{"as_of":"2026-08-09T02:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:040da8ca0e69c14b91582a926e4d7613e173511f453abca2d60d2bf2b74a30ae","coverage":[{"denominator":89,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":89,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T22:21:08.094164Z","state":"measured"},{"denominator":92,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":92,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-14T14:29:04.708853Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-01T14:35:46.884992Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","submitted_at":"2025-02-06T23:58:11Z","title":"Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer","version":2},"cited_work":{"arxiv_id":"2502.04573","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.04573","snapshot_observed_at":"2026-07-01T14:35:46.884992Z","title":"Homological neural networks: A sparse architecture for multivariate complex- ity","venue":null,"work_id":"2bae17c8-42bd-441c-bb3b-7cf7fb3dd3b5","year":2023},"citing_paper":{"arxiv_id":"2506.02978","last_updated":"2026-04-09T13:55:31Z","snapshot_observed_at":"2026-08-07T19:00:32.881703Z","submitted_at":"2025-06-03T15:15:36Z","title":"On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-19T10:50:50.333945Z"},"links":{"cited_paper":"/paper/2502.04573","citing_paper":"/paper/2506.02978"},"observation_digest":"sha256:8ff17774066fa5b91b5e2e1f3a9e28c96db734a103cdc8292dc445ee9cbeecc8","observation_id":"d36c0b2d-8c82-4454-b8c7-40d43d489895","resolution":{"observed_at":"2026-05-19T10:52:15.040464Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","submitted_at":"2025-02-06T23:58:11Z","title":"Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer","version":2},"cited_work":{"arxiv_id":"2502.04573","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.04573","snapshot_observed_at":"2026-07-01T14:35:46.884992Z","title":"Homological neural networks: A sparse architecture for multivariate complex- ity","venue":null,"work_id":"2bae17c8-42bd-441c-bb3b-7cf7fb3dd3b5","year":2023},"citing_paper":{"arxiv_id":"2605.14764","last_updated":"2026-05-14T12:26:50Z","snapshot_observed_at":"2026-08-02T09:09:19.474755Z","submitted_at":"2026-05-14T12:26:50Z","title":"Compositional Sparsity as an Inductive Bias for Neural Architecture Design","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-30T20:55:03.949611Z"},"links":{"cited_paper":"/paper/2502.04573","citing_paper":"/paper/2605.14764"},"observation_digest":"sha256:9ff775ddb856ad22b1bb079e172e379dfee549a53536f33a6de4413308bd59ae","observation_id":"9d506eb7-558b-4106-8761-535446003240","resolution":{"observed_at":"2026-07-01T14:35:46.886653Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","submitted_at":"2025-02-06T23:58:11Z","title":"Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.04573","snapshot_observed_at":"2026-07-14T14:29:04.708853Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09936","last_updated":"2026-07-10T19:33:47Z","snapshot_observed_at":"2026-08-04T16:12:15.629284Z","submitted_at":"2026-07-10T19:33:47Z","title":"SMETA-ZSL:Semantic Meta-Alignment for Zero-Shot Threat Classification","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-14T14:29:04.708853Z"},"links":{"cited_paper":"/paper/2502.04573","citing_paper":"/paper/2607.09936"},"observation_digest":"sha256:43b3a246b30082cb229e0da2d6f4a8382e6a3de7eb308f772d93066e0cc7525d","observation_id":"ece81874-52f9-449f-85c5-932e3166466c","resolution":{"observed_at":"2026-07-14T14:29:04.708853Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.04573/citation-record","integrity":"/paper/2502.04573/integrity","json":"/paper/2502.04573/citation-record.json","paper":"/paper/2502.04573"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.765768Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":1,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.765768Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:65412c16713a96feeb2cfddd70ea7f3ba45728ca004af961e348608ede5c6aca","observation_id":"1eccd6ad-081b-4d85-91f8-72e0371b160f","resolution":{"observed_at":"2026-08-08T22:21:07.765768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.770964Z","title":"and Devanbu, P","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":2,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.770964Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:becc24bf3447ef8bdcf02bd1e5e1c783233780c6e26661d0af2981f64befdd9f","observation_id":"595d2a11-5b12-4c5e-bf63-c060503cc281","resolution":{"observed_at":"2026-08-08T22:21:07.770964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.776880Z","title":"and Flammarion, N","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":3,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.776880Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:3e72a1ed720a3117ae56f364938c8f2d49a065a1d1479ae5ee8e66b0102dd0a3","observation_id":"488f1f9b-b83a-4499-a202-4da4bfd8a787","resolution":{"observed_at":"2026-08-08T22:21:07.776880Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.781135Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":4,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.781135Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:17098c71d247687b75f422a7ee08b7490b39fa77fc8b18556a9546095e4af46c","observation_id":"8a68dcca-9d81-4c9c-be3a-ce39874cb4d1","resolution":{"observed_at":"2026-08-08T22:21:07.781135Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.785313Z","title":"G., van Rijn, J","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":5,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.785313Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:d7ff196c9ee9cf235d466a28d4ade3f8cca5f8321fc4861f18545327317024bd","observation_id":"65556a87-ec9e-4748-b097-55a009e2bd09","resolution":{"observed_at":"2026-08-08T22:21:07.785313Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.789222Z","title":"Deep neural networks and tabular data: A survey","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":6,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.789222Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:5dc283f0bad33331c02eda4e95839bc4a47678a41c7dd7bd01c05d1734f25a8d","observation_id":"e8c1309e-edab-4ac2-95ba-39b859fbdd85","resolution":{"observed_at":"2026-08-08T22:21:07.789222Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.793088Z","title":"Language models are realistic tabular data generators","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":7,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.793088Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:b925347eae65ef11e45d26ca834e569ad26a73b8f12e0cdb610c655811813de6","observation_id":"a74f741c-3182-42da-8f87-28d28be8155e","resolution":{"observed_at":"2026-08-08T22:21:07.793088Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.11062","last_updated":"2024-02-06T10:16:54Z","snapshot_observed_at":"2026-07-06T15:18:26.300068Z","submitted_at":"2023-04-19T16:18:54Z","title":"Scaling Transformer to 1M tokens and beyond with RMT","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.11062","snapshot_observed_at":"2026-08-08T22:21:07.797144Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":8,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.797144Z"},"links":{"cited_paper":"/paper/2304.11062","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:851e068786362f86fc25a85b716268a4636cde262ebed54fa5d5892b2ad02108","observation_id":"ddd33a6b-8d60-44c0-92be-2c1a2ba7c93d","resolution":{"observed_at":"2026-08-08T22:21:07.797144Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.16512","last_updated":"2024-06-25T11:54:23Z","snapshot_observed_at":"2026-08-05T17:51:43.897230Z","submitted_at":"2024-03-25T07:55:29Z","title":"LLMs Are Few-Shot In-Context Low-Resource Language Learners","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.16512","snapshot_observed_at":"2026-08-08T22:21:07.801393Z","title":"Llms are few-shot in-context low-resource language learners","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":9,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.801393Z"},"links":{"cited_paper":"/paper/2403.16512","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:00d6315767f352448038c489a91591bf4c01df9709376e8304bd6f46ec4afcd4","observation_id":"07bcca3c-1be0-4d49-8c92-bee0c6944207","resolution":{"observed_at":"2026-08-08T22:21:07.801393Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.805379Z","title":"Importance of semantic representation: Dataless classification","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":10,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.805379Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:8b0398a1d1539927ff825cc9514281bbe4b16d12b1a9d8e53aee3c7581c0f635","observation_id":"6b531858-b452-4f2a-ac16-b8ba3310f7d3","resolution":{"observed_at":"2026-08-08T22:21:07.805379Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.679815Z","title":"Z., Wu, J., and Sun, J","venue":null,"work_id":"e3082305-2d34-455a-8064-5c746d62f1c5","year":2024},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":11,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.809007Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:40a8176033ad91b2c6d6b3099bec8fa963c0be443c97bf3453c104153452c7ad","observation_id":"44f12203-35e5-43dd-9e9b-e6fe5886c5d5","resolution":{"observed_at":"2026-08-08T22:21:08.683860Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.812795Z","title":"and Guestrin, C","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":12,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.812795Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:e0350742c6377e397d0921731793f4b435e5b6b27a7b1f7267585daf1bb57f36","observation_id":"983929e8-b302-454c-9af8-a163fec48322","resolution":{"observed_at":"2026-08-08T22:21:07.812795Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:09.259815Z","title":"Notes from the ai frontier: Insights from hundreds of use cases","venue":null,"work_id":"9e21c95f-e498-475c-a03c-b2e2d33e85e8","year":2018},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":13,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.816303Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:dfba58c422985bcef2d3a97a777eb2143567e7695fe79e4218adf4b6b2b87979","observation_id":"83c89287-631b-4542-a06d-dc280e29fe55","resolution":{"observed_at":"2026-08-08T22:21:09.263623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.819710Z","title":"Support-vector networks","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":14,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.819710Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:3633e3714668a75a59e81eb990b0dbb92e2c4d5c03b2c49fbf18651f512beb96","observation_id":"cf86d40a-c9fb-489b-8d08-207b11f517aa","resolution":{"observed_at":"2026-08-08T22:21:07.819710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.823234Z","title":"and Hart, P","venue":null,"work_id":null,"year":1967},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":15,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.823234Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:e3b269aa981279a3e2ef2b02832375e5f3be7d0014c135a186cb08d50cea7ca6","observation_id":"cf34445f-af38-4fa0-af48-1b2db3d197cf","resolution":{"observed_at":"2026-08-08T22:21:07.823234Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-08T22:21:07.826800Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":16,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.826800Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:9b1966a730ffbd2d6787b11644e629e78f09c4430d24ab10eb73f2d193cae3b3","observation_id":"8a9a0192-ba55-4b67-987a-a371bdf48218","resolution":{"observed_at":"2026-08-08T22:21:07.826800Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:09.235288Z","title":"Efficient and robust automated machine learning","venue":null,"work_id":"ecfa2df1-6c8b-48d8-b4d3-d5aa5e0e9d49","year":2015},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":17,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.830736Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:ed311d862287cb264e0578b4105fbc4e903de5413b61e562662632066b4c1e98","observation_id":"dcebbdc3-84ad-4458-ae20-6c2c55ba2a5d","resolution":{"observed_at":"2026-08-08T22:21:09.239075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:09.224136Z","title":"Auto-sklearn 2.0: Hands-free automl via meta-learning","venue":null,"work_id":"066cbf42-5e52-4daa-b03f-11d3432069df","year":2022},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":18,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.834598Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:c24eea9abf51d5dbc172ff1aa1475f6f7c3962810e884749378eb480e8840de6","observation_id":"eb502ad4-8f21-4ebf-ae2f-12b0b645f529","resolution":{"observed_at":"2026-08-08T22:21:09.227911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.837890Z","title":"Model-agnostic meta-learning for fast adaptation of deep networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":19,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.837890Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:b411f2d9e2f8fa68915d0835df8acbfd4837b81a6143ebe34f8dbee4a9373890","observation_id":"ef3e843b-870d-4a82-a5e9-e1e5c60c81f1","resolution":{"observed_at":"2026-08-08T22:21:07.837890Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:09.205527Z","title":"F., Feurer, M., and Bischl, B","venue":null,"work_id":"31a77171-60d6-4b47-a85b-4b8989f7417a","year":2023},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":20,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.841485Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:b7aa217daae0f6e7e80c3af001e9da74ce1531b9e2ab17cc47a78e104833fff3","observation_id":"46c9c9ea-dd72-4828-afdd-807861e76b5d","resolution":{"observed_at":"2026-08-08T22:21:09.209398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.06435","last_updated":"2021-03-11T03:45:43Z","snapshot_observed_at":"2026-07-06T10:48:49.181349Z","submitted_at":"2021-03-11T03:45:43Z","title":"Population-Based Evolution Optimizes a Meta-Learning Objective","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.06435","snapshot_observed_at":"2026-08-08T22:21:07.845118Z","title":"and Witkowski, O","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":21,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.845118Z"},"links":{"cited_paper":"/paper/2103.06435","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:622bd38f12eec8d79e22fb38e3168e8806000f5e5191b1f96eff691343210187","observation_id":"6a817304-24e2-43ad-a617-0f5f8966a3ec","resolution":{"observed_at":"2026-08-08T22:21:07.845118Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.12031","last_updated":"2024-11-20T21:20:08Z","snapshot_observed_at":"2026-07-06T18:32:32.228906Z","submitted_at":"2024-06-17T18:58:20Z","title":"Large Scale Transfer Learning for Tabular Data via Language Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.12031","snapshot_observed_at":"2026-08-08T22:21:07.849026Z","title":"C., and Schmidt, L","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":22,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.849026Z"},"links":{"cited_paper":"/paper/2406.12031","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:4446ef95a1a85877a298c09b6815faf40891e6d998bc2936b5af5745bd68b03a","observation_id":"f8ada0c8-d3fa-4fc8-92df-91babe9ed2f4","resolution":{"observed_at":"2026-08-08T22:21:07.849026Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:09.193832Z","title":"Meta-learning reduces the amount of data needed to build ai models in oncology","venue":null,"work_id":"88afd282-c5a9-4a03-9459-3cf1447d08aa","year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":23,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.852969Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:d90a0ae815cfb2b671806f20c6210acc035b5ad4621bf0da71f895ff535dd0e2","observation_id":"a565ecc4-64ee-422e-a6ab-ce45a5b37289","resolution":{"observed_at":"2026-08-08T22:21:09.197678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.856597Z","title":"Generative adversarial nets","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":24,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.856597Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:a17a76494b9ba5c3a36deb6dd562a8670a015623ec3f3380ed132c38c228de03","observation_id":"f82efcce-f5e5-4eaa-bfe1-f90339f7dd52","resolution":{"observed_at":"2026-08-08T22:21:07.856597Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6572","last_updated":"2015-03-20T20:19:16Z","snapshot_observed_at":"2026-07-06T04:04:16.777653Z","submitted_at":"2014-12-20T01:17:12Z","title":"Explaining and Harnessing Adversarial Examples","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6572","snapshot_observed_at":"2026-08-08T22:21:07.860315Z","title":"Explaining and harnessing adversarial examples","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":25,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.860315Z"},"links":{"cited_paper":"/paper/1412.6572","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:14e049ff4b5ca6d5a9aefed2066b92082c870caf3e779965aa9c36cc7594d59d","observation_id":"afea3fed-91fa-416f-8d92-7c7447b6bd5a","resolution":{"observed_at":"2026-08-08T22:21:07.860315Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.864784Z","title":"Revisiting deep learning models for tabular data","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":26,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.864784Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:8efa2d66f0ce91dec1e7b75f5a47b55d6d6bbb902180483c7edf2aba289c5250","observation_id":"afbccb73-dcf0-440b-b1d6-2aeb6c02677a","resolution":{"observed_at":"2026-08-08T22:21:07.864784Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:09.169308Z","title":"On embeddings for numerical features in tabular deep learning","venue":null,"work_id":"f30f7519-1634-4ba5-b140-95fb21770382","year":2022},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":27,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.868537Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:a7a2efc360e5413966802e9d2241b64347e8d1c433d033a65829e386ef6e32dc","observation_id":"e5e8d4e9-6c58-4472-ab63-3ee3fb0bfe7c","resolution":{"observed_at":"2026-08-08T22:21:09.173021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:09.157709Z","title":"Tabr: Tabular deep learning meets nearest neighbors","venue":null,"work_id":"a83aa90f-e37b-4afc-9d23-f1ee0aa41d75","year":2024},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":28,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.872149Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:d40b8a0183f752f3e63cfc6555c33368ab7f2a70f6a6fa47127a07f82c209146","observation_id":"bfdb4c53-25ed-4529-873c-afd5806adc5c","resolution":{"observed_at":"2026-08-08T22:21:09.161598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.875718Z","title":"Why do tree-based models still outperform deep learning on typical tabular data? Advances in neural information processing systems, 35: 0 507--520, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":29,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.875718Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:29eb339f52bf54e23c4307bd27faa97c4823b5b7b31fba9b7f1f514842e08897","observation_id":"1ad1ce3f-7d99-4cc1-a0b6-7c3cde64e5ad","resolution":{"observed_at":"2026-08-08T22:21:07.875718Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:09.139683Z","title":"Tabllm: Few-shot classification of tabular data with large language models","venue":null,"work_id":"ec589469-d4dc-40fd-a091-8fb7c9f2b413","year":2023},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":30,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.879281Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:589ec8d1affc6b6bc0fdafc59f0000767a25a63a554d330d5da45c35fdafc8b1","observation_id":"19d9936b-316b-4642-8979-c719334780dc","resolution":{"observed_at":"2026-08-08T22:21:09.143429Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:09.128776Z","title":"Drift-resilient tab PFN : In-context learning distribution shifts on tabular data","venue":null,"work_id":"77164092-7ca8-4b65-b8b2-406ae3dc08da","year":2024},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":31,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.883072Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:2109fdaed8c9e6aa01c71ef98b48ed9a39ce6b36a3946dcf7873b8139f98f554","observation_id":"4ff296d0-53f4-4e4b-927b-8c6cc95082f6","resolution":{"observed_at":"2026-08-08T22:21:09.132722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:09.118041Z","title":null,"venue":null,"work_id":"700831ce-2395-4e04-8a65-fa72bc4666ac","year":1995},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":32,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.886680Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:41f3c5c92f04c9c46174675ed223103aac89ec1796a6fe12869f9bbf23043f5f","observation_id":"4cf7cd43-51ab-484c-9468-b2ab57e8ad1a","resolution":{"observed_at":"2026-08-08T22:21:09.121607Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_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},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.01848","snapshot_observed_at":"2026-08-08T22:21:07.890222Z","title":"Tabpfn: A transformer that solves small tabular classification problems in a second","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":33,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.890222Z"},"links":{"cited_paper":"/paper/2207.01848","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:d5c82270cc6ccddfc240b2249bf7bf3ad6a69dbd5f08186b2c0421e4ca908963","observation_id":"f16e54f3-9516-496b-9a35-fcd45879df85","resolution":{"observed_at":"2026-08-08T22:21:07.890222Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.894361Z","title":"u ller, S., Purucker, L., Krishnakumar, A., K \\","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":34,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.894361Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:cb34f3bc9a45ded38c4e5720020d1a48a6f1d1a77435f9b1d8cafd4561bbd6dc","observation_id":"f43fe688-3dab-46e1-a36b-8521cb8f9fde","resolution":{"observed_at":"2026-08-08T22:21:07.894361Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.897948Z","title":"Multilayer feedforward networks are universal approximators","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":35,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.897948Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:378dda9f4c043f81c28d254fde5688ee3aa1d0f917ef984d0e1205cdb8ed74b8","observation_id":"9ea5c38c-e902-4068-bad2-ff1194edd0cd","resolution":{"observed_at":"2026-08-08T22:21:07.897948Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.901604Z","title":"Meta-learning in neural networks: A survey","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":36,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.901604Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:733b1f17cecc8cf65f551285fbea4b752fd73f0db62a621b9d787c9ffa48e4de","observation_id":"eff18c4c-e49f-4f1e-b459-ea9eadd56837","resolution":{"observed_at":"2026-08-08T22:21:07.901604Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.06678","last_updated":"2020-12-11T23:31:23Z","snapshot_observed_at":"2026-07-06T10:23:43.246202Z","submitted_at":"2020-12-11T23:31:23Z","title":"TabTransformer: Tabular Data Modeling Using Contextual Embeddings","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.06678","snapshot_observed_at":"2026-08-08T22:21:07.905128Z","title":"Tabtransformer: Tabular data modeling using contextual embeddings, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":37,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.905128Z"},"links":{"cited_paper":"/paper/2012.06678","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:e40d8696f162b15081715157019c83a3108591e355e7393b3871b98bbadb67d2","observation_id":"6feeafd9-478b-4605-8959-3f191716cf11","resolution":{"observed_at":"2026-08-08T22:21:07.905128Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:09.086822Z","title":"N., and Plaat, A","venue":null,"work_id":"dc908a7b-8f7d-408f-a81e-cd0a06f2b54d","year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":38,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.908856Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:e34784da84b1e3b8a59d2d1d83797537b03c3b4a6f8560f8307c6acc07cb11a1","observation_id":"7b547981-ba55-4eda-a81c-3aed16e35c01","resolution":{"observed_at":"2026-08-08T22:21:09.090585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1502.03167","last_updated":"2015-03-02T20:44:12Z","snapshot_observed_at":"2026-07-06T04:08:54.419941Z","submitted_at":"2015-02-11T01:44:18Z","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1502.03167","snapshot_observed_at":"2026-08-08T22:21:07.912402Z","title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":39,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.912402Z"},"links":{"cited_paper":"/paper/1502.03167","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:99df5ffbbb2bfb5ddc57c99fd59ba95a0027f3e1f4431a2fd6d9ef61904c85a7","observation_id":"b4da30f8-8987-4f13-940f-de1aa3fe9ea9","resolution":{"observed_at":"2026-08-08T22:21:07.912402Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:09.075640Z","title":"A survey on generative adversarial networks: Variants, applications, and training","venue":null,"work_id":"922253db-cd0f-4c7a-8ab6-680b9afc07b4","year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":40,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.916036Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:2a857d7520fe5bc1886536a54e2a16922dda342312cb5de95aec29ab37c76ef7","observation_id":"06fcf2e9-768e-43fc-9157-e5bd57da70cb","resolution":{"observed_at":"2026-08-08T22:21:09.079469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1611.01144","last_updated":"2017-08-05T22:45:19Z","snapshot_observed_at":"2026-08-01T18:34:23.156273Z","submitted_at":"2016-11-03T19:48:08Z","title":"Categorical Reparameterization with Gumbel-Softmax","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.01144","snapshot_observed_at":"2026-08-08T22:21:07.919384Z","title":"Categorical reparameterization with gumbel-softmax","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":41,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.919384Z"},"links":{"cited_paper":"/paper/1611.01144","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:35ee0e1d11e8c0e2df9e50fddddd089e60a873ff057d447a7b205eadfdd50ba4","observation_id":"8991a3ed-b1fe-4dbc-8bb8-69e8150fb3b8","resolution":{"observed_at":"2026-08-08T22:21:07.919384Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:09.063880Z","title":"Well-tuned simple nets excel on tabular datasets","venue":null,"work_id":"e01c5c97-abf4-45e9-9016-4392d8ecb31a","year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":42,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.923629Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:8196acbab0ed28e116fd633d0524a47ef5aa3bc276706f88604617446fca799b","observation_id":"f2f1a79d-8324-433d-8f7a-710c4cec2f94","resolution":{"observed_at":"2026-08-08T22:21:09.067826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:09.052386Z","title":"Well-tuned simple nets excel on tabular datasets","venue":null,"work_id":"658cf193-67c1-42aa-9028-7bd0db29bb74","year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":43,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.926987Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:4ce1c5af56967fb065cef030c177d3902e5057eee589f5c75193679663fa493b","observation_id":"9041dc83-c54e-4fb7-9ee3-4b378cbf6c83","resolution":{"observed_at":"2026-08-08T22:21:09.056130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.930557Z","title":"Lightgbm: A highly efficient gradient boosting decision tree","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":44,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.930557Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:77babe22defa99c5f1a9626f02010fe25bdb9f8b7a2314ff936a093d34736172","observation_id":"f2fe7548-f043-4f75-ad90-2130cdbab66e","resolution":{"observed_at":"2026-08-08T22:21:07.930557Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:09.033325Z","title":"Understanding catastrophic overfitting in single-step adversarial training","venue":null,"work_id":"bb83da81-e780-41b5-906b-ff56ee411423","year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":45,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.933984Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:8006e0aec1f2c356bc13da60b647c2f936e8d800c11b521ea4c4923cd1cfe135","observation_id":"9bccf9d2-cb82-4de4-89c4-97f12982a4f7","resolution":{"observed_at":"2026-08-08T22:21:09.037474Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:09.021965Z","title":"J., Grinsztajn, L., and Varoquaux, G","venue":null,"work_id":"8386a300-c07b-4b67-a7aa-065e63e3af53","year":2024},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":46,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.937438Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:eceabd8a1dc1f0fd99f597b7165d5042457f3f10be945525b3e9db8e3f3c0bf2","observation_id":"eaff1ebb-bb44-4a7a-9e0f-246808884a23","resolution":{"observed_at":"2026-08-08T22:21:09.025927Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:09.010536Z","title":"Tab DDPM : Modelling tabular data with diffusion models, 2023","venue":null,"work_id":"9ad6b2b1-1f78-4e9f-9a38-dfcf19332e4a","year":2023},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":47,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.940847Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:162243f95416c14fdf1f20a26788f25ed5896c0431b191098a4951e18cef2bf7","observation_id":"05485457-5608-4dd6-b7e0-ddac13248f17","resolution":{"observed_at":"2026-08-08T22:21:09.014490Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.999115Z","title":"J., and Bengio, S","venue":null,"work_id":"ff5e48c7-e867-4ef9-9c1d-6172e45250cb","year":2017},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":48,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.944263Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:0ffc810de53a52d12793b0564451775599f463f808bf5f2e84f42ce76104c7b2","observation_id":"f52a11c1-3ba4-45be-9776-7e52b8ba3240","resolution":{"observed_at":"2026-08-08T22:21:09.002767Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.987208Z","title":"Zero-data learning of new tasks","venue":null,"work_id":"d6a8f667-6eb9-4754-bb56-b426fec24b40","year":2008},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":49,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.947550Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:dc913d5d606f2268f0fbcaa1d58c6b47b49d878f8e70501589a8aed038afde70","observation_id":"67614edd-cece-4abd-8fd2-7e9d9366bdd4","resolution":{"observed_at":"2026-08-08T22:21:08.991272Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.974770Z","title":"Metalearning: a survey of trends and technologies","venue":null,"work_id":"0a3439ae-7648-4a5d-9b9b-afacd4da1f02","year":2015},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":50,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.951172Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:6ee5f7e1703f04c734c46ae30efd214710c2e4a51a201316f9b45d276b1cc157","observation_id":"41b4e13a-17a2-43d9-a78b-319ec626b0bb","resolution":{"observed_at":"2026-08-08T22:21:08.979009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.15306","last_updated":"2023-08-07T04:07:06Z","snapshot_observed_at":"2026-08-05T17:33:50.898069Z","submitted_at":"2022-06-30T14:24:32Z","title":"Transfer Learning with Deep Tabular Models","version":2},"cited_work":{"arxiv_id":"2206.15306","doi":null,"metadata_source":"pith","pith_arxiv_id":"2206.15306","snapshot_observed_at":"2026-08-08T22:21:08.436405Z","title":"Transfer Learning with Deep Tabular Models","venue":"cs.LG","work_id":"787e68b1-aff3-4cc8-867f-a8d1247a33a0","year":2022},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":51,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.954484Z"},"links":{"cited_paper":"/paper/2206.15306","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:6eaf4ef3e2c9733be68864ce0f5eeefa94c11a34a4fe5a680f7b6106bc30748e","observation_id":"56c65db6-696a-4a09-8277-137515728949","resolution":{"observed_at":"2026-08-08T22:21:08.442251Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.963593Z","title":"Neural architecture optimization","venue":null,"work_id":"e11c4927-967b-4dd2-8512-08ea85e67616","year":2018},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":52,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.958087Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:c3de80f0c7451372fa07ee2b3dfb753297ce68aee1c9b229ff33f878261134b2","observation_id":"72659559-85e3-4fa5-a662-5c5d45521b7d","resolution":{"observed_at":"2026-08-08T22:21:08.967429Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1611.00712","last_updated":"2017-03-05T16:59:44Z","snapshot_observed_at":"2026-08-05T03:48:52.905768Z","submitted_at":"2016-11-02T18:25:40Z","title":"The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.00712","snapshot_observed_at":"2026-08-08T22:21:07.961559Z","title":"J., Mnih, A., and Teh, Y","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":53,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.961559Z"},"links":{"cited_paper":"/paper/1611.00712","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:4371ab431fc9d60614e5c6f0af3382cba40ad39553d67e71cf6c3635e3bb3796","observation_id":"d32d2d83-7941-4c5d-bf68-d3f98f7be453","resolution":{"observed_at":"2026-08-08T22:21:07.961559Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.965206Z","title":"Towards deep learning models resistant to adversarial attacks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":54,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.965206Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:1d20e5a93355a7718dd20ac1043327271a0eac58c11fc943aabeb3a7143659d5","observation_id":"9189d6ad-10d0-4446-a648-4c3b204aadbb","resolution":{"observed_at":"2026-08-08T22:21:07.965206Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.14165","last_updated":"2020-07-22T19:47:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-28T17:29:03Z","title":"Language Models are Few-Shot Learners","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.14165","snapshot_observed_at":"2026-08-08T22:21:07.968600Z","title":"Language models are few-shot learners","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":55,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.968600Z"},"links":{"cited_paper":"/paper/2005.14165","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:48451daaa19085e33625749179ca90b376d1f434fdda7ad3756b9486bcf34924","observation_id":"a70be90c-c3e8-47e1-a78a-4323dabce444","resolution":{"observed_at":"2026-08-08T22:21:07.968600Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:07.972375Z","title":"When do neural nets outperform boosted trees on tabular data? Advances in Neural Information Processing Systems, 36, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":56,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.972375Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:e98a07a49150fba961055c232397a16e5cd9be284d6ce633287535a33150f386","observation_id":"536d2d8a-04e7-4329-9e37-e618bd3866bf","resolution":{"observed_at":"2026-08-08T22:21:07.972375Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.10510","last_updated":"2024-08-13T09:58:44Z","snapshot_observed_at":"2026-08-08T12:08:58.086464Z","submitted_at":"2021-12-20T13:07:39Z","title":"Transformers Can Do Bayesian Inference","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.10510","snapshot_observed_at":"2026-08-08T22:21:07.975675Z","title":"P., Grabocka, J., and Hutter, F","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":57,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.975675Z"},"links":{"cited_paper":"/paper/2112.10510","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:eb57f77c8bf2cb4aa43ab83cc5d47bd0eb48d71aa710d11c37f41d76b6fac3e1","observation_id":"9ff48f83-757f-43cb-83de-d1631cecc024","resolution":{"observed_at":"2026-08-08T22:21:07.975675Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.937810Z","title":"Statistical foundations of prior-data fitted networks","venue":null,"work_id":"ba05bedf-cb80-4392-88ca-24967623aa01","year":2023},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":58,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.979695Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:8e8f67a403115982831089e3c2175c52950ffa59e01f993aec8f1989548e48a0","observation_id":"6a3871a3-a1bc-4357-92b3-25934bd22d41","resolution":{"observed_at":"2026-08-08T22:21:08.941638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.926511Z","title":"STUNT : Few-shot tabular learning with self-generated tasks from unlabeled tables","venue":null,"work_id":"d57b76d0-c28b-4646-b7a2-f0c9a1a26c27","year":2023},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":59,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.983288Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:fb5fefaa8ffd01363c5e3ff762d45cd97e6fd55a10e4c4a342ee55e9342caef0","observation_id":"6d1ff19a-03f4-4a9a-84d4-da57d2bb4175","resolution":{"observed_at":"2026-08-08T22:21:08.930402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.02999","last_updated":"2018-10-22T16:11:14Z","snapshot_observed_at":"2026-08-05T12:53:31.632675Z","submitted_at":"2018-03-08T08:29:38Z","title":"On First-Order Meta-Learning Algorithms","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.02999","snapshot_observed_at":"2026-08-08T22:21:07.986702Z","title":"and Schulman, J","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":60,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.986702Z"},"links":{"cited_paper":"/paper/1803.02999","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:c2c03c6a82d03b70add0e86ae2ab2772558d1433c36e3bce4b12d80206186e48","observation_id":"4220f157-59e6-404b-adec-b5b51931d92e","resolution":{"observed_at":"2026-08-08T22:21:07.986702Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.915239Z","title":"E., and Mitchell, T","venue":null,"work_id":"00905cfd-36af-40ae-81f5-6e76d92cbc6b","year":2009},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":61,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.990461Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:b3a3877b9fd0ee545b48acc531907b4457e9cc2d380099f13cc0331b6f79ca94","observation_id":"165dc0f1-51f4-4890-8365-c88aac0aa68b","resolution":{"observed_at":"2026-08-08T22:21:08.918971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.904152Z","title":"True few-shot learning with language models","venue":null,"work_id":"70a28018-3fdc-4f4e-bf5e-58a27fba95b0","year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":62,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.993966Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:ed86118822fd7360f026deada868ee1d48ba3a01d72b1a5a1351c05579d916b2","observation_id":"260ac65e-551b-496c-be3c-00bc747b816f","resolution":{"observed_at":"2026-08-08T22:21:08.908018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.06312","last_updated":"2019-09-19T13:30:23Z","snapshot_observed_at":"2026-07-06T08:21:35.030482Z","submitted_at":"2019-09-13T16:11:28Z","title":"Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.06312","snapshot_observed_at":"2026-08-08T22:21:07.997778Z","title":"Neural oblivious decision ensembles for deep learning on tabular data","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":63,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:07.997778Z"},"links":{"cited_paper":"/paper/1909.06312","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:95335ce3fbbaaeb32cdf39e722e3f120dd97a7427dbffcd5948e6eedae2bbd87","observation_id":"16dc560c-68d1-49ab-a000-44ae4afa8d55","resolution":{"observed_at":"2026-08-08T22:21:07.997778Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.12409","last_updated":"2022-04-22T18:20:48Z","snapshot_observed_at":"2026-08-07T11:26:24.970964Z","submitted_at":"2021-08-27T17:35:06Z","title":"Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.12409","snapshot_observed_at":"2026-08-08T22:21:08.001797Z","title":"A., and Lewis, M","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":64,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.001797Z"},"links":{"cited_paper":"/paper/2108.12409","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:3bd40cbba8399064e934bf87c24ff809219a3fadd156a144f2c80b1c6e48bcf1","observation_id":"a884971f-4a12-48fd-8c4a-c0217b9f3b82","resolution":{"observed_at":"2026-08-08T22:21:08.001797Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.005832Z","title":"V., and Gulin, A","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":65,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.005832Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:7d2f5ca6ba389999506ad48c5b52f41b76120f9e597b551115f9cf5edc059b78","observation_id":"a0bd913c-056b-4db4-bc8a-010b008769a7","resolution":{"observed_at":"2026-08-08T22:21:08.005832Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.05564","last_updated":"2025-05-24T08:05:47Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-08T13:25:04Z","title":"TabICL: A Tabular Foundation Model for In-Context Learning on Large Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.05564","snapshot_observed_at":"2026-08-08T22:21:08.009691Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":66,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.009691Z"},"links":{"cited_paper":"/paper/2502.05564","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:1add672e99692f2ceab25061111b026e78be00ee78c5268b2f38737ba4f15035","observation_id":"5ab4df1b-b7c7-4556-baf0-48ff0978485f","resolution":{"observed_at":"2026-08-08T22:21:08.009691Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.885266Z","title":"J., Burden, S","venue":null,"work_id":"08594182-4e7b-454f-8f34-3af4ee501f55","year":2016},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":67,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.013484Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:78edd0d74bbb2130276ab4ed3025fe2f7845964d47699b0e6986ab42bfdff9c4","observation_id":"1fe21054-ca63-42ac-8519-d00e57d7f19d","resolution":{"observed_at":"2026-08-08T22:21:08.889094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.03208","last_updated":"2022-07-12T11:56:17Z","snapshot_observed_at":"2026-08-03T22:23:42.091266Z","submitted_at":"2022-07-07T10:29:31Z","title":"Revisiting Pretraining Objectives for Tabular Deep Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.03208","snapshot_observed_at":"2026-08-08T22:21:08.016902Z","title":"Revisiting pretraining objectives for tabular deep learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":68,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.016902Z"},"links":{"cited_paper":"/paper/2207.03208","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:d7ffbd1378c6648417d10fd4cb7095c053165af324cb9c6590dfc9325ef7dce0","observation_id":"74c6b109-7fd8-4fcb-83b0-762adeedcc0b","resolution":{"observed_at":"2026-08-08T22:21:08.016902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.873393Z","title":"A., Xu, Z., Dickerson, J., Studer, C., Davis, L","venue":null,"work_id":"ef29bf24-9108-41d5-b24a-b2f1868bcc19","year":2019},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":69,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.020416Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:dc483c48892ea5a31c3dbe11673134a486f3749c56f92036351184d944c8a9a3","observation_id":"93673bfd-4fe0-43af-a311-1dcecd61b563","resolution":{"observed_at":"2026-08-08T22:21:08.877356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.861531Z","title":"and Armon, A","venue":null,"work_id":"2efb14ff-a197-4679-b140-70b75c513a3e","year":2022},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":70,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.023845Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:bf87380df5a08f38038dafee1f5847fbbc167051e2584d80fc1cc985231cfee1","observation_id":"570c38e0-e234-4594-a5fb-cac88ea268a0","resolution":{"observed_at":"2026-08-08T22:21:08.865980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-08T22:21:08.027224Z","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":"2026-08-08T22:12:40.989329Z","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":"sha256:33544f14c57910a2164a086c11d559ef8b788bfa6fe683065edc54b2b875f750","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.850233Z","title":"C., Thelin, S., and Klein, T","venue":null,"work_id":"254f069e-07f1-42aa-8f8d-2bee8b9309f6","year":2024},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":72,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.031031Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:bef9e2c6973e536fe0fd235966c6ca0df72d367f879ae319645c30829766df38","observation_id":"f6f946b1-5dc5-4d86-9040-1c0b488b425d","resolution":{"observed_at":"2026-08-08T22:21:08.853865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.09864","last_updated":"2023-11-08T13:36:32Z","snapshot_observed_at":"2026-07-06T11:01:58.137141Z","submitted_at":"2021-04-20T09:54:06Z","title":"RoFormer: Enhanced Transformer with Rotary Position Embedding","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.09864","snapshot_observed_at":"2026-08-08T22:21:08.034681Z","title":"Enhanced transformer with rotary position embedding","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":73,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.034681Z"},"links":{"cited_paper":"/paper/2104.09864","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:a167d0db9e8e5e258136e709ba0d793f562418e340489179accbf2a2bda1c4ee","observation_id":"a3d14aa7-0488-4f13-b2f7-9567a1677212","resolution":{"observed_at":"2026-08-08T22:21:08.034681Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.038509Z","title":"Regression shrinkage and selection via the lasso","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":74,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.038509Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:01b57112a54655bc848657b9e2ceace1d82d85793f79713466974e968d84f8b7","observation_id":"be9ac445-8da2-443c-a49c-936ed28a1478","resolution":{"observed_at":"2026-08-08T22:21:08.038509Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.833340Z","title":null,"venue":null,"work_id":"5f448923-3fcc-4fa2-9b77-3c059aafb996","year":1963},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":75,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.041800Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:e94f4e64cb0dd63fab7ea3e8e7c1f306a7404778892f68495e58b6bd8ab8860a","observation_id":"e3ff7f27-2140-4197-bcc3-0c268659f65a","resolution":{"observed_at":"2026-08-08T22:21:08.836727Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.823109Z","title":"L., Cabi, S., Eslami, S., Vinyals, O., and Hill, F","venue":null,"work_id":"99d8533e-6891-443a-9d3a-f8e66f13b54d","year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":76,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.045183Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:515c64a701856d62784f2c5628034b6f55a1b87c8e0fd8e319eba036318f99fd","observation_id":"2a1c5f2a-d99e-428a-8389-6940fdfc01ca","resolution":{"observed_at":"2026-08-08T22:21:08.826753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.03548","last_updated":"2018-10-08T16:07:11Z","snapshot_observed_at":"2026-08-04T00:29:41.447623Z","submitted_at":"2018-10-08T16:07:11Z","title":"Meta-Learning: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.03548","snapshot_observed_at":"2026-08-08T22:21:08.048836Z","title":"Meta-learning: A survey","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":77,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.048836Z"},"links":{"cited_paper":"/paper/1810.03548","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:14c26acf23ea8ce4ec19cc808ef344512d4c5a2e654e3f6286539e043bc27768","observation_id":"3502c078-0805-490e-84c2-0758c05fbad4","resolution":{"observed_at":"2026-08-08T22:21:08.048836Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.812459Z","title":"K., Brahma, D., and Rai, P","venue":null,"work_id":"51c3abb0-247c-4042-b861-6bef4b8cd1ac","year":2020},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":78,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.052834Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:9105901162ec7bc417b14bb762a0fcb7f6d69841bb8e5d71ef9edfd46ea36b08","observation_id":"3ee407ba-b2e8-498f-a6b0-82c12534041d","resolution":{"observed_at":"2026-08-08T22:21:08.815882Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.800872Z","title":null,"venue":null,"work_id":"b2f09a75-ebb9-4bc1-858f-345835cbc2b8","year":2020},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":79,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.056431Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:7f094680f4ecd687b02f5d863070a96561f0a58e6f49a3528155951e841b6c6c","observation_id":"d20bd88a-b744-4a69-8435-a37d340954c2","resolution":{"observed_at":"2026-08-08T22:21:08.804633Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.788997Z","title":"N., Hutchins, D., and Szegedy, C","venue":null,"work_id":"9c33fd44-7999-493a-9301-34647745b7a0","year":2022},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":80,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.059854Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:8bb6e01dd46e4e3139cbab82e06cd4077ea465070bb6935479d26d9a412f3113","observation_id":"937f2aec-b678-422f-bb97-2f75e0e9eff9","resolution":{"observed_at":"2026-08-08T22:21:08.793107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.776783Z","title":"Zero-shot learning - the good, the bad and the ugly","venue":null,"work_id":"03832fec-bbdd-46f1-9649-5c6dbc76f3f1","year":2017},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":81,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.063428Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:bb519a6acb856b89d1034c4ae0afc3b8d77147b0f6dc8cd58fa18d357408deeb","observation_id":"a60e71ea-a9e5-41d0-879b-0096a80ca80d","resolution":{"observed_at":"2026-08-08T22:21:08.781211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.764180Z","title":"H., Schiele, B., and Akata, Z","venue":null,"work_id":"6e416fd9-3ccd-4231-9aa8-d369c6c85d1b","year":2018},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":82,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.067125Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:5d2f192c06fc929302c8cd1e23d26e0dee32a23a9b4effdec82db1ac28407704","observation_id":"6aa000ea-6eed-4879-9e1f-e2ea6fde13ea","resolution":{"observed_at":"2026-08-08T22:21:08.768622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.751587Z","title":"Making pre-trained language models great on tabular prediction","venue":null,"work_id":"fef3eed5-6515-4b48-8c4c-db2e89763b9c","year":2024},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":83,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.070662Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:6a7444f422aafffeb62897c06dc41e8f105c9c751bb4d494966a25e6136e6baa","observation_id":"88ca2ab7-c220-4e14-b98c-4d2b7d30d8fa","resolution":{"observed_at":"2026-08-08T22:21:08.755858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.739078Z","title":"Towards cross-table masked pretraining for web data mining","venue":null,"work_id":"3d9712d2-db84-4cda-9097-1b18f635acea","year":2024},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":84,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.074503Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:b86b42be750c3c1df2dc49ab5ed5c72a4eb452ed655068c4f04d960cce9869cf","observation_id":"ad0d049c-8d0c-4370-ab47-686dcac526ed","resolution":{"observed_at":"2026-08-08T22:21:08.742873Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.078648Z","title":"A closer look at deep learning on tabular data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":85,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.078648Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:73e03e8dd0658bcce9ba7b9e6a4cfed61d01053682831f7680e9d330e19d5dcf","observation_id":"52bceb4c-9c23-4dbb-b5a3-c3a772fc7773","resolution":{"observed_at":"2026-08-08T22:21:08.078648Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.727315Z","title":"You only propagate once: Accelerating adversarial training via maximal principle","venue":null,"work_id":"8ec0ad31-46c1-4db8-ad79-fd8a50a07a98","year":2019},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":86,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.082500Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:b313680d885bb137d8d3380f19c4ceb313afdf08b815228565055a7f8433ee35","observation_id":"e87858bd-e853-458e-bb50-9a1572adf6bf","resolution":{"observed_at":"2026-08-08T22:21:08.731374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.715613Z","title":"Free adversarial training with layerwise heuristic learning","venue":null,"work_id":"7d57dec8-c458-4eb9-8130-3c7c4613c04c","year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":87,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.086012Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:0742cc69f02fce103ed037905a18e82966cb7e2ebebbd78f53bd776bcc2b2a59","observation_id":"edefe187-824f-419a-8fb9-2c13c55b2d01","resolution":{"observed_at":"2026-08-08T22:21:08.719763Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.06090","last_updated":"2023-05-10T12:17:52Z","snapshot_observed_at":"2026-07-06T15:25:32.095335Z","submitted_at":"2023-05-10T12:17:52Z","title":"XTab: Cross-table Pretraining for Tabular Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.06090","snapshot_observed_at":"2026-08-08T22:21:08.089771Z","title":"Xtab: Cross-table pretraining for tabular transformers","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":88,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.089771Z"},"links":{"cited_paper":"/paper/2305.06090","citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:59036dea8a38892e8818881b909dce12eff8f95ce4b425e13e940dc52069a3ae","observation_id":"794d53a2-cdee-4938-a53e-639c07aa23a5","resolution":{"observed_at":"2026-08-08T22:21:08.089771Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T22:21:08.704051Z","title":"Varibad: Variational bayes-adaptive deep rl via meta-learning","venue":null,"work_id":"beeaa259-d7cc-43cc-8baa-80f9f698ff13","year":2021},"citing_paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","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":89,"source":"arxiv_source","source_observed_at":"2026-08-08T22:21:08.094164Z"},"links":{"citing_paper":"/paper/2502.04573"},"observation_digest":"sha256:984c46f6bc0a441316c4a85fae0ff32f7c4b299f15f4523630690a42f2e2dedb","observation_id":"e3ae12b9-6150-49b4-afec-56774acd2620","resolution":{"observed_at":"2026-08-08T22:21:08.708079Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.04573","last_updated":"2025-06-10T01:38:35Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T22:12:40.989329Z","submitted_at":"2025-02-06T23:58:11Z","title":"Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer"},"reference_resolution":{"displayed":89,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":49,"verified_exact":1,"verified_fuzzy":38},"total_outbound_references":89},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 89 of 89 outbound references and 3 inbound Pith citation observations for arXiv:2502.04573."}