{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:NDFFWIO2QPESQTMCHRS6ML3RRU","short_pith_number":"pith:NDFFWIO2","canonical_record":{"source":{"id":"2504.16109","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-17T17:58:23Z","cross_cats_sorted":[],"title_canon_sha256":"eb2f75d3ca39970eef5a705f260719800602abb08dcc3768af7769adcb2be8d4","abstract_canon_sha256":"3a8de24837593b3c589dafe71ccf39935b9103a6c31522d36fc676b2d8238b8c"},"schema_version":"1.0"},"canonical_sha256":"68ca5b21da83c9284d823c65e62f718d307db5b48a66df9be26be38eee90d35d","source":{"kind":"arxiv","id":"2504.16109","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.16109","created_at":"2026-07-05T10:52:41Z"},{"alias_kind":"arxiv_version","alias_value":"2504.16109v1","created_at":"2026-07-05T10:52:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.16109","created_at":"2026-07-05T10:52:41Z"},{"alias_kind":"pith_short_12","alias_value":"NDFFWIO2QPES","created_at":"2026-07-05T10:52:41Z"},{"alias_kind":"pith_short_16","alias_value":"NDFFWIO2QPESQTMC","created_at":"2026-07-05T10:52:41Z"},{"alias_kind":"pith_short_8","alias_value":"NDFFWIO2","created_at":"2026-07-05T10:52:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:NDFFWIO2QPESQTMCHRS6ML3RRU","target":"record","payload":{"canonical_record":{"source":{"id":"2504.16109","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-17T17:58:23Z","cross_cats_sorted":[],"title_canon_sha256":"eb2f75d3ca39970eef5a705f260719800602abb08dcc3768af7769adcb2be8d4","abstract_canon_sha256":"3a8de24837593b3c589dafe71ccf39935b9103a6c31522d36fc676b2d8238b8c"},"schema_version":"1.0"},"canonical_sha256":"68ca5b21da83c9284d823c65e62f718d307db5b48a66df9be26be38eee90d35d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:52:41.550115Z","signature_b64":"WtsiFq4rDZwTajDtJrJ+vx+hY8RaBHzVH+v22o58AQ7nFMwk17NiK5uxEyQtF430CP3y4HnbapTc/f3wmCwEDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"68ca5b21da83c9284d823c65e62f718d307db5b48a66df9be26be38eee90d35d","last_reissued_at":"2026-07-05T10:52:41.549668Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:52:41.549668Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.16109","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:52:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Te8KBcMuVGV3hWdmrcf4jrX825GPsaD+iVBgiPwVN3zCGn4yZrdALhiqJwfxetwmYwYSJ0ozVZE4ARkddB53BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T20:55:30.027688Z"},"content_sha256":"cb01e933c8de7ef24a1893afa64f2f7b09c5b88eb1de4dc66d3a7204f8781d96","schema_version":"1.0","event_id":"sha256:cb01e933c8de7ef24a1893afa64f2f7b09c5b88eb1de4dc66d3a7204f8781d96"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:NDFFWIO2QPESQTMCHRS6ML3RRU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Representation Learning for Tabular Data: A Comprehensive Survey","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Han-Jia Ye, Hao-Run Cai, Jun-Peng Jiang, Qile Zhou, Si-Yang Liu","submitted_at":"2025-04-17T17:58:23Z","abstract_excerpt":"Tabular data, structured as rows and columns, is among the most prevalent data types in machine learning classification and regression applications. Models for learning from tabular data have continuously evolved, with Deep Neural Networks (DNNs) recently demonstrating promising results through their capability of representation learning. In this survey, we systematically introduce the field of tabular representation learning, covering the background, challenges, and benchmarks, along with the pros and cons of using DNNs. We organize existing methods into three main categories according to the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.16109","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/2504.16109/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:52:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L1A2x0F7rwwHk616jgEpxW3j7nPGXp02VadLAWlgIFut+U8PFZZr8f7kN72bAvtfLTm03rYPGXjyv2WKRCWyCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T20:55:30.028350Z"},"content_sha256":"2d15c0091b5311fbcfb842a263d9c5f8e3471a06664cf9de80ef65676ae65f33","schema_version":"1.0","event_id":"sha256:2d15c0091b5311fbcfb842a263d9c5f8e3471a06664cf9de80ef65676ae65f33"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NDFFWIO2QPESQTMCHRS6ML3RRU/bundle.json","state_url":"https://pith.science/pith/NDFFWIO2QPESQTMCHRS6ML3RRU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NDFFWIO2QPESQTMCHRS6ML3RRU/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-04T20:55:30Z","links":{"resolver":"https://pith.science/pith/NDFFWIO2QPESQTMCHRS6ML3RRU","bundle":"https://pith.science/pith/NDFFWIO2QPESQTMCHRS6ML3RRU/bundle.json","state":"https://pith.science/pith/NDFFWIO2QPESQTMCHRS6ML3RRU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NDFFWIO2QPESQTMCHRS6ML3RRU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:NDFFWIO2QPESQTMCHRS6ML3RRU","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":"3a8de24837593b3c589dafe71ccf39935b9103a6c31522d36fc676b2d8238b8c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-17T17:58:23Z","title_canon_sha256":"eb2f75d3ca39970eef5a705f260719800602abb08dcc3768af7769adcb2be8d4"},"schema_version":"1.0","source":{"id":"2504.16109","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.16109","created_at":"2026-07-05T10:52:41Z"},{"alias_kind":"arxiv_version","alias_value":"2504.16109v1","created_at":"2026-07-05T10:52:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.16109","created_at":"2026-07-05T10:52:41Z"},{"alias_kind":"pith_short_12","alias_value":"NDFFWIO2QPES","created_at":"2026-07-05T10:52:41Z"},{"alias_kind":"pith_short_16","alias_value":"NDFFWIO2QPESQTMC","created_at":"2026-07-05T10:52:41Z"},{"alias_kind":"pith_short_8","alias_value":"NDFFWIO2","created_at":"2026-07-05T10:52:41Z"}],"graph_snapshots":[{"event_id":"sha256:2d15c0091b5311fbcfb842a263d9c5f8e3471a06664cf9de80ef65676ae65f33","target":"graph","created_at":"2026-07-05T10:52:41Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2504.16109/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Tabular data, structured as rows and columns, is among the most prevalent data types in machine learning classification and regression applications. Models for learning from tabular data have continuously evolved, with Deep Neural Networks (DNNs) recently demonstrating promising results through their capability of representation learning. In this survey, we systematically introduce the field of tabular representation learning, covering the background, challenges, and benchmarks, along with the pros and cons of using DNNs. We organize existing methods into three main categories according to the","authors_text":"Han-Jia Ye, Hao-Run Cai, Jun-Peng Jiang, Qile Zhou, Si-Yang Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-17T17:58:23Z","title":"Representation Learning for Tabular Data: A Comprehensive Survey"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.16109","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:cb01e933c8de7ef24a1893afa64f2f7b09c5b88eb1de4dc66d3a7204f8781d96","target":"record","created_at":"2026-07-05T10:52:41Z","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":"3a8de24837593b3c589dafe71ccf39935b9103a6c31522d36fc676b2d8238b8c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-17T17:58:23Z","title_canon_sha256":"eb2f75d3ca39970eef5a705f260719800602abb08dcc3768af7769adcb2be8d4"},"schema_version":"1.0","source":{"id":"2504.16109","kind":"arxiv","version":1}},"canonical_sha256":"68ca5b21da83c9284d823c65e62f718d307db5b48a66df9be26be38eee90d35d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"68ca5b21da83c9284d823c65e62f718d307db5b48a66df9be26be38eee90d35d","first_computed_at":"2026-07-05T10:52:41.549668Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:52:41.549668Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WtsiFq4rDZwTajDtJrJ+vx+hY8RaBHzVH+v22o58AQ7nFMwk17NiK5uxEyQtF430CP3y4HnbapTc/f3wmCwEDA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:52:41.550115Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.16109","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cb01e933c8de7ef24a1893afa64f2f7b09c5b88eb1de4dc66d3a7204f8781d96","sha256:2d15c0091b5311fbcfb842a263d9c5f8e3471a06664cf9de80ef65676ae65f33"],"state_sha256":"c892d4efcca422367d357ad36c3a997df6090aae3a5f6c29ad8aed73fc4a3466"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fvQ7I4SV5eHT+T9/17R6cBVpSkdaF3oBhmgBUCOPnhjBfZCv9EQpugnX5vuJflSkrwKQ2vjWLpItaFSOzc4jCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T20:55:30.032988Z","bundle_sha256":"0ea2a99fc4882e7d31948b694b5df52af29c4ebeaf9fd3950f59c94a1bffd349"}}