{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NV35LX6GROLJKQBBT7MCYSJC7K","short_pith_number":"pith:NV35LX6G","schema_version":"1.0","canonical_sha256":"6d77d5dfc68b969540219fd82c4922fa9d495c15757e69819def454626c54640","source":{"kind":"arxiv","id":"2501.03540","version":1},"attestation_state":"computed","paper":{"title":"Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Tianxiang Zhao, Vasant Honavar, Weijieying Ren, Yuqing Huang","submitted_at":"2025-01-07T05:23:36Z","abstract_excerpt":"Tabular data remains one of the most prevalent data types across a wide range of real-world applications, yet effective representation learning for this domain poses unique challenges due to its irregular patterns, heterogeneous feature distributions, and complex inter-column dependencies. This survey provides a comprehensive review of state-of-the-art techniques in tabular data representation learning, structured around three foundational design elements: training data, neural architectures, and learning objectives. Unlike prior surveys that focus primarily on either architecture design or le"},"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":"2501.03540","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-07T05:23:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a48c1153212afb1a690f7d69a0d903a961d1e36677b87abfc111fabb52e26526","abstract_canon_sha256":"b4ba08381ecdf7e5baf844b789926f84b37b6bfe362a3d45a3a06c4ccdb144dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:57:56.303484Z","signature_b64":"DAQeQzH8wNEIErlmI4jRtbC5mB1goDimnIEw7MfdXsL+W8iIjSijKd9nQw2fprXnMd/9dF/ouRWwMu1nHJT9Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d77d5dfc68b969540219fd82c4922fa9d495c15757e69819def454626c54640","last_reissued_at":"2026-07-05T09:57:56.303005Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:57:56.303005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Tianxiang Zhao, Vasant Honavar, Weijieying Ren, Yuqing Huang","submitted_at":"2025-01-07T05:23:36Z","abstract_excerpt":"Tabular data remains one of the most prevalent data types across a wide range of real-world applications, yet effective representation learning for this domain poses unique challenges due to its irregular patterns, heterogeneous feature distributions, and complex inter-column dependencies. This survey provides a comprehensive review of state-of-the-art techniques in tabular data representation learning, structured around three foundational design elements: training data, neural architectures, and learning objectives. Unlike prior surveys that focus primarily on either architecture design or le"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.03540","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.03540/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2501.03540","created_at":"2026-07-05T09:57:56.303061+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.03540v1","created_at":"2026-07-05T09:57:56.303061+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.03540","created_at":"2026-07-05T09:57:56.303061+00:00"},{"alias_kind":"pith_short_12","alias_value":"NV35LX6GROLJ","created_at":"2026-07-05T09:57:56.303061+00:00"},{"alias_kind":"pith_short_16","alias_value":"NV35LX6GROLJKQBB","created_at":"2026-07-05T09:57:56.303061+00:00"},{"alias_kind":"pith_short_8","alias_value":"NV35LX6G","created_at":"2026-07-05T09:57:56.303061+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.16383","citing_title":"Multivariate Uncertainty Quantification with Tomographic Quantile Forests","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NV35LX6GROLJKQBBT7MCYSJC7K","json":"https://pith.science/pith/NV35LX6GROLJKQBBT7MCYSJC7K.json","graph_json":"https://pith.science/api/pith-number/NV35LX6GROLJKQBBT7MCYSJC7K/graph.json","events_json":"https://pith.science/api/pith-number/NV35LX6GROLJKQBBT7MCYSJC7K/events.json","paper":"https://pith.science/paper/NV35LX6G"},"agent_actions":{"view_html":"https://pith.science/pith/NV35LX6GROLJKQBBT7MCYSJC7K","download_json":"https://pith.science/pith/NV35LX6GROLJKQBBT7MCYSJC7K.json","view_paper":"https://pith.science/paper/NV35LX6G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.03540&json=true","fetch_graph":"https://pith.science/api/pith-number/NV35LX6GROLJKQBBT7MCYSJC7K/graph.json","fetch_events":"https://pith.science/api/pith-number/NV35LX6GROLJKQBBT7MCYSJC7K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NV35LX6GROLJKQBBT7MCYSJC7K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NV35LX6GROLJKQBBT7MCYSJC7K/action/storage_attestation","attest_author":"https://pith.science/pith/NV35LX6GROLJKQBBT7MCYSJC7K/action/author_attestation","sign_citation":"https://pith.science/pith/NV35LX6GROLJKQBBT7MCYSJC7K/action/citation_signature","submit_replication":"https://pith.science/pith/NV35LX6GROLJKQBBT7MCYSJC7K/action/replication_record"}},"created_at":"2026-07-05T09:57:56.303061+00:00","updated_at":"2026-07-05T09:57:56.303061+00:00"}