{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:UYCF7O5PNSGT7YFQZM6Z53YEXV","short_pith_number":"pith:UYCF7O5P","schema_version":"1.0","canonical_sha256":"a6045fbbaf6c8d3fe0b0cb3d9eef04bd59e694a6fe84a2610e0998dacc97a95f","source":{"kind":"arxiv","id":"1906.02691","version":3},"attestation_state":"computed","paper":{"title":"An Introduction to Variational Autoencoders","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Diederik P. Kingma, Max Welling","submitted_at":"2019-06-06T16:35:38Z","abstract_excerpt":"Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. In this work, we provide an introduction to variational autoencoders and some important extensions."},"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":"1906.02691","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-06T16:35:38Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5a92bc53e791e05aa177bcbc2864412913ce6261868a690bb6834a36b8753589","abstract_canon_sha256":"be2f32fff60c9074db8c6c0439968cea62733b470d8c7ca6e9874560470782c6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:25:27.061055Z","signature_b64":"POTXhEDFSUwfJq1INj7N09Jc+EM2BtaQJCkKIuOZgzkne2qJ6ZtKf+mymmFIsNbtxTGthMADUbrlTACk9MqdCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6045fbbaf6c8d3fe0b0cb3d9eef04bd59e694a6fe84a2610e0998dacc97a95f","last_reissued_at":"2026-07-05T00:25:27.060548Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:25:27.060548Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Introduction to Variational Autoencoders","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Diederik P. Kingma, Max Welling","submitted_at":"2019-06-06T16:35:38Z","abstract_excerpt":"Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. In this work, we provide an introduction to variational autoencoders and some important extensions."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.02691","kind":"arxiv","version":3},"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/1906.02691/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1906.02691","created_at":"2026-07-05T00:25:27.060611+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.02691v3","created_at":"2026-07-05T00:25:27.060611+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.02691","created_at":"2026-07-05T00:25:27.060611+00:00"},{"alias_kind":"pith_short_12","alias_value":"UYCF7O5PNSGT","created_at":"2026-07-05T00:25:27.060611+00:00"},{"alias_kind":"pith_short_16","alias_value":"UYCF7O5PNSGT7YFQ","created_at":"2026-07-05T00:25:27.060611+00:00"},{"alias_kind":"pith_short_8","alias_value":"UYCF7O5P","created_at":"2026-07-05T00:25:27.060611+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":12,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25389","citing_title":"Offline Multi-agent Continual Cooperation via Skill Partition and Reuse","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2605.27562","citing_title":"A Semi-Supervised Variational Autoencoder for Generating Neutron Star Equations of State","ref_index":52,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01145","citing_title":"When Independent Gaussian Models Break Down: Characterizing Regime-Dependent Modeling Failures in $\\phi^4$ Theory","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14839","citing_title":"GenAI for Energy-Efficient and Interference-Aware Compressed Sensing of GNSS Signals on a Google Edge TPU","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2511.12642","citing_title":"Auto-encoder model for faster generation of effective one-body gravitational waveform approximations","ref_index":71,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13399","citing_title":"The Diffusion Encoder","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2604.02438","citing_title":"Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01145","citing_title":"When Independent Gaussian Models Break Down: Characterizing Regime-Dependent Modeling Failures in $\\phi^4$ Theory","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2604.11827","citing_title":"Inverse Design of Inorganic Compounds with Generative AI","ref_index":75,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01862","citing_title":"QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL","ref_index":76,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07513","citing_title":"Tessellations of Semi-Discrete Flow Matching","ref_index":137,"is_internal_anchor":false},{"citing_arxiv_id":"2604.06348","citing_title":"Dartmouth Stellar Evolution Emulator (DSEE) 1: Generative Stellar Evolution Model Database","ref_index":60,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UYCF7O5PNSGT7YFQZM6Z53YEXV","json":"https://pith.science/pith/UYCF7O5PNSGT7YFQZM6Z53YEXV.json","graph_json":"https://pith.science/api/pith-number/UYCF7O5PNSGT7YFQZM6Z53YEXV/graph.json","events_json":"https://pith.science/api/pith-number/UYCF7O5PNSGT7YFQZM6Z53YEXV/events.json","paper":"https://pith.science/paper/UYCF7O5P"},"agent_actions":{"view_html":"https://pith.science/pith/UYCF7O5PNSGT7YFQZM6Z53YEXV","download_json":"https://pith.science/pith/UYCF7O5PNSGT7YFQZM6Z53YEXV.json","view_paper":"https://pith.science/paper/UYCF7O5P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.02691&json=true","fetch_graph":"https://pith.science/api/pith-number/UYCF7O5PNSGT7YFQZM6Z53YEXV/graph.json","fetch_events":"https://pith.science/api/pith-number/UYCF7O5PNSGT7YFQZM6Z53YEXV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UYCF7O5PNSGT7YFQZM6Z53YEXV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UYCF7O5PNSGT7YFQZM6Z53YEXV/action/storage_attestation","attest_author":"https://pith.science/pith/UYCF7O5PNSGT7YFQZM6Z53YEXV/action/author_attestation","sign_citation":"https://pith.science/pith/UYCF7O5PNSGT7YFQZM6Z53YEXV/action/citation_signature","submit_replication":"https://pith.science/pith/UYCF7O5PNSGT7YFQZM6Z53YEXV/action/replication_record"}},"created_at":"2026-07-05T00:25:27.060611+00:00","updated_at":"2026-07-05T00:25:27.060611+00:00"}