{"as_of":"2026-08-09T20:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:01d9406ecd92930e773004c6b74770c461e41f86c7f9eb7485e495fe06c84fc3","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":44,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":44,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T11:34:30.740675Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":120,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2207.01848","last_updated":"2023-09-16T09:33:32Z","snapshot_observed_at":"2026-07-06T13:27:49.894090Z","submitted_at":"2022-07-05T07:17:43Z","title":"TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second","version":6},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-15T03:04:00.513443Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2207.01848"},"observation_digest":"sha256:821e929b3ca5bc58f138e0b8e1fe884b69cd4189abed0a7029bca1441b41b9a6","observation_id":"f9ff442e-c37f-4a87-a04c-8bcd54b0d96d","resolution":{"observed_at":"2026-05-15T03:04:00.566990Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-09T11:34:30.740675Z","title":"Saint: Improved neural networks for tabular data via row attention and contrastive pre-training","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.02672","last_updated":"2025-02-06T02:39:35Z","snapshot_observed_at":"2026-08-09T11:28:54.378852Z","submitted_at":"2025-02-04T19:30:41Z","title":"Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T11:34:30.740675Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2502.02672"},"observation_digest":"sha256:3a4cd1665afa28fba18b09717416772e6bf91f2d838185f78b4130f9bfdd9d04","observation_id":"0ae56d11-982f-4f8e-8221-781a2855b5af","resolution":{"observed_at":"2026-08-09T11:34:30.740675Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-09T05:48:07.665569Z","title":"B., and Goldstein, T","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.03147","last_updated":"2025-02-05T13:16:41Z","snapshot_observed_at":"2026-08-09T05:40:55.387361Z","submitted_at":"2025-02-05T13:16:41Z","title":"Scalable In-Context Learning on Tabular Data via Retrieval-Augmented Large Language Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T05:48:07.665569Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2502.03147"},"observation_digest":"sha256:50fadcd76b96d65bd64ea88b7e37827b01e88b90044a336436d7af680331a28e","observation_id":"8a60e3bd-11f2-4885-8d8f-feb54cbb83fd","resolution":{"observed_at":"2026-08-09T05:48:07.665569Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-09T04:26:41.694395Z","title":"B., and Goldstein, T","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.03608","last_updated":"2025-02-05T20:53:16Z","snapshot_observed_at":"2026-08-09T04:19:12.610416Z","submitted_at":"2025-02-05T20:53:16Z","title":"(GG) MoE vs. MLP on Tabular Data","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T04:26:41.694395Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2502.03608"},"observation_digest":"sha256:e6be0687dca4824ba38f4b1ac9651547bf9cd9ccc3b7e1064156a826f934dc4c","observation_id":"0ed0bb05-a327-4f09-a488-55ea5057aae9","resolution":{"observed_at":"2026-08-09T04:26:41.694395Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-08T22:21:08.027224Z","title":"B., and Goldstein, T","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-09T04:39:26.910275Z","submitted_at":"2025-02-06T23:58:11Z","title":"Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer","version":2},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.027224Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:a6f78d68afa9038a34dc32622fac76c0248b78a24ef41297bc102982f1ff0e8a","observation_id":"94b2a683-17c5-4f29-8290-c46a1e4cc1e7","resolution":{"observed_at":"2026-08-08T22:21:08.027224Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-07T15:39:26.596297Z","title":"Saint: Improved neural networks for tabular data via row attention and contrastive pre-training","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.14312","last_updated":"2025-05-20T13:00:43Z","snapshot_observed_at":"2026-08-07T15:34:25.974450Z","submitted_at":"2025-05-20T13:00:43Z","title":"MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T15:39:26.596297Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2505.14312"},"observation_digest":"sha256:7e0e589eaaa19f46c4a3e2664dae6b68ae3acd28411f3ab9fc331a4c8594a3e2","observation_id":"c2c9ef15-af47-4570-81f5-3cbabc79b3db","resolution":{"observed_at":"2026-08-07T15:39:26.596297Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-07T13:40:22.016710Z","title":"Saint: Improved neural networks for tabular data via row attention and contrastive pre-training","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.21422","last_updated":"2025-05-27T16:50:44Z","snapshot_observed_at":"2026-08-08T21:43:36.948248Z","submitted_at":"2025-05-27T16:50:44Z","title":"When Shift Happens - Confounding Is to Blame","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-07T13:40:22.016710Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2505.21422"},"observation_digest":"sha256:066dce22c23a350ad9496d1b50e22f7799e5ea601299da0ec8d21d692dfd76e9","observation_id":"adcab557-11c1-4a46-8a17-ec010f9f0a85","resolution":{"observed_at":"2026-08-07T13:40:22.016710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-07T12:03:08.434756Z","title":"Somepalli, M","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.00813","last_updated":"2025-06-01T03:29:30Z","snapshot_observed_at":"2026-08-08T19:36:59.253325Z","submitted_at":"2025-06-01T03:29:30Z","title":"TIME: TabPFN-Integrated Multimodal Engine for Robust Tabular-Image Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T12:03:08.434756Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2506.00813"},"observation_digest":"sha256:ad1920c14323316c23736d2db0dd70ee34eb6f82b4b9dcb9261645cddbc89354","observation_id":"55361ce2-4fb3-4f96-96a4-fc35361ef874","resolution":{"observed_at":"2026-08-07T12:03:08.434756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-07T11:29:50.672770Z","title":"B., and Goldstein, T","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.02406","last_updated":"2025-06-03T03:45:13Z","snapshot_observed_at":"2026-08-09T18:34:02.644753Z","submitted_at":"2025-06-03T03:45:13Z","title":"Random at First, Fast at Last: NTK-Guided Fourier Pre-Processing for Tabular DL","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T11:29:50.672770Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2506.02406"},"observation_digest":"sha256:ba7aa912cff5cd244f72e7c6a014ade3f51b60620f883812100d3c59b3f64275","observation_id":"91f0b308-31c0-4e6c-bd6e-d2337e14594e","resolution":{"observed_at":"2026-08-07T11:29:50.672770Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-07T10:23:06.405863Z","title":"B., and Goldstein, T","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.05584","last_updated":"2025-06-05T20:59:33Z","snapshot_observed_at":"2026-08-08T04:55:43.591413Z","submitted_at":"2025-06-05T20:59:33Z","title":"TabFlex: Scaling Tabular Learning to Millions with Linear Attention","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-07T10:23:06.405863Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2506.05584"},"observation_digest":"sha256:c0fcea9909f56a964c6122749ec90da169b06ef575831d55e58ed6628dfb5695","observation_id":"8258b155-2756-43ea-882c-914cdcad3fa8","resolution":{"observed_at":"2026-08-07T10:23:06.405863Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-07T05:01:16.394064Z","title":"B., and Goldstein, T","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.08982","last_updated":"2025-06-11T09:47:49Z","snapshot_observed_at":"2026-08-07T05:43:15.063082Z","submitted_at":"2025-06-10T16:52:31Z","title":"On Finetuning Tabular Foundation Models","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-07T05:01:16.394064Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2506.08982"},"observation_digest":"sha256:91bcd3dcae41176bf3b7dd7d3fe311f1915151ef8bdf5fed20d77799c641e98f","observation_id":"1d180baa-2d8b-4f52-bccf-26d36e070ebc","resolution":{"observed_at":"2026-08-07T05:01:16.394064Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-06T19:46:41.626833Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.04687","last_updated":"2025-07-08T16:51:53Z","snapshot_observed_at":"2026-08-06T19:39:54.563905Z","submitted_at":"2025-07-07T06:08:45Z","title":"LAKEGEN: A LLM-based Tabular Corpus Generator for Evaluating Dataset Discovery in Data Lakes","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T19:46:41.626833Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2507.04687"},"observation_digest":"sha256:44be40eb60a075bab89549474634157e93a1ebbf58f3a758a1f9ef2a0c20188a","observation_id":"b79f17ec-2440-45e6-bdde-d000e72ff68a","resolution":{"observed_at":"2026-08-06T19:46:41.626833Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-06T19:47:57.907153Z","title":"Goldblum, A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.04779","last_updated":"2025-09-12T03:29:11Z","snapshot_observed_at":"2026-08-09T01:29:00.499958Z","submitted_at":"2025-07-07T08:55:28Z","title":"Constructive Universal Approximation and Sure Convergence for Multi-Layer Neural Networks","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-06T19:47:57.907153Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2507.04779"},"observation_digest":"sha256:5fa32e4f3d8e419941c19dc492686fa8672cb8e00db38841cbc5c8b1f7f47bc3","observation_id":"e60dbf0e-f69c-4529-8d5c-a3f91de58a33","resolution":{"observed_at":"2026-08-06T19:47:57.907153Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-06T19:26:05.053459Z","title":"Saint: Improved neural networks for tabular data via row attention and contrastive pre-training","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.05651","last_updated":"2025-07-08T04:10:25Z","snapshot_observed_at":"2026-08-08T06:16:58.595027Z","submitted_at":"2025-07-08T04:10:25Z","title":"City-Level Foreign Direct Investment Prediction with Tabular Learning on Judicial Data","version":1},"reference_index":1990,"source":"pdf_text","source_observed_at":"2026-08-06T19:26:05.053459Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2507.05651"},"observation_digest":"sha256:1f4ff8eb4b056b271811ce906e49b4747ff0299adbc1aec13fa9714e208bf7a5","observation_id":"ec5fd82a-ace0-4960-8abe-5b16709fa4fb","resolution":{"observed_at":"2026-08-06T19:26:05.053459Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-06T18:31:52.669644Z","title":"Bayan Bruss, and Tom Goldstein","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:52.669644Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:4dd006c0b01eb6257f6ea91f283bd380d5a457e14100f58445589f2bf1252db1","observation_id":"fc3c1b1c-f961-44c5-87ca-878e3eed7da6","resolution":{"observed_at":"2026-08-06T18:31:52.669644Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2509.11449","last_updated":"2025-09-14T21:46:17Z","snapshot_observed_at":"2026-07-06T22:29:58.066459Z","submitted_at":"2025-09-14T21:46:17Z","title":"Tabular Data with Class Imbalance: Predicting Electric Vehicle Crash Severity with Pretrained Transformers (TabPFN) and Mamba-Based Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-18T16:04:22.198171Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2509.11449"},"observation_digest":"sha256:b9a8b7b89a0574ef668c61b0bb7e796a174393bd0ee64c39b73cd066ff4eb4b6","observation_id":"bdac9e6c-1dbf-4386-ad96-c84cb7eb5647","resolution":{"observed_at":"2026-05-18T16:06:35.062397Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-04T06:07:17.068181Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2602.10441","last_updated":"2026-08-03T09:45:34Z","snapshot_observed_at":"2026-08-08T17:27:35.063258Z","submitted_at":"2026-02-11T02:33:29Z","title":"LakeMLB: Data Lake Machine Learning Benchmark","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T06:07:17.068181Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2602.10441"},"observation_digest":"sha256:debeedb92d279316b207d76a165655cc717f9d83ce4a626548e50cc5a8501344","observation_id":"fb825a22-4e05-4d56-8f00-daa86d2feaee","resolution":{"observed_at":"2026-08-04T06:07:17.068181Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-02T23:33:32.469224Z","title":"B., and Goldstein, T","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.13697","last_updated":"2026-06-04T09:11:11Z","snapshot_observed_at":"2026-08-09T09:08:14.315139Z","submitted_at":"2026-02-14T09:38:57Z","title":"No Need to Train Your RDB Foundation Model","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-02T23:33:32.469224Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2602.13697"},"observation_digest":"sha256:4c566959022dfaf4ff5296cc71585652fa6fe758ffd83080977f08c9ed9f54ba","observation_id":"74ff921b-a274-4b94-a08e-97468029c6bc","resolution":{"observed_at":"2026-08-02T23:33:32.469224Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2602.20223","last_updated":"2026-04-09T07:40:01Z","snapshot_observed_at":"2026-07-06T22:46:49.536946Z","submitted_at":"2026-02-23T13:37:44Z","title":"MultiModalPFN: Extending Prior-Data Fitted Networks for Multimodal Tabular Learning","version":3},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-15T20:11:22.146641Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2602.20223"},"observation_digest":"sha256:bb15fe27e53cd5dbdf62939992f631816e11b03021413c0722e4b74185f7391b","observation_id":"3aef6dfe-c439-4d06-9e6a-eb9641885c15","resolution":{"observed_at":"2026-05-15T20:11:34.268622Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2603.16513","last_updated":"2026-05-20T11:16:01Z","snapshot_observed_at":"2026-07-06T22:49:23.750692Z","submitted_at":"2026-03-17T13:40:39Z","title":"FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-21T10:27:08.416710Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2603.16513"},"observation_digest":"sha256:444545126d9490fea6c84adf2113da651e7f1b496b1178e6962c3c9732d70b1f","observation_id":"3f43283a-4a2e-42f2-94d3-30d6bcdacdd1","resolution":{"observed_at":"2026-05-21T10:30:00.295029Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2603.21236","last_updated":"2026-04-05T02:55:44Z","snapshot_observed_at":"2026-08-06T23:11:01.263934Z","submitted_at":"2026-03-22T13:51:45Z","title":"Posterior-Calibrated Causal Circuits in Variational Autoencoders: Why Image-Domain Interpretability Fails on Tabular Data","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-15T06:56:31.622297Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2603.21236"},"observation_digest":"sha256:2cc0bc4495cc68a13025fcf1d3c069683951fe0d68e5a4d14166ef6bb9a1b71a","observation_id":"2b7093f0-d152-45e4-a396-d6e14b7eb079","resolution":{"observed_at":"2026-05-15T06:59:49.283118Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-07-13T09:21:37.064578Z","title":"Shriyank Somvanshi, Subasish Das, Syed Aaqib Javed, Gian Antariksa, and Ahmed Hossain","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.05634","last_updated":"2026-05-31T12:40:15Z","snapshot_observed_at":"2026-08-03T08:05:07.025714Z","submitted_at":"2026-04-07T09:36:12Z","title":"PECKER: A Precisely Efficient Critical Knowledge Erasure Recipe For Machine Unlearning in Diffusion Models","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-13T09:21:37.064578Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2604.05634"},"observation_digest":"sha256:9abd01727a3a6afa118923731d27067bf7471960455d5ac4bca77a6bc955f123","observation_id":"52ac7b6c-a13c-4d01-ae01-4f236edd151b","resolution":{"observed_at":"2026-07-13T09:21:37.064578Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2604.05635","last_updated":"2026-04-07T09:36:24Z","snapshot_observed_at":"2026-07-06T22:54:16.913706Z","submitted_at":"2026-04-07T09:36:24Z","title":"From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-10T19:44:52.676095Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2604.05635"},"observation_digest":"sha256:e8a844e1f22ed35836e9118c350bcdee861e8a3eed650c172a1a3267702eb942","observation_id":"2b636d6b-d2b4-4d92-bc72-4a9e9c3e61bd","resolution":{"observed_at":"2026-05-10T22:30:54.008697Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2604.05857","last_updated":"2026-04-07T13:18:31Z","snapshot_observed_at":"2026-07-06T22:54:30.567988Z","submitted_at":"2026-04-07T13:18:31Z","title":"Weight-Informed Self-Explaining Clustering for Mixed-Type Tabular Data","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-10T18:50:12.041085Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2604.05857"},"observation_digest":"sha256:9d6b2598794adbea3a5793ea4806d34ddc007fac5aac1b107287001b994e6b73","observation_id":"1110f8f0-ed31-4cce-b4dd-2ca880245a16","resolution":{"observed_at":"2026-05-10T23:50:56.242578Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2604.08649","last_updated":"2026-04-09T18:00:00Z","snapshot_observed_at":"2026-07-06T22:57:40.773778Z","submitted_at":"2026-04-09T18:00:00Z","title":"PRAGMA: Revolut Foundation Model","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-10T17:37:04.381074Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2604.08649"},"observation_digest":"sha256:1db00200aef0ccbf046f26af07ccad8083ac9325fa2226c949646983e38c6e35","observation_id":"111f16c6-d455-4b68-95bc-caa657a7ebdd","resolution":{"observed_at":"2026-05-11T06:30:59.099540Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2604.10337","last_updated":"2026-04-11T19:51:01Z","snapshot_observed_at":"2026-07-31T18:53:57.550208Z","submitted_at":"2026-04-11T19:51:01Z","title":"Integrating SAINT with Tree-Based Models: A Case Study in Employee Attrition Prediction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:15.286694Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2604.10337"},"observation_digest":"sha256:a158e7e03cd155e73c7d2b5dfc930d1dc36488178721e77f47b9fe7267deaa9e","observation_id":"c8ef2a02-7a73-4312-96a6-ea3877b5e8d1","resolution":{"observed_at":"2026-05-11T10:01:01.270696Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2605.04363","last_updated":"2026-05-24T03:30:57Z","snapshot_observed_at":"2026-07-06T23:17:09.246351Z","submitted_at":"2026-05-06T00:01:47Z","title":"Mitigating Label Shift in Tabular In-Context Learning via Test-Time Posterior Adjustment","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-08T18:28:39.171830Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2605.04363"},"observation_digest":"sha256:efab198a9821c6d46aa2db4776088205ef8b9aa0cf38aca2ae9fbdf4ce6f07fd","observation_id":"43155a87-5c6e-4c63-920d-ca0a14b914bb","resolution":{"observed_at":"2026-05-09T06:25:44.722790Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2605.04363","last_updated":"2026-05-24T03:30:57Z","snapshot_observed_at":"2026-07-06T23:17:09.246351Z","submitted_at":"2026-05-06T00:01:47Z","title":"Mitigating Label Shift in Tabular In-Context Learning via Test-Time Posterior Adjustment","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-01T00:30:39.185222Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2605.04363"},"observation_digest":"sha256:cbb41737eaf5b648de024626b8ca156cd4f401180a5390736a1551ba955600aa","observation_id":"4a1c5121-36d0-4a52-8bf7-15c5ee82b82c","resolution":{"observed_at":"2026-07-01T00:35:10.318386Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2605.16085","last_updated":"2026-05-15T15:46:38Z","snapshot_observed_at":"2026-07-06T23:27:15.745896Z","submitted_at":"2026-05-15T15:46:38Z","title":"Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-19T18:38:03.078374Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2605.16085"},"observation_digest":"sha256:34b65dea766cd13a0c368d7c2b9ebd4ff349b04925ab144aed31b6df71485e78","observation_id":"c9dff112-928b-44bc-8054-b304a95a0689","resolution":{"observed_at":"2026-05-19T18:42:43.724488Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2605.18147","last_updated":"2026-07-15T09:43:13Z","snapshot_observed_at":"2026-08-09T15:34:35.266282Z","submitted_at":"2026-05-18T09:52:48Z","title":"Foundation Models for Credit Risk Prediction: A Game Changer?","version":1},"reference_index":159,"source":"arxiv_source","source_observed_at":"2026-05-20T13:17:44.504335Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2605.18147"},"observation_digest":"sha256:ea4a60b693d00beb4ad4869ae5b9785b9701ec3ebce66b9ceb8c705a14e29344","observation_id":"794cadc7-cc7d-400b-9690-d988d9bc6555","resolution":{"observed_at":"2026-05-20T13:18:18.246633Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-02T13:47:25.044109Z","title":"Bayan Bruss, and Tom Goldstein","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.18147","last_updated":"2026-07-15T09:43:13Z","snapshot_observed_at":"2026-08-09T15:34:35.266282Z","submitted_at":"2026-05-18T09:52:48Z","title":"Foundation Models for Credit Risk Prediction: A Game Changer?","version":2},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-08-02T13:47:25.044109Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2605.18147"},"observation_digest":"sha256:ae058f1be7222735013ca5d7fe3cd086c575bd02130ac45c08e66ce59a313547","observation_id":"b865807b-90d0-4c9f-a99f-c9872e94b6b5","resolution":{"observed_at":"2026-08-02T13:47:25.044109Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2605.19014","last_updated":"2026-05-18T18:35:20Z","snapshot_observed_at":"2026-08-04T00:34:50.255323Z","submitted_at":"2026-05-18T18:35:20Z","title":"SAGA: A Sequence-Adaptive Generative Architecture for Multi-Horizon Probabilistic Forecasting with Adaptive Temporal Conformal Prediction","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-20T12:19:53.531780Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2605.19014"},"observation_digest":"sha256:314bca95eaa223ba58888f03debd779156bde45b835adf6827638c27aeff4ddf","observation_id":"d926331b-9bc2-4f9d-b23c-7da59db0f5d1","resolution":{"observed_at":"2026-05-20T12:23:16.996607Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2605.20234","last_updated":"2026-05-16T15:02:03Z","snapshot_observed_at":"2026-08-06T21:57:26.548917Z","submitted_at":"2026-05-16T15:02:03Z","title":"TabPFN-MT: A Natively Multitask In-Context Learner for Tabular Data","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-21T07:40:47.466415Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2605.20234"},"observation_digest":"sha256:1860b3131b50b1b05d1c503b0ae32c5c4d513b0a844129679aa9aa71defbabaa","observation_id":"36e93974-3e91-4d64-97b0-e6c39f03fefa","resolution":{"observed_at":"2026-05-21T07:44:02.640738Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2605.23241","last_updated":"2026-05-22T05:19:58Z","snapshot_observed_at":"2026-07-06T23:33:29.550551Z","submitted_at":"2026-05-22T05:19:58Z","title":"RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-25T05:05:04.461460Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2605.23241"},"observation_digest":"sha256:4aa2b6fcecca152408bed74b4bd115781113f25d96bac4bfd0fe8495481a19d7","observation_id":"8bf0473c-e909-4088-8eac-4635773f2aef","resolution":{"observed_at":"2026-05-25T05:05:22.270223Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2606.01189","last_updated":"2026-05-31T12:11:47Z","snapshot_observed_at":"2026-08-06T00:50:49.461826Z","submitted_at":"2026-05-31T12:11:47Z","title":"The Case for Model Science: Verify, Explore, Steer, Refine","version":1},"reference_index":253,"source":"arxiv_source","source_observed_at":"2026-06-28T17:24:32.311565Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2606.01189"},"observation_digest":"sha256:943fb2703666e1025b0914ec93e778dfac1319d3f02efbd15e5281e0ae8b9d2e","observation_id":"75a97833-7c1d-4093-8819-d28ef7c1832e","resolution":{"observed_at":"2026-07-01T21:16:13.560939Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2606.04445","last_updated":"2026-06-03T04:47:38Z","snapshot_observed_at":"2026-08-07T18:10:08.903944Z","submitted_at":"2026-06-03T04:47:38Z","title":"RowNet: A Memory Transformer for Tabular Regression","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-28T07:16:34.310531Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2606.04445"},"observation_digest":"sha256:c137c5710fa89a9c83c4dfb43c79d83fffa6ec841b6f87a4b5902da4f5fc6b0b","observation_id":"15e4447a-44f8-48e0-b1d5-38550a9258a8","resolution":{"observed_at":"2026-07-02T06:56:44.722766Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":"2106.01342","doi":"10.48550/arxiv.2106.01342","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training.arXiv preprint arXiv:2106.01342","venue":"arXiv (Cornell University)","work_id":"8d2897d2-d597-488d-955c-18aadee49615","year":2021},"citing_paper":{"arxiv_id":"2606.09323","last_updated":"2026-06-08T10:39:25Z","snapshot_observed_at":"2026-08-06T04:56:32.773827Z","submitted_at":"2026-06-08T10:39:25Z","title":"TRL-Bench: Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-06-27T16:48:38.820334Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2606.09323"},"observation_digest":"sha256:748a247c49608e65680196e70a295a71f25405e806405523e0d0043c23ff5fc6","observation_id":"968fd8b0-8aab-475d-b61f-4d6e928bd6d6","resolution":{"observed_at":"2026-07-03T01:07:30.392514Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T18:54:15.710132+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-07-13T05:50:23.546639Z","title":"SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.08915","last_updated":"2026-07-09T20:14:39Z","snapshot_observed_at":"2026-08-06T22:30:58.273660Z","submitted_at":"2026-07-09T20:14:39Z","title":"Pattern-Aware Graph Neural Networks for Handling Missing Data","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-13T05:50:23.546639Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2607.08915"},"observation_digest":"sha256:3e760e72f92ef1828ddeeaf4c04836f15afa56905d047b2e8c977bdfa970c14a","observation_id":"d5abf0ba-7f2d-40f6-a5b9-d03d17877b0b","resolution":{"observed_at":"2026-07-13T05:50:23.546639Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-07-14T00:36:05.289408Z","title":"Somepalli, M","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.10077","last_updated":"2026-07-11T02:07:53Z","snapshot_observed_at":"2026-08-06T14:08:24.481355Z","submitted_at":"2026-07-11T02:07:53Z","title":"TabLoRA: Parameter-Efficient Low-Rank Ensemble Learning for Large-Scale Tabular Data","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-14T00:36:05.289408Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2607.10077"},"observation_digest":"sha256:dfaf68b9a6848f1fa031fff8fb5d653271766583d78076d78e035f925a9b060e","observation_id":"adcb6f53-0945-4263-a883-69ce0c5fa838","resolution":{"observed_at":"2026-07-14T00:36:05.289408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-02T05:18:46.407357Z","title":"arXiv preprint arXiv:2106.01342 (2021),https://arxiv.org/abs/2106","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.13413","last_updated":"2026-07-15T03:30:46Z","snapshot_observed_at":"2026-08-07T08:27:43.134547Z","submitted_at":"2026-07-15T03:30:46Z","title":"Is the Statistical Advantage Worth the Cost? An Empirical Comparison of KANs and MLPs for Structured Data Classification","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-02T05:18:46.407357Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2607.13413"},"observation_digest":"sha256:b9c8fd49c526974c9aa19e27cdc333b5640595a94576ae57d4a9cf21a00973ad","observation_id":"d6cd63a1-0ffa-4ee3-b5d5-6cc64fa5a838","resolution":{"observed_at":"2026-08-02T05:18:46.407357Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-01T00:54:47.346496Z","title":"arXiv preprint arXiv:2106.01342 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.26000","last_updated":"2026-07-28T17:16:01Z","snapshot_observed_at":"2026-08-05T21:57:16.615125Z","submitted_at":"2026-07-28T17:16:01Z","title":"Empirical Evaluation of Out-Of-Distribution Performance of Tabular Foundation Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T00:54:47.346496Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2607.26000"},"observation_digest":"sha256:e9d6fe18e1551ebe8e917aa1346e2d418d9cdb1a10f8dadb04086cb6de17c18c","observation_id":"dbb75207-ff8c-42cc-be28-07974d0e5287","resolution":{"observed_at":"2026-08-01T00:54:47.346496Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-07-30T15:56:46.035459Z","title":"arXiv preprint arXiv:2106.01342 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26955","last_updated":"2026-07-29T14:21:39Z","snapshot_observed_at":"2026-08-06T21:10:17.072894Z","submitted_at":"2026-07-29T14:21:39Z","title":"Breaking the Curse with BAND: Nonparametric Distribution Estimation in High Dimensions","version":1},"reference_index":156,"source":"arxiv_source","source_observed_at":"2026-07-30T15:56:46.035459Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2607.26955"},"observation_digest":"sha256:b9a4fc8380dfa749aeead58928532a13f692209f31535fd13b1923cf61857e1e","observation_id":"697abd10-2892-4644-922a-192bce44ee4a","resolution":{"observed_at":"2026-07-30T15:56:46.035459Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-01T09:03:19.680449Z","title":"https://doi.org/10.48550/arXiv.2106.01342","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.27350","last_updated":"2026-07-29T18:08:07Z","snapshot_observed_at":"2026-08-07T20:20:36.560134Z","submitted_at":"2026-07-29T18:08:07Z","title":"Modeling Decisions in Blockchain Analytics: A Leakage-Aware Evaluation of Tree-Based vs. Sequential Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T09:03:19.680449Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2607.27350"},"observation_digest":"sha256:e91f3c54123c273b36daea77e43bcaa5af72141ad53b6d155fc3db3a76b1c59d","observation_id":"a3babd14-86e5-468b-9bdf-05c805d7abe9","resolution":{"observed_at":"2026-08-01T09:03:19.680449Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-06T21:03:59.336455Z","title":"B., & Goldstein, T","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.04602","last_updated":"2026-08-05T09:06:45Z","snapshot_observed_at":"2026-08-08T23:11:59.225789Z","submitted_at":"2026-08-05T09:06:45Z","title":"Adaptive Intrusion Detection System using Transformer-Based Neural Networks and Continual Learning Approach with Adversarial Investigation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T21:03:59.336455Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2608.04602"},"observation_digest":"sha256:84305ed4bb4243d7cfe12118f2fcaf1cb2a1fcd9662e952119f2b9b2b2aa1646","observation_id":"1d5ab4a1-96b5-496d-a1f9-0aca8ba942f2","resolution":{"observed_at":"2026-08-06T21:03:59.336455Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2106.01342/citation-record","integrity":"/paper/2106.01342/integrity","json":"/paper/2106.01342/citation-record.json","paper":"/paper/2106.01342"},"outbound":[],"paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 44 inbound Pith citation observations for arXiv:2106.01342."}