{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:WGIPOSEADJGMLGBQVGBWFVZOA2","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":"b75bc46ff332270dfa1cbca848011fc3b5206b5bdc3bcea87c666215e651d17a","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-08-16T16:07:05Z","title_canon_sha256":"255aeaeaf7cc55caf21b547e67a83c9cb9341cc189930542c844c9ce339b19bb"},"schema_version":"1.0","source":{"id":"2208.07818","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.07818","created_at":"2026-07-05T04:49:09Z"},{"alias_kind":"arxiv_version","alias_value":"2208.07818v1","created_at":"2026-07-05T04:49:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.07818","created_at":"2026-07-05T04:49:09Z"},{"alias_kind":"pith_short_12","alias_value":"WGIPOSEADJGM","created_at":"2026-07-05T04:49:09Z"},{"alias_kind":"pith_short_16","alias_value":"WGIPOSEADJGMLGBQ","created_at":"2026-07-05T04:49:09Z"},{"alias_kind":"pith_short_8","alias_value":"WGIPOSEA","created_at":"2026-07-05T04:49:09Z"}],"graph_snapshots":[{"event_id":"sha256:339f8fcb34194c4772b8da0e3ff27554bf318553a69851ee0872c8fc364b4737","target":"graph","created_at":"2026-07-05T04:49:09Z","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/2208.07818/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Auto-encoding Variational Bayes (AEVB) is a powerful and general algorithm for fitting latent variable models (a promising direction for unsupervised learning), and is well-known for training the Variational Auto-Encoder (VAE). In this tutorial, we focus on motivating AEVB from the classic Expectation Maximization (EM) algorithm, as opposed to from deterministic auto-encoders. Though natural and somewhat self-evident, the connection between EM and AEVB is not emphasized in the recent deep learning literature, and we believe that emphasizing this connection can improve the community's understan","authors_text":"Yang Zhi-Han","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-08-16T16:07:05Z","title":"Training Latent Variable Models with Auto-encoding Variational Bayes: A Tutorial"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.07818","kind":"arxiv","version":1},"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":"sha256:34f781b932a83092409eee8df8a8eb0c0200903e8600679fe746ce51866caf14","target":"record","created_at":"2026-07-05T04:49:09Z","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":"b75bc46ff332270dfa1cbca848011fc3b5206b5bdc3bcea87c666215e651d17a","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-08-16T16:07:05Z","title_canon_sha256":"255aeaeaf7cc55caf21b547e67a83c9cb9341cc189930542c844c9ce339b19bb"},"schema_version":"1.0","source":{"id":"2208.07818","kind":"arxiv","version":1}},"canonical_sha256":"b190f748801a4cc59830a98362d72e06add83efeae30eb2a89ae42f24289e1f9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b190f748801a4cc59830a98362d72e06add83efeae30eb2a89ae42f24289e1f9","first_computed_at":"2026-07-05T04:49:09.353610Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:49:09.353610Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fE6+HErq0l9gM7RTZ31rM92GAzNVT+2FMWbw6dx4sJzaqljl9ILOpOaZadGHxcg9eR4AYGDj3lOGZ83BNxz1CQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:49:09.354075Z","signed_message":"canonical_sha256_bytes"},"source_id":"2208.07818","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:34f781b932a83092409eee8df8a8eb0c0200903e8600679fe746ce51866caf14","sha256:339f8fcb34194c4772b8da0e3ff27554bf318553a69851ee0872c8fc364b4737"],"state_sha256":"19b40df60bd324a40db92b25293a47668fc790b3f882475eae220f7719c29853"}