{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:OKGL5N6GQPBLM2EDHLEXJEJ4MR","short_pith_number":"pith:OKGL5N6G","canonical_record":{"source":{"id":"2410.05016","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-07T13:15:07Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"887de6b2347b7119c6dbadace99ef3b679547782682d2992f61aff92461059b6","abstract_canon_sha256":"577eb84a57ced4cce589df4490a144f71ea0c6010e662c2cb1bd84418cdcf670"},"schema_version":"1.0"},"canonical_sha256":"728cbeb7c683c2b668833ac974913c6465201c8896f6544cdf5b347e0b339abc","source":{"kind":"arxiv","id":"2410.05016","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.05016","created_at":"2026-07-05T10:57:54Z"},{"alias_kind":"arxiv_version","alias_value":"2410.05016v3","created_at":"2026-07-05T10:57:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.05016","created_at":"2026-07-05T10:57:54Z"},{"alias_kind":"pith_short_12","alias_value":"OKGL5N6GQPBL","created_at":"2026-07-05T10:57:54Z"},{"alias_kind":"pith_short_16","alias_value":"OKGL5N6GQPBLM2ED","created_at":"2026-07-05T10:57:54Z"},{"alias_kind":"pith_short_8","alias_value":"OKGL5N6G","created_at":"2026-07-05T10:57:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:OKGL5N6GQPBLM2EDHLEXJEJ4MR","target":"record","payload":{"canonical_record":{"source":{"id":"2410.05016","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-07T13:15:07Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"887de6b2347b7119c6dbadace99ef3b679547782682d2992f61aff92461059b6","abstract_canon_sha256":"577eb84a57ced4cce589df4490a144f71ea0c6010e662c2cb1bd84418cdcf670"},"schema_version":"1.0"},"canonical_sha256":"728cbeb7c683c2b668833ac974913c6465201c8896f6544cdf5b347e0b339abc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:57:54.306714Z","signature_b64":"4ip+CPnWzsTO3HUwfjpEkv23WAYzjVXJmHTkfQyyevroVZ+dTQYC/VQ1MHN9EMqHnq5NNSwLgiZ+ynGQXmsRAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"728cbeb7c683c2b668833ac974913c6465201c8896f6544cdf5b347e0b339abc","last_reissued_at":"2026-07-05T10:57:54.306168Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:57:54.306168Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.05016","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:57:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A5YcgW95xlWKv3Dka3FtMuWwBuA2j0x4q+Ko70Tm0zr1FjS2WmV9lKzyudzUVWLgfOjpiW2ZcDOWdfGiVsbwAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T23:22:11.711523Z"},"content_sha256":"c18ae9930edf9684e30c3e865f79f9ed940045863b276b29d2d28faff1ecf94d","schema_version":"1.0","event_id":"sha256:c18ae9930edf9684e30c3e865f79f9ed940045863b276b29d2d28faff1ecf94d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:OKGL5N6GQPBLM2EDHLEXJEJ4MR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"T-JEPA: Augmentation-Free Self-Supervised Learning for Tabular Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Arpad Rimmel, Bich-Li\\^en Doan, Fabrice Popineau, Hugo Thimonier, Jos\\'e Lucas De Melo Costa","submitted_at":"2024-10-07T13:15:07Z","abstract_excerpt":"Self-supervision is often used for pre-training to foster performance on a downstream task by constructing meaningful representations of samples. Self-supervised learning (SSL) generally involves generating different views of the same sample and thus requires data augmentations that are challenging to construct for tabular data. This constitutes one of the main challenges of self-supervision for structured data. In the present work, we propose a novel augmentation-free SSL method for tabular data. Our approach, T-JEPA, relies on a Joint Embedding Predictive Architecture (JEPA) and is akin to m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.05016","kind":"arxiv","version":3},"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/2410.05016/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":"2026-07-05T10:57:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JYzPu1V3yPZtD76vy81T6VMEL7mIf5RoepFZ9Xpy3BcpvmTV+bv2RKX4Fda6YBN9p/QIBqD34fQ8vB6J3xIdAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T23:22:11.712683Z"},"content_sha256":"91d8d317868e7bfd919115e1acd42a45cb3a392cde3e7ecf69431c7b7b7e8e75","schema_version":"1.0","event_id":"sha256:91d8d317868e7bfd919115e1acd42a45cb3a392cde3e7ecf69431c7b7b7e8e75"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OKGL5N6GQPBLM2EDHLEXJEJ4MR/bundle.json","state_url":"https://pith.science/pith/OKGL5N6GQPBLM2EDHLEXJEJ4MR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OKGL5N6GQPBLM2EDHLEXJEJ4MR/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":"2026-08-03T23:22:11Z","links":{"resolver":"https://pith.science/pith/OKGL5N6GQPBLM2EDHLEXJEJ4MR","bundle":"https://pith.science/pith/OKGL5N6GQPBLM2EDHLEXJEJ4MR/bundle.json","state":"https://pith.science/pith/OKGL5N6GQPBLM2EDHLEXJEJ4MR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OKGL5N6GQPBLM2EDHLEXJEJ4MR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:OKGL5N6GQPBLM2EDHLEXJEJ4MR","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":"577eb84a57ced4cce589df4490a144f71ea0c6010e662c2cb1bd84418cdcf670","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-07T13:15:07Z","title_canon_sha256":"887de6b2347b7119c6dbadace99ef3b679547782682d2992f61aff92461059b6"},"schema_version":"1.0","source":{"id":"2410.05016","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.05016","created_at":"2026-07-05T10:57:54Z"},{"alias_kind":"arxiv_version","alias_value":"2410.05016v3","created_at":"2026-07-05T10:57:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.05016","created_at":"2026-07-05T10:57:54Z"},{"alias_kind":"pith_short_12","alias_value":"OKGL5N6GQPBL","created_at":"2026-07-05T10:57:54Z"},{"alias_kind":"pith_short_16","alias_value":"OKGL5N6GQPBLM2ED","created_at":"2026-07-05T10:57:54Z"},{"alias_kind":"pith_short_8","alias_value":"OKGL5N6G","created_at":"2026-07-05T10:57:54Z"}],"graph_snapshots":[{"event_id":"sha256:91d8d317868e7bfd919115e1acd42a45cb3a392cde3e7ecf69431c7b7b7e8e75","target":"graph","created_at":"2026-07-05T10:57:54Z","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/2410.05016/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Self-supervision is often used for pre-training to foster performance on a downstream task by constructing meaningful representations of samples. Self-supervised learning (SSL) generally involves generating different views of the same sample and thus requires data augmentations that are challenging to construct for tabular data. This constitutes one of the main challenges of self-supervision for structured data. In the present work, we propose a novel augmentation-free SSL method for tabular data. Our approach, T-JEPA, relies on a Joint Embedding Predictive Architecture (JEPA) and is akin to m","authors_text":"Arpad Rimmel, Bich-Li\\^en Doan, Fabrice Popineau, Hugo Thimonier, Jos\\'e Lucas De Melo Costa","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-07T13:15:07Z","title":"T-JEPA: Augmentation-Free Self-Supervised Learning for Tabular Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.05016","kind":"arxiv","version":3},"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:c18ae9930edf9684e30c3e865f79f9ed940045863b276b29d2d28faff1ecf94d","target":"record","created_at":"2026-07-05T10:57:54Z","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":"577eb84a57ced4cce589df4490a144f71ea0c6010e662c2cb1bd84418cdcf670","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-07T13:15:07Z","title_canon_sha256":"887de6b2347b7119c6dbadace99ef3b679547782682d2992f61aff92461059b6"},"schema_version":"1.0","source":{"id":"2410.05016","kind":"arxiv","version":3}},"canonical_sha256":"728cbeb7c683c2b668833ac974913c6465201c8896f6544cdf5b347e0b339abc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"728cbeb7c683c2b668833ac974913c6465201c8896f6544cdf5b347e0b339abc","first_computed_at":"2026-07-05T10:57:54.306168Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:57:54.306168Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4ip+CPnWzsTO3HUwfjpEkv23WAYzjVXJmHTkfQyyevroVZ+dTQYC/VQ1MHN9EMqHnq5NNSwLgiZ+ynGQXmsRAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:57:54.306714Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.05016","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c18ae9930edf9684e30c3e865f79f9ed940045863b276b29d2d28faff1ecf94d","sha256:91d8d317868e7bfd919115e1acd42a45cb3a392cde3e7ecf69431c7b7b7e8e75"],"state_sha256":"35a8f172c473172364bee0a2ff391e2da70659c9a7d75a74cc09fe932727fe88"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ej2SbfGpSmLKm3YqSuKuHpjDPjWQPGFjX8iD31O+lIOjPIKpjSi/qQ/oJxZ1BfW8lhYVhihUflzZ/HYsx+XcAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T23:22:11.717589Z","bundle_sha256":"57d03ee9e5443f7d8f21024c6a13f219672893e43847c3726514355b123b3427"}}