{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:JK7FZFL45GIR6YFVSWC7SWER6D","short_pith_number":"pith:JK7FZFL4","schema_version":"1.0","canonical_sha256":"4abe5c957ce9911f60b59585f95891f0ceeb1d9c5c860f970a9344dfc767e86e","source":{"kind":"arxiv","id":"1903.07303","version":1},"attestation_state":"computed","paper":{"title":"M$^2$VAE - Derivation of a Multi-Modal Variational Autoencoder Objective from the Marginal Joint Log-Likelihood","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Timo Korthals","submitted_at":"2019-03-18T08:45:27Z","abstract_excerpt":"This work gives an in-depth derivation of the trainable evidence lower bound obtained from the marginal joint log-Likelihood with the goal of training a Multi-Modal Variational Autoencoder (M$^2$VAE)."},"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":"1903.07303","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-18T08:45:27Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"d95e4d170b7871b65eabf98d9632e4023d81b51ed29b7edccad6edd287a30649","abstract_canon_sha256":"9a49f91623848818009e9d8c517edb7e4f0ac839f58a7cb048ac0fceeff48ae4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:01.804401Z","signature_b64":"eY7Ejlp+gCiuv9tqAWemzzHRBc65Yl8lqvZP0lkInnNtQxv1IZvvZ8IS+0hmHNrbSS7TLeAyYqEIxoKXBDfjDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4abe5c957ce9911f60b59585f95891f0ceeb1d9c5c860f970a9344dfc767e86e","last_reissued_at":"2026-05-17T23:51:01.803677Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:01.803677Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"M$^2$VAE - Derivation of a Multi-Modal Variational Autoencoder Objective from the Marginal Joint Log-Likelihood","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Timo Korthals","submitted_at":"2019-03-18T08:45:27Z","abstract_excerpt":"This work gives an in-depth derivation of the trainable evidence lower bound obtained from the marginal joint log-Likelihood with the goal of training a Multi-Modal Variational Autoencoder (M$^2$VAE)."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.07303","kind":"arxiv","version":1},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1903.07303","created_at":"2026-05-17T23:51:01.803795+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.07303v1","created_at":"2026-05-17T23:51:01.803795+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.07303","created_at":"2026-05-17T23:51:01.803795+00:00"},{"alias_kind":"pith_short_12","alias_value":"JK7FZFL45GIR","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"JK7FZFL45GIR6YFV","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"JK7FZFL4","created_at":"2026-05-18T12:33:21.387695+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JK7FZFL45GIR6YFVSWC7SWER6D","json":"https://pith.science/pith/JK7FZFL45GIR6YFVSWC7SWER6D.json","graph_json":"https://pith.science/api/pith-number/JK7FZFL45GIR6YFVSWC7SWER6D/graph.json","events_json":"https://pith.science/api/pith-number/JK7FZFL45GIR6YFVSWC7SWER6D/events.json","paper":"https://pith.science/paper/JK7FZFL4"},"agent_actions":{"view_html":"https://pith.science/pith/JK7FZFL45GIR6YFVSWC7SWER6D","download_json":"https://pith.science/pith/JK7FZFL45GIR6YFVSWC7SWER6D.json","view_paper":"https://pith.science/paper/JK7FZFL4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.07303&json=true","fetch_graph":"https://pith.science/api/pith-number/JK7FZFL45GIR6YFVSWC7SWER6D/graph.json","fetch_events":"https://pith.science/api/pith-number/JK7FZFL45GIR6YFVSWC7SWER6D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JK7FZFL45GIR6YFVSWC7SWER6D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JK7FZFL45GIR6YFVSWC7SWER6D/action/storage_attestation","attest_author":"https://pith.science/pith/JK7FZFL45GIR6YFVSWC7SWER6D/action/author_attestation","sign_citation":"https://pith.science/pith/JK7FZFL45GIR6YFVSWC7SWER6D/action/citation_signature","submit_replication":"https://pith.science/pith/JK7FZFL45GIR6YFVSWC7SWER6D/action/replication_record"}},"created_at":"2026-05-17T23:51:01.803795+00:00","updated_at":"2026-05-17T23:51:01.803795+00:00"}