{"as_of":"2026-08-04T22:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:255d728d9f31c68af766b29b7b004f7bf463418c77a3729a551791ffe824717f","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-14T17:23:01.013162Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.09710/citation-record","integrity":"/paper/2607.09710/integrity","json":"/paper/2607.09710/citation-record.json","paper":"/paper/2607.09710"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T17:23:01.013162Z","title":"Deepfm: a factorization-machine based neural network for ctr prediction","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:7455cc0dd96fda27ec9e700e210cf763114aaab37e1cf015bb0f4f664be40fba","observation_id":"fc110fbe-98cf-44ee-bcdd-427a01b648c7","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"A survey of data mining and machine learning methods for cyber security intrusion detection.IEEE Communications surveys & tutorials, 18(2):1153–1176, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:4d534dde3c4b4769bafe5d62798df5723c2bb11a6b610f3c2e2bd95bc578308e","observation_id":"75cf1c76-09c0-47cc-b509-b42d57929abd","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Early prediction of circulatory failure in the intensive care unit using machine learning.Nature medicine, 26(3):364–373, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:31fdae3409c6c25acaaa68970c1e625bdd3abcac5152b2b0d2d9c962c01e562f","observation_id":"faa01297-2a7e-4205-a5f6-9b3c6333a277","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Talent: A tabular analytics and learning toolbox.Journal of Machine Learning Research, 26(226):1–16, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:8ec9352f5f904c59ea7c507ff63b3a4bcc1c9314a3e5f383ae204ab83fc72d11","observation_id":"6f54946e-61b8-4ae9-9224-71357a2c8cba","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Xgboost: A scalable tree boosting system.Cornell University, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:63cc97bf6c941adb0da3fa97981d0a0b3337a2c8095b507203f8e12565d5d938","observation_id":"05968c41-8b2a-4868-b897-fea581f680ed","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Catboost: unbiased boosting with categorical features","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:1928a02aaa1b9d3344efdfa409e31b59f88d08baa04635ba752c604d39d35241","observation_id":"9edc87fe-c70f-4db2-9cc2-6272caa8fa38","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Wide & deep learning for recommender systems","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:ccd352eec30fffb76d0b332ac06a7715073492c84900e5ea41fd6461fc31efac","observation_id":"326c80be-1554-4d5a-8002-6569aace8ea8","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Deep & cross network for ad click predictions","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:0a6211ffd29612555e54afda4c7dfdeaf3c587b193ad4fb7dd467810c4b5332e","observation_id":"a40379bd-b6ba-43ee-887a-9a79950e52ff","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Tabnet: Attentive interpretable tabular learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:790caa974079cf2546c50d1370911d129ba3738ae15688de3333467ccc221b64","observation_id":"3267d7bc-867d-4ed7-a48d-4d636c212371","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"On embeddings for numer- ical features in tabular deep learning.Advances in Neural Information Processing Systems, 35:24991–25004, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:b766a2e8b2e42f7f7ecb8295ecee2d74e4921db2d51ac464047dec7a4a2e7ff1","observation_id":"7f01bb39-70f9-40a7-bd78-f72e243c2c26","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Realmlp: Advancing mlps and default parameters for tabular data","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:01be3b4aead7294150b7fb38ed8bb83a54665ffc668400d4fc5fe431fb32a8f9","observation_id":"d2405c65-4c25-4858-b356-7ea2c33a5201","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Autoint: Automatic feature interaction learning via self-attentive neural networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:d0e9dcb9f08e96d536725e2ed172882dd197ceadd1cd0d75741fff6ba2a18f2a","observation_id":"e0e61db4-e9c8-49d1-aa2e-973105e8d9d3","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Re- visiting deep learning models for tabular data.Advances in neural information processing systems, 34:18932–18943, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:1522022bd56cc561e8aacdaaefaf701efb355594bbc68f0e81276cb80edf2daf","observation_id":"0909011f-1a53-4c5c-89e9-06d4b0dcbb68","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.02819","last_updated":"2024-07-25T22:27:56Z","snapshot_observed_at":"2026-07-06T14:38:17.005863Z","submitted_at":"2023-01-07T09:42:03Z","title":"ExcelFormer: A neural network surpassing GBDTs on tabular data","version":8},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.02819","snapshot_observed_at":"2026-07-14T17:23:01.013162Z","title":"Excelformer: A neural network surpassing gbdts on tabular data.arXiv preprint arXiv:2301.02819, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"cited_paper":"/paper/2301.02819","citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:f888f755b8387cee107488a23a90b573ebeb2879909e1dcbbf0b2518d3ed3a99","observation_id":"7f92cedc-ed8a-47d1-9ff3-4e00ad06f9dd","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:4e4d6fe00be59c43ecbb0f9623c7941bb77cea7183c1db4660f13738d5888b28","observation_id":"7c7aa0dd-da87-4e23-b615-c77158008c79","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Poincaré embeddings for learning hier- archical representations.Advances in neural information processing systems, 30, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:26f4a199493beb0eb4ca0c8e5e6e2043234ad87c2e781ae5f024439c8a2913dc","observation_id":"8dc8ff98-ea9a-4e91-bef1-1795bc625528","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Hyperbolic deep neural networks: A survey.IEEE Transactions on pattern analysis and machine intelligence, 44(12):10023–10044, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:109925622f178424519866356c89ddce0ebb1d8b58760f14b02e123f0dd8b3e6","observation_id":"8c1c0d77-3188-463e-93a1-f88a8fd09863","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Tabtrans- former: Tabular data modeling using contextual embeddings.arXiv preprint arXiv:2012.06678, 2020","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"cited_paper":"/paper/2012.06678","citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:45b4ca199536adf19a2f3763f13ffa2503042d714ea010e534df42d7761d102b","observation_id":"27c109ed-f5b9-497a-9b3a-e027a224afe7","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Tabm: Advancing tabular deep learning with parameter-efficient ensembling","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:e975322a821d81632123a365adf29ffb827a6b721b4c057718b106f27d300059","observation_id":"925874cd-3281-43fa-a7b2-290ea18ef59c","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Tabr: Tabular deep learning meets nearest neighbors","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:e57546c3093b941668cd09092fa58b91bf80de488f27e2b727ab529f305909dc","observation_id":"d44103ff-f25a-46e1-8804-aa90af67e306","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Revisiting nearest neighbor for tabular data: A deep tabular baseline two decades later","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:1aa496358f59d2a724904cc39c865976aab7c177009f11d620070bdc113f55da","observation_id":"bc5bd5b0-78bc-4f63-b25f-f763ede413ad","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"TabPFN: A transformer that solves small tabular classification problems in a second","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:6efba4e8abdf0e530239f240b9790c131816ef0f813e26696a95db146c55ee7b","observation_id":"11929a4b-7615-4549-ad71-bd6711c93d21","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Hyperbolic graph convolutional neural networks.Advances in neural information processing systems, 32, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:fe851f5ebd3b38cccfc4af9e11b23a6c23d1574ef788606ab9e4510fba400da1","observation_id":"ddc20a12-bef0-4693-9151-0fa738589aa3","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"A hyperbolic-to-hyperbolic graph convolutional network","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:53e97fbb4038e3e6815c605517cc8cdebba5ad84e17b705c84dbfb066897eb14","observation_id":"b16a5fb8-8254-4d5c-bba2-d53cf709acf0","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Fully hyperbolic convolutional neural networks.Research in the Mathematical Sciences, 9(4):60, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:c7a183fb4780e4b7c6aa816a61b22b35406c0548d8d99102fd36f82859fd094d","observation_id":"616b7341-600d-4465-b597-b2dbd29effe9","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Hyperbolic image-text representations","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:cec0e32f4013d7b895b54b1dcc46b835dbdc059a0d1055c4437b3e0c7d729d39","observation_id":"0de85ef4-95a6-4557-8e9b-42e44fcc4046","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Fast hyperboloid decision tree algorithms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:47a652b686eb7f2031eca8b0bc2bad8dfa524d58319967c16b98dd42d2f50077","observation_id":"42408367-3c78-4161-8d2f-d528aa1d50b3","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Euclidean and poincare space ensemble xgboost.Information Fusion, 115:102746, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:536369104d93720a666105ddfee7ab6d44355b6e0975969f2a148ec9e575117c","observation_id":"074a4383-ba96-4b60-9e11-8a7c8786067c","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"World Scientific, 2008","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:2bedc99ccbaa40e81faca568df41944a1080439a39e097e0745492d4ccb27d9e","observation_id":"d957848a-a895-4b29-8350-b73acbb2454c","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Hyperbolic graph neural networks: A tutorial on methods and applications","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:25155d2a9662abd496aaf63a2696b37c2a80c24f8e9d2d1ac007ee0526f82b89","observation_id":"62c2fab0-8de1-4564-88ac-39dfd4479c91","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Hyperbolic neural networks.Advances in neural information processing systems, 31, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:90eb81bcc982c05627069d58f44a7cf6e02bc1449496da880e57563ea4e2a2d7","observation_id":"84878db2-9c03-4774-9f98-f068768a41af","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Riemannian adaptive optimization methods","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:6c62e291ab68fcba0a82984e8e75c715f67041ccf375db7ac58a831f8ced8358","observation_id":"940f5938-0736-461c-ae66-5025e7ad2ed6","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Lightgbm: A highly efficient gradient boosting decision tree.Advances in neural information processing systems, 30, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:d792c6fc435c4d4ffc76f7848df846d7a2e845264e5771ad66184ce976f1b7d4","observation_id":"31bf69c1-04a6-4e3d-b43f-21e3f24ea511","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Better by default: Strong pre-tuned mlps and boosted trees on tabular data.Advances in Neural Information Processing Systems, 37:26577–26658, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:beb13ece2d89acc702ae208960bce93a532d75cefd40765d3719ae3e8dd80fd0","observation_id":"cfea6a43-d4d0-49af-afbc-c9ab7b3b54d0","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Statistical comparisons of classifiers over multiple data sets","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:43499c48f2e430c306712bd3a3f4c2828645d95ebde882ea78f486056c089d6f","observation_id":"8802f0eb-7a06-4e0d-9417-86beb1fad616","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","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":"2026-07-14T17:23:01.013162Z","title":"Hyperbolic neural networks++","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-14T17:23:01.013162Z"},"links":{"citing_paper":"/paper/2607.09710"},"observation_digest":"sha256:15841247519ceaa6721575d6d290131b1fa4680eb67abe2fb9db08d407550045","observation_id":"c9d3c535-da51-40fd-8cf9-1b727f3a8987","resolution":{"observed_at":"2026-07-14T17:23:01.013162Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.09710","last_updated":"2026-06-23T19:11:52Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-16T23:17:59.252814Z","submitted_at":"2026-06-23T19:11:52Z","title":"Manifold Constrained Tabular Deep Neural Networks"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":36,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":36},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 4 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2607.09710."}