{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:S7PUDP64ZDYPSKMXU6THRZFMLM","short_pith_number":"pith:S7PUDP64","canonical_record":{"source":{"id":"1701.04722","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-17T15:18:31Z","cross_cats_sorted":[],"title_canon_sha256":"6a9603b9d6172c67466289976fb4ca83eca047f9e91fedf7c06fa76fa9c15757","abstract_canon_sha256":"674adb3962c940c52f026a8a2f3e80e6e395bb7f9d53fc7448ba143555ca7c07"},"schema_version":"1.0"},"canonical_sha256":"97df41bfdcc8f0f92997a7a678e4ac5b31dd0335ec881556afe1940cfeab688e","source":{"kind":"arxiv","id":"1701.04722","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.04722","created_at":"2026-05-18T00:13:48Z"},{"alias_kind":"arxiv_version","alias_value":"1701.04722v4","created_at":"2026-05-18T00:13:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.04722","created_at":"2026-05-18T00:13:48Z"},{"alias_kind":"pith_short_12","alias_value":"S7PUDP64ZDYP","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"S7PUDP64ZDYPSKMX","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"S7PUDP64","created_at":"2026-05-18T12:31:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:S7PUDP64ZDYPSKMXU6THRZFMLM","target":"record","payload":{"canonical_record":{"source":{"id":"1701.04722","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-17T15:18:31Z","cross_cats_sorted":[],"title_canon_sha256":"6a9603b9d6172c67466289976fb4ca83eca047f9e91fedf7c06fa76fa9c15757","abstract_canon_sha256":"674adb3962c940c52f026a8a2f3e80e6e395bb7f9d53fc7448ba143555ca7c07"},"schema_version":"1.0"},"canonical_sha256":"97df41bfdcc8f0f92997a7a678e4ac5b31dd0335ec881556afe1940cfeab688e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:48.229828Z","signature_b64":"FR08CDBA6pwL0YBNLjimOZK+eZx30rQDUYwUatu8PaOcqqVoTrvE+pll2OItdpzmXrIJUnzLX3o7NS2OV7rGCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"97df41bfdcc8f0f92997a7a678e4ac5b31dd0335ec881556afe1940cfeab688e","last_reissued_at":"2026-05-18T00:13:48.229274Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:48.229274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1701.04722","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:13:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qZUJwtlQj5I78BFphLQwm1aFJlc9HD9j7XmSbgh2swFL+T9xwFTB3xIQ9toYoJfcdJs8gEF1IvdLpFmaSDzQCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T10:20:49.470055Z"},"content_sha256":"a15dc00cdfbe7fb9cbfa719f572a61edc3a478fc16b90b149bcac3ecc896856a","schema_version":"1.0","event_id":"sha256:a15dc00cdfbe7fb9cbfa719f572a61edc3a478fc16b90b149bcac3ecc896856a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:S7PUDP64ZDYPSKMXU6THRZFMLM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Andreas Geiger, Lars Mescheder, Sebastian Nowozin","submitted_at":"2017-01-17T15:18:31Z","abstract_excerpt":"Variational Autoencoders (VAEs) are expressive latent variable models that can be used to learn complex probability distributions from training data. However, the quality of the resulting model crucially relies on the expressiveness of the inference model. We introduce Adversarial Variational Bayes (AVB), a technique for training Variational Autoencoders with arbitrarily expressive inference models. We achieve this by introducing an auxiliary discriminative network that allows to rephrase the maximum-likelihood-problem as a two-player game, hence establishing a principled connection between VA"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.04722","kind":"arxiv","version":4},"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":"2026-05-18T00:13:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oXQd/uQH0GZtJbBRiUdMc1ufgF2Pax4MMIZytfFenhZxzghhDIJxf5H6wtdj5OI6v9xWo/zhWvQRPzrdHREDCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T10:20:49.470411Z"},"content_sha256":"4320ebf813f55b17d0f34e744bfb31ef7d2bb4a2c13df4782edc16bbcee57128","schema_version":"1.0","event_id":"sha256:4320ebf813f55b17d0f34e744bfb31ef7d2bb4a2c13df4782edc16bbcee57128"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/S7PUDP64ZDYPSKMXU6THRZFMLM/bundle.json","state_url":"https://pith.science/pith/S7PUDP64ZDYPSKMXU6THRZFMLM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/S7PUDP64ZDYPSKMXU6THRZFMLM/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":"2026-06-03T10:20:49Z","links":{"resolver":"https://pith.science/pith/S7PUDP64ZDYPSKMXU6THRZFMLM","bundle":"https://pith.science/pith/S7PUDP64ZDYPSKMXU6THRZFMLM/bundle.json","state":"https://pith.science/pith/S7PUDP64ZDYPSKMXU6THRZFMLM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/S7PUDP64ZDYPSKMXU6THRZFMLM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:S7PUDP64ZDYPSKMXU6THRZFMLM","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":"674adb3962c940c52f026a8a2f3e80e6e395bb7f9d53fc7448ba143555ca7c07","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-17T15:18:31Z","title_canon_sha256":"6a9603b9d6172c67466289976fb4ca83eca047f9e91fedf7c06fa76fa9c15757"},"schema_version":"1.0","source":{"id":"1701.04722","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.04722","created_at":"2026-05-18T00:13:48Z"},{"alias_kind":"arxiv_version","alias_value":"1701.04722v4","created_at":"2026-05-18T00:13:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.04722","created_at":"2026-05-18T00:13:48Z"},{"alias_kind":"pith_short_12","alias_value":"S7PUDP64ZDYP","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"S7PUDP64ZDYPSKMX","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"S7PUDP64","created_at":"2026-05-18T12:31:43Z"}],"graph_snapshots":[{"event_id":"sha256:4320ebf813f55b17d0f34e744bfb31ef7d2bb4a2c13df4782edc16bbcee57128","target":"graph","created_at":"2026-05-18T00:13:48Z","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":"Variational Autoencoders (VAEs) are expressive latent variable models that can be used to learn complex probability distributions from training data. However, the quality of the resulting model crucially relies on the expressiveness of the inference model. We introduce Adversarial Variational Bayes (AVB), a technique for training Variational Autoencoders with arbitrarily expressive inference models. We achieve this by introducing an auxiliary discriminative network that allows to rephrase the maximum-likelihood-problem as a two-player game, hence establishing a principled connection between VA","authors_text":"Andreas Geiger, Lars Mescheder, Sebastian Nowozin","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-17T15:18:31Z","title":"Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.04722","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:a15dc00cdfbe7fb9cbfa719f572a61edc3a478fc16b90b149bcac3ecc896856a","target":"record","created_at":"2026-05-18T00:13:48Z","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":"674adb3962c940c52f026a8a2f3e80e6e395bb7f9d53fc7448ba143555ca7c07","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-17T15:18:31Z","title_canon_sha256":"6a9603b9d6172c67466289976fb4ca83eca047f9e91fedf7c06fa76fa9c15757"},"schema_version":"1.0","source":{"id":"1701.04722","kind":"arxiv","version":4}},"canonical_sha256":"97df41bfdcc8f0f92997a7a678e4ac5b31dd0335ec881556afe1940cfeab688e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"97df41bfdcc8f0f92997a7a678e4ac5b31dd0335ec881556afe1940cfeab688e","first_computed_at":"2026-05-18T00:13:48.229274Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:13:48.229274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FR08CDBA6pwL0YBNLjimOZK+eZx30rQDUYwUatu8PaOcqqVoTrvE+pll2OItdpzmXrIJUnzLX3o7NS2OV7rGCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:13:48.229828Z","signed_message":"canonical_sha256_bytes"},"source_id":"1701.04722","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a15dc00cdfbe7fb9cbfa719f572a61edc3a478fc16b90b149bcac3ecc896856a","sha256:4320ebf813f55b17d0f34e744bfb31ef7d2bb4a2c13df4782edc16bbcee57128"],"state_sha256":"0dd1701755c652bb098124830cfba2a76f4426996fc70eb19b994c405b357515"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eqDgBMvupA81CeRMEoHcFnc3xKG3quG8yb+jhoz7aXk6TgYrvwG8yRGHO0PRk7iM2IJ+rXNKNTrEfJOsLwFzBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T10:20:49.472361Z","bundle_sha256":"d03212f53ede02c3a29945658240d51a8ce777b76bc567918f0fae84464fa816"}}