{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:WBDH6DEZZ2GKMNXJTCN7L6P37E","short_pith_number":"pith:WBDH6DEZ","schema_version":"1.0","canonical_sha256":"b0467f0c99ce8ca636e9989bf5f9fbf92f35428c99b1cd527b143ee30fed7102","source":{"kind":"arxiv","id":"1806.00064","version":1},"attestation_state":"computed","paper":{"title":"Efficient Low-rank Multimodal Fusion with Modality-Specific Factors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.AI","authors_text":"Amir Zadeh, Louis-Philippe Morency, Paul Pu Liang, Varun Bharadhwaj Lakshminarasimhan, Ying Shen, Zhun Liu","submitted_at":"2018-05-31T19:28:23Z","abstract_excerpt":"Multimodal research is an emerging field of artificial intelligence, and one of the main research problems in this field is multimodal fusion. The fusion of multimodal data is the process of integrating multiple unimodal representations into one compact multimodal representation. Previous research in this field has exploited the expressiveness of tensors for multimodal representation. However, these methods often suffer from exponential increase in dimensions and in computational complexity introduced by transformation of input into tensor. In this paper, we propose the Low-rank Multimodal Fus"},"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":"1806.00064","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-05-31T19:28:23Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"f0719831620593924ebccd8a03097a013254f3575ba99f8d39e07942d6ac3c18","abstract_canon_sha256":"e78c45b139bf91dbceae20077de09dce677d07a201a1dfd0412bc1a2ce123d9f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:28.037452Z","signature_b64":"eHpK9/4RBojTNeYktgvPAy9zK42j3+W30HVhROjRyW49TSc/Sx7QXonz9k6vXRGM1RV+ePlRHMlUoiZTWJkGBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b0467f0c99ce8ca636e9989bf5f9fbf92f35428c99b1cd527b143ee30fed7102","last_reissued_at":"2026-05-18T00:14:28.036699Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:28.036699Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Low-rank Multimodal Fusion with Modality-Specific Factors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.AI","authors_text":"Amir Zadeh, Louis-Philippe Morency, Paul Pu Liang, Varun Bharadhwaj Lakshminarasimhan, Ying Shen, Zhun Liu","submitted_at":"2018-05-31T19:28:23Z","abstract_excerpt":"Multimodal research is an emerging field of artificial intelligence, and one of the main research problems in this field is multimodal fusion. The fusion of multimodal data is the process of integrating multiple unimodal representations into one compact multimodal representation. Previous research in this field has exploited the expressiveness of tensors for multimodal representation. However, these methods often suffer from exponential increase in dimensions and in computational complexity introduced by transformation of input into tensor. In this paper, we propose the Low-rank Multimodal Fus"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.00064","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":"1806.00064","created_at":"2026-05-18T00:14:28.036824+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.00064v1","created_at":"2026-05-18T00:14:28.036824+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.00064","created_at":"2026-05-18T00:14:28.036824+00:00"},{"alias_kind":"pith_short_12","alias_value":"WBDH6DEZZ2GK","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_16","alias_value":"WBDH6DEZZ2GKMNXJ","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_8","alias_value":"WBDH6DEZ","created_at":"2026-05-18T12:32:59.047623+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2508.04999","citing_title":"Disentangling Bias by Modeling Intra- and Inter-modal Causal Attention for Multimodal Sentiment Analysis","ref_index":1,"is_internal_anchor":true},{"citing_arxiv_id":"2511.20657","citing_title":"Intelligent Agents with Emotional Intelligence: Current Trends, Challenges, and Future Prospects","ref_index":200,"is_internal_anchor":true},{"citing_arxiv_id":"2605.13897","citing_title":"Attention-Based Multimodal Survival Prediction with Cross-Modal Bilinear Fusion","ref_index":12,"is_internal_anchor":true},{"citing_arxiv_id":"2605.00370","citing_title":"Group Cognition Learning: Making Everything Better Through Governed Two-Stage Agents Collaboration","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10498","citing_title":"Simultaneous Long-tailed Recognition and Multi-modal Fusion for Highly Imbalanced Multi-modal Data","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06289","citing_title":"Multimodal Deep Generative Model for Semi-Supervised Learning under Class Imbalance","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00370","citing_title":"Group Cognition Learning: Making Everything Better Through Governed Two-Stage Agents Collaboration","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WBDH6DEZZ2GKMNXJTCN7L6P37E","json":"https://pith.science/pith/WBDH6DEZZ2GKMNXJTCN7L6P37E.json","graph_json":"https://pith.science/api/pith-number/WBDH6DEZZ2GKMNXJTCN7L6P37E/graph.json","events_json":"https://pith.science/api/pith-number/WBDH6DEZZ2GKMNXJTCN7L6P37E/events.json","paper":"https://pith.science/paper/WBDH6DEZ"},"agent_actions":{"view_html":"https://pith.science/pith/WBDH6DEZZ2GKMNXJTCN7L6P37E","download_json":"https://pith.science/pith/WBDH6DEZZ2GKMNXJTCN7L6P37E.json","view_paper":"https://pith.science/paper/WBDH6DEZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.00064&json=true","fetch_graph":"https://pith.science/api/pith-number/WBDH6DEZZ2GKMNXJTCN7L6P37E/graph.json","fetch_events":"https://pith.science/api/pith-number/WBDH6DEZZ2GKMNXJTCN7L6P37E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WBDH6DEZZ2GKMNXJTCN7L6P37E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WBDH6DEZZ2GKMNXJTCN7L6P37E/action/storage_attestation","attest_author":"https://pith.science/pith/WBDH6DEZZ2GKMNXJTCN7L6P37E/action/author_attestation","sign_citation":"https://pith.science/pith/WBDH6DEZZ2GKMNXJTCN7L6P37E/action/citation_signature","submit_replication":"https://pith.science/pith/WBDH6DEZZ2GKMNXJTCN7L6P37E/action/replication_record"}},"created_at":"2026-05-18T00:14:28.036824+00:00","updated_at":"2026-05-18T00:14:28.036824+00:00"}