{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:BNTH6YSVCLVPTSVTQPM77K6WII","short_pith_number":"pith:BNTH6YSV","canonical_record":{"source":{"id":"1706.00550","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-02T04:15:44Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"24fb07786ee28a2f6ee06619275807288be6a2f846652970564613bd804010b3","abstract_canon_sha256":"c7f0dc0ddb29b07917b27a9ce9f76f9969552c4bc417f81f03ad6925cf831569"},"schema_version":"1.0"},"canonical_sha256":"0b667f625512eaf9cab383d9ffabd6421ded69ba99766e67293a26645636bb05","source":{"kind":"arxiv","id":"1706.00550","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.00550","created_at":"2026-05-18T00:11:00Z"},{"alias_kind":"arxiv_version","alias_value":"1706.00550v5","created_at":"2026-05-18T00:11:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.00550","created_at":"2026-05-18T00:11:00Z"},{"alias_kind":"pith_short_12","alias_value":"BNTH6YSVCLVP","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"BNTH6YSVCLVPTSVT","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"BNTH6YSV","created_at":"2026-05-18T12:31:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:BNTH6YSVCLVPTSVTQPM77K6WII","target":"record","payload":{"canonical_record":{"source":{"id":"1706.00550","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-02T04:15:44Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"24fb07786ee28a2f6ee06619275807288be6a2f846652970564613bd804010b3","abstract_canon_sha256":"c7f0dc0ddb29b07917b27a9ce9f76f9969552c4bc417f81f03ad6925cf831569"},"schema_version":"1.0"},"canonical_sha256":"0b667f625512eaf9cab383d9ffabd6421ded69ba99766e67293a26645636bb05","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:00.546022Z","signature_b64":"a42jo+xWQxwTtpowE81U8tfRKXdWIaZ5dBwdn4OzNLl18Agi2KmuwKxJMHtHUEGIUeQkObOT2B4zp0yYblkaBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b667f625512eaf9cab383d9ffabd6421ded69ba99766e67293a26645636bb05","last_reissued_at":"2026-05-18T00:11:00.545257Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:00.545257Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.00550","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:11:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qJQpkmh/6dUD4hMXE/pzhfJkLK+drxLWfv4fm6Vamu4hU5Kl5/t5ZUImZLWwmqRm4dXXj9e/vXejRhVUTgWSAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T18:14:24.732271Z"},"content_sha256":"3ee1e970310231663b0c811cbc4b6d8924dc93494e527701cfb5953e57288757","schema_version":"1.0","event_id":"sha256:3ee1e970310231663b0c811cbc4b6d8924dc93494e527701cfb5953e57288757"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:BNTH6YSVCLVPTSVTQPM77K6WII","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On Unifying Deep Generative Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Eric P. Xing, Ruslan Salakhutdinov, Zhiting Hu, Zichao Yang","submitted_at":"2017-06-02T04:15:44Z","abstract_excerpt":"Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as emerging families for generative model learning, have largely been considered as two distinct paradigms and received extensive independent studies respectively. This paper aims to establish formal connections between GANs and VAEs through a new formulation of them. We interpret sample generation in GANs as performing posterior inference, and show that GANs and VAEs involve minimizing KL divergences of respective posterior and inference distribu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.00550","kind":"arxiv","version":5},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:11:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sGZ0RCGylmEvnmHP/vXHnItwRDAMxdguWDvQSI4Danc+mT0E6H/t06reONOotudTVMj6xY3f7A4y8L2RekKBBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T18:14:24.732974Z"},"content_sha256":"0198ae8e045eb06ec229f5de70706c984627180bc0a108e607351ed4a82116d3","schema_version":"1.0","event_id":"sha256:0198ae8e045eb06ec229f5de70706c984627180bc0a108e607351ed4a82116d3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BNTH6YSVCLVPTSVTQPM77K6WII/bundle.json","state_url":"https://pith.science/pith/BNTH6YSVCLVPTSVTQPM77K6WII/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BNTH6YSVCLVPTSVTQPM77K6WII/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-15T18:14:24Z","links":{"resolver":"https://pith.science/pith/BNTH6YSVCLVPTSVTQPM77K6WII","bundle":"https://pith.science/pith/BNTH6YSVCLVPTSVTQPM77K6WII/bundle.json","state":"https://pith.science/pith/BNTH6YSVCLVPTSVTQPM77K6WII/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BNTH6YSVCLVPTSVTQPM77K6WII/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:BNTH6YSVCLVPTSVTQPM77K6WII","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":"c7f0dc0ddb29b07917b27a9ce9f76f9969552c4bc417f81f03ad6925cf831569","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-02T04:15:44Z","title_canon_sha256":"24fb07786ee28a2f6ee06619275807288be6a2f846652970564613bd804010b3"},"schema_version":"1.0","source":{"id":"1706.00550","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.00550","created_at":"2026-05-18T00:11:00Z"},{"alias_kind":"arxiv_version","alias_value":"1706.00550v5","created_at":"2026-05-18T00:11:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.00550","created_at":"2026-05-18T00:11:00Z"},{"alias_kind":"pith_short_12","alias_value":"BNTH6YSVCLVP","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"BNTH6YSVCLVPTSVT","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"BNTH6YSV","created_at":"2026-05-18T12:31:08Z"}],"graph_snapshots":[{"event_id":"sha256:0198ae8e045eb06ec229f5de70706c984627180bc0a108e607351ed4a82116d3","target":"graph","created_at":"2026-05-18T00:11:00Z","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":"Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as emerging families for generative model learning, have largely been considered as two distinct paradigms and received extensive independent studies respectively. This paper aims to establish formal connections between GANs and VAEs through a new formulation of them. We interpret sample generation in GANs as performing posterior inference, and show that GANs and VAEs involve minimizing KL divergences of respective posterior and inference distribu","authors_text":"Eric P. Xing, Ruslan Salakhutdinov, Zhiting Hu, Zichao Yang","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-02T04:15:44Z","title":"On Unifying Deep Generative Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.00550","kind":"arxiv","version":5},"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":"sha256:3ee1e970310231663b0c811cbc4b6d8924dc93494e527701cfb5953e57288757","target":"record","created_at":"2026-05-18T00:11:00Z","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":"c7f0dc0ddb29b07917b27a9ce9f76f9969552c4bc417f81f03ad6925cf831569","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-02T04:15:44Z","title_canon_sha256":"24fb07786ee28a2f6ee06619275807288be6a2f846652970564613bd804010b3"},"schema_version":"1.0","source":{"id":"1706.00550","kind":"arxiv","version":5}},"canonical_sha256":"0b667f625512eaf9cab383d9ffabd6421ded69ba99766e67293a26645636bb05","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0b667f625512eaf9cab383d9ffabd6421ded69ba99766e67293a26645636bb05","first_computed_at":"2026-05-18T00:11:00.545257Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:11:00.545257Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"a42jo+xWQxwTtpowE81U8tfRKXdWIaZ5dBwdn4OzNLl18Agi2KmuwKxJMHtHUEGIUeQkObOT2B4zp0yYblkaBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:11:00.546022Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.00550","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3ee1e970310231663b0c811cbc4b6d8924dc93494e527701cfb5953e57288757","sha256:0198ae8e045eb06ec229f5de70706c984627180bc0a108e607351ed4a82116d3"],"state_sha256":"fabfa7d120b5321ee3a5aa41d7343777575600afae7fd8de290f88579691e09b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PTaCvsjRViaS/cj8iVQRKH/zeHR7/9xsdhBjRQDqWZjnnKs0PDVNAK0KMefFETOAZ6bSx6eG68PJQQ0WExy4AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T18:14:24.737906Z","bundle_sha256":"eeee004bab585e1c2292d443acf3f6bae9a439b20ebf3c1d6d7ee636e486f5bc"}}