{"as_of":"2026-08-15T06:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7836eb8cacb7d6d59a27099a8506dd84f4fc2623d2ac2e76d11134f3ec38cd86","coverage":[{"denominator":9,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T15:13:58.965767Z","state":"measured"},{"denominator":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2512.17678/citation-record","integrity":"/paper/2512.17678/integrity","json":"/paper/2512.17678/citation-record.json","paper":"/paper/2512.17678"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1308.3432","last_updated":"2013-08-15T15:19:34Z","snapshot_observed_at":"2026-08-14T04:51:04.817737Z","submitted_at":"2013-08-15T15:19:34Z","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1308.3432","snapshot_observed_at":"2026-08-03T15:13:57.987208Z","title":"Estimating or propagating gradients through stochastic neurons for conditional computation.arXiv preprint arXiv:1308.3432,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.17678","last_updated":"2026-06-03T10:54:04Z","snapshot_observed_at":"2026-08-15T02:18:47.288942Z","submitted_at":"2025-12-19T15:17:34Z","title":"You Only Train Once: Differentiable Subset Selection for Omics Data","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T15:13:57.987208Z"},"links":{"cited_paper":"/paper/1308.3432","citing_paper":"/paper/2512.17678"},"observation_digest":"sha256:c691593149e2b1ceb92f81b749ee6d49b9a1f7f73a58fa274c8c7da0d5aeb063","observation_id":"6ded4178-56d1-446d-861d-bd8d6dbce015","resolution":{"observed_at":"2026-08-03T15:13:57.987208Z","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":"2026-08-03T15:13:58.544321Z","title":"N-act: An interpretable deep learning model for automatic cell type and salient gene identification","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.17678","last_updated":"2026-06-03T10:54:04Z","snapshot_observed_at":"2026-08-15T02:18:47.288942Z","submitted_at":"2025-12-19T15:17:34Z","title":"You Only Train Once: Differentiable Subset Selection for Omics Data","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T15:13:58.544321Z"},"links":{"citing_paper":"/paper/2512.17678"},"observation_digest":"sha256:12e9ec7076634ad72a0f8c966b8c0122e2e85ef1a41a3e4bd3bf9792d107bef3","observation_id":"92ee533d-f2c3-4345-b9e7-1d8616b47610","resolution":{"observed_at":"2026-08-03T15:13:58.544321Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1611.00712","last_updated":"2017-03-05T16:59:44Z","snapshot_observed_at":"2026-08-15T03:06:21.621383Z","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":"2026-08-03T15:13:58.739843Z","title":"The concrete distribution: A continuous relaxation of discrete random variables.arXiv preprint arXiv:1611.00712,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.17678","last_updated":"2026-06-03T10:54:04Z","snapshot_observed_at":"2026-08-15T02:18:47.288942Z","submitted_at":"2025-12-19T15:17:34Z","title":"You Only Train Once: Differentiable Subset Selection for Omics Data","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T15:13:58.739843Z"},"links":{"cited_paper":"/paper/1611.00712","citing_paper":"/paper/2512.17678"},"observation_digest":"sha256:c7841c9b6c2532e47229c9abad5ffa0b142d28bb0b2855028940cac4ae762699","observation_id":"fdd69254-dcdd-450a-9176-8cd38ffdb192","resolution":{"observed_at":"2026-08-03T15:13:58.739843Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.12273","last_updated":"2020-06-03T15:16:42Z","snapshot_observed_at":"2026-08-09T22:55:50.676881Z","submitted_at":"2020-03-27T08:19:22Z","title":"Open Access uptake by universities worldwide","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.12273","snapshot_observed_at":"2026-08-03T15:13:58.436455Z","title":"URL https://doi.org/10.1109/CSB.2003.1227396","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2512.17678","last_updated":"2026-06-03T10:54:04Z","snapshot_observed_at":"2026-08-15T02:18:47.288942Z","submitted_at":"2025-12-19T15:17:34Z","title":"You Only Train Once: Differentiable Subset Selection for Omics Data","version":2},"reference_index":2003,"source":"pdf_text","source_observed_at":"2026-08-03T15:13:58.436455Z"},"links":{"cited_paper":"/paper/2003.12273","citing_paper":"/paper/2512.17678"},"observation_digest":"sha256:4268c6213547ccb8ba92a63c092926255226c2399a9a2a68f456167df86c0b60","observation_id":"5a9a3311-c302-4102-bf25-7f31f9f55733","resolution":{"observed_at":"2026-08-03T15:13:58.436455Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.05386","last_updated":"2016-06-16T23:39:41Z","snapshot_observed_at":"2026-08-15T02:18:02.092001Z","submitted_at":"2016-06-16T23:39:41Z","title":"Model-Agnostic Interpretability of Machine Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.05386","snapshot_observed_at":"2026-08-03T15:13:58.869972Z","title":"Model-agnostic interpretability of machine learn- ing.arXiv preprint arXiv:1606.05386,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.17678","last_updated":"2026-06-03T10:54:04Z","snapshot_observed_at":"2026-08-15T02:18:47.288942Z","submitted_at":"2025-12-19T15:17:34Z","title":"You Only Train Once: Differentiable Subset Selection for Omics Data","version":2},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-03T15:13:58.869972Z"},"links":{"cited_paper":"/paper/1606.05386","citing_paper":"/paper/2512.17678"},"observation_digest":"sha256:05d0a617223880c84e6545dc2170341faf59eef2ce2b6acc7897ef0b31fe1692","observation_id":"6110c0ae-360e-4878-9825-61375328caf6","resolution":{"observed_at":"2026-08-03T15:13:58.869972Z","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":"2026-08-03T15:13:58.965767Z","title":"Comprehensive integration of single-cell data.cell, 177(7):1888–1902,","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2512.17678","last_updated":"2026-06-03T10:54:04Z","snapshot_observed_at":"2026-08-15T02:18:47.288942Z","submitted_at":"2025-12-19T15:17:34Z","title":"You Only Train Once: Differentiable Subset Selection for Omics Data","version":2},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-03T15:13:58.965767Z"},"links":{"citing_paper":"/paper/2512.17678"},"observation_digest":"sha256:d4e45268f784895b30d61e58439f73d0725275814bca8e1ed7d7f52b58ec2ec4","observation_id":"69b11fd8-f599-4df4-a853-fe8b8d2dbaf0","resolution":{"observed_at":"2026-08-03T15:13:58.965767Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1611.01144","last_updated":"2017-08-05T22:45:19Z","snapshot_observed_at":"2026-08-15T03:05:41.956181Z","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":"2026-08-03T15:13:58.636952Z","title":"Categorical reparameterization with gumbel-softmax.arXiv preprint arXiv:1611.01144,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.17678","last_updated":"2026-06-03T10:54:04Z","snapshot_observed_at":"2026-08-15T02:18:47.288942Z","submitted_at":"2025-12-19T15:17:34Z","title":"You Only Train Once: Differentiable Subset Selection for Omics Data","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-03T15:13:58.636952Z"},"links":{"cited_paper":"/paper/1611.01144","citing_paper":"/paper/2512.17678"},"observation_digest":"sha256:840668dd09d003e477b3c32e1f9fda338bd1d54b82e2ec4cb4068291640a8fb7","observation_id":"cf84d2b3-c562-48b2-a699-a2ebdf39218f","resolution":{"observed_at":"2026-08-03T15:13:58.636952Z","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":"2026-08-03T15:13:58.189837Z","title":"Predictive and robust gene selection for spatial transcriptomics.Nature Communications, 14(1):2091,","venue":null,"work_id":null,"year":2091},"citing_paper":{"arxiv_id":"2512.17678","last_updated":"2026-06-03T10:54:04Z","snapshot_observed_at":"2026-08-15T02:18:47.288942Z","submitted_at":"2025-12-19T15:17:34Z","title":"You Only Train Once: Differentiable Subset Selection for Omics Data","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-03T15:13:58.189837Z"},"links":{"citing_paper":"/paper/2512.17678"},"observation_digest":"sha256:7a91f7623f5aad6abcee0cf35a7f4ea5157924ceddf0703b1d34759be1a1c51b","observation_id":"0f14c0f4-315f-4a4c-9172-d4bde4b173c5","resolution":{"observed_at":"2026-08-03T15:13:58.189837Z","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":"2026-08-03T15:13:58.318438Z","title":null,"venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2512.17678","last_updated":"2026-06-03T10:54:04Z","snapshot_observed_at":"2026-08-15T02:18:47.288942Z","submitted_at":"2025-12-19T15:17:34Z","title":"You Only Train Once: Differentiable Subset Selection for Omics Data","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-03T15:13:58.318438Z"},"links":{"citing_paper":"/paper/2512.17678"},"observation_digest":"sha256:d219807b233971f6145ab3038f1eb66105a96715e7d2fe643055c4d7ba90f7e2","observation_id":"50532dee-b711-478f-b771-603b8e72f191","resolution":{"observed_at":"2026-08-03T15:13:58.318438Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2512.17678","last_updated":"2026-06-03T10:54:04Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T02:18:47.288942Z","submitted_at":"2025-12-19T15:17:34Z","title":"You Only Train Once: Differentiable Subset Selection for Omics Data"},"reference_resolution":{"displayed":9,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":9},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 9 of 9 outbound references and 0 inbound Pith citation observations for arXiv:2512.17678."}