{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:M573EHDGRMYUMR2CIJSLWY2GQJ","short_pith_number":"pith:M573EHDG","schema_version":"1.0","canonical_sha256":"677fb21c668b314647424264bb6346826d1d2eacf9340fafcbe2476e90e9fe7b","source":{"kind":"arxiv","id":"1611.06306","version":1},"attestation_state":"computed","paper":{"title":"Cross-model convolutional neural network for multiple modality data representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Baoming Dong, Fan Cui, Hongbin Zhai, Jim Jing-Yan Wang, Li Wang, Yanbin Wu","submitted_at":"2016-11-19T05:24:48Z","abstract_excerpt":"A novel data representation method of convolutional neural net- work (CNN) is proposed in this paper to represent data of different modalities. We learn a CNN model for the data of each modality to map the data of differ- ent modalities to a common space, and regularize the new representations in the common space by a cross-model relevance matrix. We further impose that the class label of data points can also be predicted from the CNN representa- tions in the common space. The learning problem is modeled as a minimiza- tion problem, which is solved by an augmented Lagrange method (ALM) with up"},"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":"1611.06306","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-19T05:24:48Z","cross_cats_sorted":[],"title_canon_sha256":"1325a83040ac3c1766f6dbf2a434cc7ece6eeab97a0b39f8e702d80562c6cd5e","abstract_canon_sha256":"cb28588b012c94dc9952de16a5f9d7b0147fdf0817740a384c913f4818f96938"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:57:39.073535Z","signature_b64":"TsTX5mFNfKjr/DyIkMBacsvlkwZFTvFeU10d+sG7dF4TFCym4chObyRP70hzS++tMdIRsYUMPpU0eewWPgkLCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"677fb21c668b314647424264bb6346826d1d2eacf9340fafcbe2476e90e9fe7b","last_reissued_at":"2026-05-18T00:57:39.072892Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:57:39.072892Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cross-model convolutional neural network for multiple modality data representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Baoming Dong, Fan Cui, Hongbin Zhai, Jim Jing-Yan Wang, Li Wang, Yanbin Wu","submitted_at":"2016-11-19T05:24:48Z","abstract_excerpt":"A novel data representation method of convolutional neural net- work (CNN) is proposed in this paper to represent data of different modalities. We learn a CNN model for the data of each modality to map the data of differ- ent modalities to a common space, and regularize the new representations in the common space by a cross-model relevance matrix. We further impose that the class label of data points can also be predicted from the CNN representa- tions in the common space. The learning problem is modeled as a minimiza- tion problem, which is solved by an augmented Lagrange method (ALM) with up"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.06306","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":"1611.06306","created_at":"2026-05-18T00:57:39.072982+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.06306v1","created_at":"2026-05-18T00:57:39.072982+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.06306","created_at":"2026-05-18T00:57:39.072982+00:00"},{"alias_kind":"pith_short_12","alias_value":"M573EHDGRMYU","created_at":"2026-05-18T12:30:29.479603+00:00"},{"alias_kind":"pith_short_16","alias_value":"M573EHDGRMYUMR2C","created_at":"2026-05-18T12:30:29.479603+00:00"},{"alias_kind":"pith_short_8","alias_value":"M573EHDG","created_at":"2026-05-18T12:30:29.479603+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/M573EHDGRMYUMR2CIJSLWY2GQJ","json":"https://pith.science/pith/M573EHDGRMYUMR2CIJSLWY2GQJ.json","graph_json":"https://pith.science/api/pith-number/M573EHDGRMYUMR2CIJSLWY2GQJ/graph.json","events_json":"https://pith.science/api/pith-number/M573EHDGRMYUMR2CIJSLWY2GQJ/events.json","paper":"https://pith.science/paper/M573EHDG"},"agent_actions":{"view_html":"https://pith.science/pith/M573EHDGRMYUMR2CIJSLWY2GQJ","download_json":"https://pith.science/pith/M573EHDGRMYUMR2CIJSLWY2GQJ.json","view_paper":"https://pith.science/paper/M573EHDG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.06306&json=true","fetch_graph":"https://pith.science/api/pith-number/M573EHDGRMYUMR2CIJSLWY2GQJ/graph.json","fetch_events":"https://pith.science/api/pith-number/M573EHDGRMYUMR2CIJSLWY2GQJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M573EHDGRMYUMR2CIJSLWY2GQJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M573EHDGRMYUMR2CIJSLWY2GQJ/action/storage_attestation","attest_author":"https://pith.science/pith/M573EHDGRMYUMR2CIJSLWY2GQJ/action/author_attestation","sign_citation":"https://pith.science/pith/M573EHDGRMYUMR2CIJSLWY2GQJ/action/citation_signature","submit_replication":"https://pith.science/pith/M573EHDGRMYUMR2CIJSLWY2GQJ/action/replication_record"}},"created_at":"2026-05-18T00:57:39.072982+00:00","updated_at":"2026-05-18T00:57:39.072982+00:00"}