{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:MJ4QFDJEQQ7TTG27SFVFAK3CXC","short_pith_number":"pith:MJ4QFDJE","schema_version":"1.0","canonical_sha256":"6279028d24843f399b5f916a502b62b8a8c9f4e7c66022fcb1f8e941968e0dce","source":{"kind":"arxiv","id":"1610.00163","version":2},"attestation_state":"computed","paper":{"title":"X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"stat.ML","authors_text":"Duo Wang, Nicholas D. Lane, Petar Veli\\v{c}kovi\\'c, Pietro Li\\`o","submitted_at":"2016-10-01T18:01:35Z","abstract_excerpt":"In this paper we propose cross-modal convolutional neural networks (X-CNNs), a novel biologically inspired type of CNN architectures, treating gradient descent-specialised CNNs as individual units of processing in a larger-scale network topology, while allowing for unconstrained information flow and/or weight sharing between analogous hidden layers of the network---thus generalising the already well-established concept of neural network ensembles (where information typically may flow only between the output layers of the individual networks). The constituent networks are individually designed "},"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":"1610.00163","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-10-01T18:01:35Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"d63c604162858592313d547158d40f7b64029f1e66d861e226e0c0aabcb3cb60","abstract_canon_sha256":"ebcd2994d3dba89fd352b39a27fadfe465c9d597357c3f62fb51611252b777e0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:34:31.567545Z","signature_b64":"Z6wP3cw9yZmfhmbAp8rIXRnuBpp0/zPwt4I2VhMBLRTy0EBAs0bNHlAfHVWhqZvj8PjnDo6rU7c/FbSkzUkLDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6279028d24843f399b5f916a502b62b8a8c9f4e7c66022fcb1f8e941968e0dce","last_reissued_at":"2026-05-18T00:34:31.567100Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:34:31.567100Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"stat.ML","authors_text":"Duo Wang, Nicholas D. Lane, Petar Veli\\v{c}kovi\\'c, Pietro Li\\`o","submitted_at":"2016-10-01T18:01:35Z","abstract_excerpt":"In this paper we propose cross-modal convolutional neural networks (X-CNNs), a novel biologically inspired type of CNN architectures, treating gradient descent-specialised CNNs as individual units of processing in a larger-scale network topology, while allowing for unconstrained information flow and/or weight sharing between analogous hidden layers of the network---thus generalising the already well-established concept of neural network ensembles (where information typically may flow only between the output layers of the individual networks). The constituent networks are individually designed "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.00163","kind":"arxiv","version":2},"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":"1610.00163","created_at":"2026-05-18T00:34:31.567160+00:00"},{"alias_kind":"arxiv_version","alias_value":"1610.00163v2","created_at":"2026-05-18T00:34:31.567160+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.00163","created_at":"2026-05-18T00:34:31.567160+00:00"},{"alias_kind":"pith_short_12","alias_value":"MJ4QFDJEQQ7T","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_16","alias_value":"MJ4QFDJEQQ7TTG27","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_8","alias_value":"MJ4QFDJE","created_at":"2026-05-18T12:30:32.724797+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/MJ4QFDJEQQ7TTG27SFVFAK3CXC","json":"https://pith.science/pith/MJ4QFDJEQQ7TTG27SFVFAK3CXC.json","graph_json":"https://pith.science/api/pith-number/MJ4QFDJEQQ7TTG27SFVFAK3CXC/graph.json","events_json":"https://pith.science/api/pith-number/MJ4QFDJEQQ7TTG27SFVFAK3CXC/events.json","paper":"https://pith.science/paper/MJ4QFDJE"},"agent_actions":{"view_html":"https://pith.science/pith/MJ4QFDJEQQ7TTG27SFVFAK3CXC","download_json":"https://pith.science/pith/MJ4QFDJEQQ7TTG27SFVFAK3CXC.json","view_paper":"https://pith.science/paper/MJ4QFDJE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1610.00163&json=true","fetch_graph":"https://pith.science/api/pith-number/MJ4QFDJEQQ7TTG27SFVFAK3CXC/graph.json","fetch_events":"https://pith.science/api/pith-number/MJ4QFDJEQQ7TTG27SFVFAK3CXC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MJ4QFDJEQQ7TTG27SFVFAK3CXC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MJ4QFDJEQQ7TTG27SFVFAK3CXC/action/storage_attestation","attest_author":"https://pith.science/pith/MJ4QFDJEQQ7TTG27SFVFAK3CXC/action/author_attestation","sign_citation":"https://pith.science/pith/MJ4QFDJEQQ7TTG27SFVFAK3CXC/action/citation_signature","submit_replication":"https://pith.science/pith/MJ4QFDJEQQ7TTG27SFVFAK3CXC/action/replication_record"}},"created_at":"2026-05-18T00:34:31.567160+00:00","updated_at":"2026-05-18T00:34:31.567160+00:00"}