{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:WUD2PE3XQHXEIRYO3G2TIEZY5P","short_pith_number":"pith:WUD2PE3X","canonical_record":{"source":{"id":"1907.04809","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-07-10T16:08:32Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d13bdc52d2d9e17e269836b1db48e428f1e7285d597129fa43439ae54a86466f","abstract_canon_sha256":"61339638f7c66674ec846099aa64c98abeb5d88253bd8f85a762a547711e0270"},"schema_version":"1.0"},"canonical_sha256":"b507a7937781ee44470ed9b5341338ebc1167791e4c40dcfaf54f3cea8482574","source":{"kind":"arxiv","id":"1907.04809","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.04809","created_at":"2026-07-05T02:00:42Z"},{"alias_kind":"arxiv_version","alias_value":"1907.04809v4","created_at":"2026-07-05T02:00:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.04809","created_at":"2026-07-05T02:00:42Z"},{"alias_kind":"pith_short_12","alias_value":"WUD2PE3XQHXE","created_at":"2026-07-05T02:00:42Z"},{"alias_kind":"pith_short_16","alias_value":"WUD2PE3XQHXEIRYO","created_at":"2026-07-05T02:00:42Z"},{"alias_kind":"pith_short_8","alias_value":"WUD2PE3X","created_at":"2026-07-05T02:00:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:WUD2PE3XQHXEIRYO3G2TIEZY5P","target":"record","payload":{"canonical_record":{"source":{"id":"1907.04809","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-07-10T16:08:32Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d13bdc52d2d9e17e269836b1db48e428f1e7285d597129fa43439ae54a86466f","abstract_canon_sha256":"61339638f7c66674ec846099aa64c98abeb5d88253bd8f85a762a547711e0270"},"schema_version":"1.0"},"canonical_sha256":"b507a7937781ee44470ed9b5341338ebc1167791e4c40dcfaf54f3cea8482574","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:00:42.429489Z","signature_b64":"Szh8qKLVr96Mb6Fek6rpFHvbRN+ZYzqB/G5S//6lpRSGnM8hhxThdJrVc2iYrt5hsYzoaVWk1rGFa3aeDGjdCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b507a7937781ee44470ed9b5341338ebc1167791e4c40dcfaf54f3cea8482574","last_reissued_at":"2026-07-05T02:00:42.429104Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:00:42.429104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.04809","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":"2026-07-05T02:00:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C2AAWxFB1Ed3e9gJF5tClmf2P48Iy1L6gyeN3JTgjmHMzmSo6VrX4TBETGsh9jSqpIBozLwQWu2fiUZqiPb4Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T07:19:12.916020Z"},"content_sha256":"f2639c5f5e770b7ca078abb98ef8bbce93008ed736e7a39275e75960184d249e","schema_version":"1.0","event_id":"sha256:f2639c5f5e770b7ca078abb98ef8bbce93008ed736e7a39275e75960184d249e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:WUD2PE3XQHXEIRYO3G2TIEZY5P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Variational Autoencoders and Nonlinear ICA: A Unifying Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Aapo Hyv\\\"arinen, Diederik P. Kingma, Ilyes Khemakhem, Ricardo Pio Monti","submitted_at":"2019-07-10T16:08:32Z","abstract_excerpt":"The framework of variational autoencoders allows us to efficiently learn deep latent-variable models, such that the model's marginal distribution over observed variables fits the data. Often, we're interested in going a step further, and want to approximate the true joint distribution over observed and latent variables, including the true prior and posterior distributions over latent variables. This is known to be generally impossible due to unidentifiability of the model. We address this issue by showing that for a broad family of deep latent-variable models, identification of the true joint "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.04809","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1907.04809/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T02:00:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PgOD3uViYPjkmb5hslfC6XWGcos0ghLIuMTQm+TLXHPi5Au59R3Bn6/PrhV4SZ66RnEid0Akr2zGwQ2yCYJXCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T07:19:12.916536Z"},"content_sha256":"4807502696cb28d80ea657f758f2f0b079760dfbab1dc1c815525766b92c95f2","schema_version":"1.0","event_id":"sha256:4807502696cb28d80ea657f758f2f0b079760dfbab1dc1c815525766b92c95f2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WUD2PE3XQHXEIRYO3G2TIEZY5P/bundle.json","state_url":"https://pith.science/pith/WUD2PE3XQHXEIRYO3G2TIEZY5P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WUD2PE3XQHXEIRYO3G2TIEZY5P/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":"2026-08-20T07:19:12Z","links":{"resolver":"https://pith.science/pith/WUD2PE3XQHXEIRYO3G2TIEZY5P","bundle":"https://pith.science/pith/WUD2PE3XQHXEIRYO3G2TIEZY5P/bundle.json","state":"https://pith.science/pith/WUD2PE3XQHXEIRYO3G2TIEZY5P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WUD2PE3XQHXEIRYO3G2TIEZY5P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:WUD2PE3XQHXEIRYO3G2TIEZY5P","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":"61339638f7c66674ec846099aa64c98abeb5d88253bd8f85a762a547711e0270","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-07-10T16:08:32Z","title_canon_sha256":"d13bdc52d2d9e17e269836b1db48e428f1e7285d597129fa43439ae54a86466f"},"schema_version":"1.0","source":{"id":"1907.04809","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.04809","created_at":"2026-07-05T02:00:42Z"},{"alias_kind":"arxiv_version","alias_value":"1907.04809v4","created_at":"2026-07-05T02:00:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.04809","created_at":"2026-07-05T02:00:42Z"},{"alias_kind":"pith_short_12","alias_value":"WUD2PE3XQHXE","created_at":"2026-07-05T02:00:42Z"},{"alias_kind":"pith_short_16","alias_value":"WUD2PE3XQHXEIRYO","created_at":"2026-07-05T02:00:42Z"},{"alias_kind":"pith_short_8","alias_value":"WUD2PE3X","created_at":"2026-07-05T02:00:42Z"}],"graph_snapshots":[{"event_id":"sha256:4807502696cb28d80ea657f758f2f0b079760dfbab1dc1c815525766b92c95f2","target":"graph","created_at":"2026-07-05T02:00:42Z","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/1907.04809/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The framework of variational autoencoders allows us to efficiently learn deep latent-variable models, such that the model's marginal distribution over observed variables fits the data. Often, we're interested in going a step further, and want to approximate the true joint distribution over observed and latent variables, including the true prior and posterior distributions over latent variables. This is known to be generally impossible due to unidentifiability of the model. We address this issue by showing that for a broad family of deep latent-variable models, identification of the true joint ","authors_text":"Aapo Hyv\\\"arinen, Diederik P. Kingma, Ilyes Khemakhem, Ricardo Pio Monti","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-07-10T16:08:32Z","title":"Variational Autoencoders and Nonlinear ICA: A Unifying Framework"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.04809","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:f2639c5f5e770b7ca078abb98ef8bbce93008ed736e7a39275e75960184d249e","target":"record","created_at":"2026-07-05T02:00:42Z","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":"61339638f7c66674ec846099aa64c98abeb5d88253bd8f85a762a547711e0270","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-07-10T16:08:32Z","title_canon_sha256":"d13bdc52d2d9e17e269836b1db48e428f1e7285d597129fa43439ae54a86466f"},"schema_version":"1.0","source":{"id":"1907.04809","kind":"arxiv","version":4}},"canonical_sha256":"b507a7937781ee44470ed9b5341338ebc1167791e4c40dcfaf54f3cea8482574","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b507a7937781ee44470ed9b5341338ebc1167791e4c40dcfaf54f3cea8482574","first_computed_at":"2026-07-05T02:00:42.429104Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:00:42.429104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Szh8qKLVr96Mb6Fek6rpFHvbRN+ZYzqB/G5S//6lpRSGnM8hhxThdJrVc2iYrt5hsYzoaVWk1rGFa3aeDGjdCg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:00:42.429489Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.04809","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f2639c5f5e770b7ca078abb98ef8bbce93008ed736e7a39275e75960184d249e","sha256:4807502696cb28d80ea657f758f2f0b079760dfbab1dc1c815525766b92c95f2"],"state_sha256":"8384b34f4079ba13016e501c37d4d2884e78a86b9055ac361483dfd4362e3deb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1/UoHl9nGzd4oP1OAxqOhKr5qvdWHT2QAlggYueArgqUtGBQCJ9eTMDs+tPSM62S0uM3jUDyQNyKAdc15XkJBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T07:19:12.922055Z","bundle_sha256":"b00d5e89296422f97dc05421b0288daaaf0a998e1906b4a4754c4dbf032afe38"}}