBEUTUXZ46JZQGWUU/action/author_attestation","sign_citation":"https://pith.science/pith/7QKDCMJFPQBEUTUXZ46JZQGWUU/action/citation_signature","submit_replication":"https://pith.science/pith/7QKDCMJFPQBEUTUXZ46JZQGWUU/action/replication_record"}},"created_at":"2026-05-17T23:44:07.537611+00:00","updated_at":"2026-05-17T23:44:07.537611+00:00"}