{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:HUQX5H3CEAYMCD6FYC22SYXESI","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":"ad8314bb4846b89b6e56eeb985e54254b1359078aee185a9ca9b0c2c823b680c","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-04-25T06:29:59Z","title_canon_sha256":"634f08423c34d7236569250147100141c95d72085a55a198b0371160a4f12388"},"schema_version":"1.0","source":{"id":"1704.07548","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.07548","created_at":"2026-05-18T00:45:37Z"},{"alias_kind":"arxiv_version","alias_value":"1704.07548v1","created_at":"2026-05-18T00:45:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.07548","created_at":"2026-05-18T00:45:37Z"},{"alias_kind":"pith_short_12","alias_value":"HUQX5H3CEAYM","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_16","alias_value":"HUQX5H3CEAYMCD6F","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_8","alias_value":"HUQX5H3C","created_at":"2026-05-18T12:31:18Z"}],"graph_snapshots":[{"event_id":"sha256:35730b357987e8da01fab9f9658a1b0c59a3684472c340865f1dbf040cb4a181","target":"graph","created_at":"2026-05-18T00:45:37Z","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":"In emotion recognition, it is difficult to recognize human's emotional states using just a single modality. Besides, the annotation of physiological emotional data is particularly expensive. These two aspects make the building of effective emotion recognition model challenging. In this paper, we first build a multi-view deep generative model to simulate the generative process of multi-modality emotional data. By imposing a mixture of Gaussians assumption on the posterior approximation of the latent variables, our model can learn the shared deep representation from multiple modalities. To solve","authors_text":"Bao-Liang Lu, Changde Du, Changying Du, Huiguang He, Jinpeng Li, Wei-Long Zheng","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-04-25T06:29:59Z","title":"Semi-supervised Bayesian Deep Multi-modal Emotion Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.07548","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":"sha256:154a499966f150d32b6bdb5c9d1b153bf7b708d6c5ecfe2ca1375415f724092b","target":"record","created_at":"2026-05-18T00:45:37Z","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":"ad8314bb4846b89b6e56eeb985e54254b1359078aee185a9ca9b0c2c823b680c","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-04-25T06:29:59Z","title_canon_sha256":"634f08423c34d7236569250147100141c95d72085a55a198b0371160a4f12388"},"schema_version":"1.0","source":{"id":"1704.07548","kind":"arxiv","version":1}},"canonical_sha256":"3d217e9f622030c10fc5c0b5a962e4921e1738d21d7e7f741b90c5c11eaf176f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3d217e9f622030c10fc5c0b5a962e4921e1738d21d7e7f741b90c5c11eaf176f","first_computed_at":"2026-05-18T00:45:37.712031Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:45:37.712031Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"574e+3m+T5VJo2T4cotwnOI8WNCiFh9RLQWLeQXi6YxGBpssEYz3PslA48g1h41MsUS9cxfMT5c8nJD9dzKTDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:45:37.712525Z","signed_message":"canonical_sha256_bytes"},"source_id":"1704.07548","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:154a499966f150d32b6bdb5c9d1b153bf7b708d6c5ecfe2ca1375415f724092b","sha256:35730b357987e8da01fab9f9658a1b0c59a3684472c340865f1dbf040cb4a181"],"state_sha256":"3c0bea7ef91d1130edd8ad41995c06876bc1665502301afa6e9e0e08eb32805f"}