{"as_of":"2026-08-10T13:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:36c30f671bfc875f18c3cdd60b020e7a2ad4062391d7ea2e0622eb698fbbeebf","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T12:34:02.263194Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2502.02338/citation-record","integrity":"/paper/2502.02338/integrity","json":"/paper/2502.02338/citation-record.json","paper":"/paper/2502.02338"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2106.05264","last_updated":"2021-06-09T17:59:10Z","snapshot_observed_at":"2026-08-10T03:35:32.024866Z","submitted_at":"2021-06-09T17:59:10Z","title":"NeRF in detail: Learning to sample for view synthesis","version":1},"cited_work":{"arxiv_id":"2106.05264","doi":null,"metadata_source":"pith","pith_arxiv_id":"2106.05264","snapshot_observed_at":"2026-08-09T12:34:02.687906Z","title":"NeRF in detail: Learning to sample for view synthesis","venue":"cs.CV","work_id":"70095c08-4369-405c-9e5f-60117ac56044","year":2021},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.083329Z"},"links":{"cited_paper":"/paper/2106.05264","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:3a7561fdeb1668c15e3427020693649f0f64f14a43ed48f3aa850f83845ae486","observation_id":"0b254a50-288c-4dd8-816f-2f21f1d82cb5","resolution":{"observed_at":"2026-08-09T12:34:02.693090Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T12:34:02.774816Z","title":null,"venue":null,"work_id":"5fc17e8a-dad4-4b11-a5d7-889cb2e63474","year":2022},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.240806Z"},"links":{"citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:32243ed9d60117461ab1c261b8d6fd2c26b43553ad6c42af1b36f7fb7204be97","observation_id":"dfb1bcdd-99f9-4ccb-b186-2bc9fc284e5d","resolution":{"observed_at":"2026-08-09T12:34:02.780330Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T12:34:02.106901Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.106901Z"},"links":{"citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:bc73d3ac697b709b0880ac783e67a96c8cd00f740f5afefdc7ecc5b1bd1a2379","observation_id":"fc345eba-8be6-4b3e-805b-8b43d1ced692","resolution":{"observed_at":"2026-08-09T12:34:02.106901Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.12204","last_updated":"2022-11-10T13:32:44Z","snapshot_observed_at":"2026-08-10T01:25:46.866994Z","submitted_at":"2022-01-28T15:59:58Z","title":"From data to functa: Your data point is a function and you can treat it like one","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.12204","snapshot_observed_at":"2026-08-09T12:34:02.120422Z","title":"From data to functa: Your data point is a function and you can treat it like one","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.120422Z"},"links":{"cited_paper":"/paper/2201.12204","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:0636bba14a902912f10536a85f9f06a63736b1c9e233c2e3cdac035f50baaa17","observation_id":"800d1241-2e46-4b4b-9f4d-f1b23ec2145c","resolution":{"observed_at":"2026-08-09T12:34:02.120422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.08883","last_updated":"2023-02-21T09:36:16Z","snapshot_observed_at":"2026-08-10T03:54:39.490020Z","submitted_at":"2023-01-21T04:08:46Z","title":"Versatile Neural Processes for Learning Implicit Neural Representations","version":3},"cited_work":{"arxiv_id":"2301.08883","doi":null,"metadata_source":"pith","pith_arxiv_id":"2301.08883","snapshot_observed_at":"2026-08-09T12:34:02.553987Z","title":"Versatile Neural Processes for Learning Implicit Neural Representations","venue":"cs.LG","work_id":"d5e9c9d2-9e08-48f1-8e05-a7953218b951","year":2023},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.136121Z"},"links":{"cited_paper":"/paper/2301.08883","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:7d971868f21893f37ba4f230c0f758e47185e81e57e959bd70a9171c1b01782f","observation_id":"6ce7ef6e-e35c-43bd-8d5f-df27168f71cc","resolution":{"observed_at":"2026-08-09T12:34:02.558710Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.09106","last_updated":"2016-12-01T10:08:15Z","snapshot_observed_at":"2026-08-08T11:39:44.107967Z","submitted_at":"2016-09-27T05:57:00Z","title":"HyperNetworks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.09106","snapshot_observed_at":"2026-08-09T12:34:02.141315Z","title":"M., and Le, Q","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.141315Z"},"links":{"cited_paper":"/paper/1609.09106","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:9ab3662f4d3eb9b929533af8b8e48e0d59f2e9d391442a9a3d2124240fdc450e","observation_id":"7e4eee52-a191-4eb1-99d2-57413b9bf7bb","resolution":{"observed_at":"2026-08-09T12:34:02.141315Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.04400","last_updated":"2024-03-09T10:47:51Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-11-08T00:03:52Z","title":"LRM: Large Reconstruction Model for Single Image to 3D","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.04400","snapshot_observed_at":"2026-08-09T12:34:02.148034Z","title":"Lrm: Large recon- struction model for single image to 3d","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.148034Z"},"links":{"cited_paper":"/paper/2311.04400","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:d607c9e5862c2ccd90c31e0936e779647ccd05563003e6b2e386eec3e4dad5d1","observation_id":"56d98909-8909-4c05-bb56-dc202265589c","resolution":{"observed_at":"2026-08-09T12:34:02.148034Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1901.05761","last_updated":"2019-07-09T10:49:01Z","snapshot_observed_at":"2026-08-10T08:45:21.703981Z","submitted_at":"2019-01-17T12:37:26Z","title":"Attentive Neural Processes","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.05761","snapshot_observed_at":"2026-08-09T12:34:02.154758Z","title":null,"venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.154758Z"},"links":{"cited_paper":"/paper/1901.05761","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:24d619a54d0c1800e82cbd8b146c8ca44ebd9af0c38dee45373f8efcb9804ff6","observation_id":"39cb8c12-efcb-44bd-8094-ec8a5b7ea64e","resolution":{"observed_at":"2026-08-09T12:34:02.154758Z","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":"2026-08-09T12:34:02.757422Z","title":null,"venue":null,"work_id":"54db04ba-3a75-473d-9768-477f968456b8","year":2021},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.248152Z"},"links":{"citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:71af7762ae926c55ca7d18b456b6a3b6544ff656421f590000ed2ca2af9f7a73","observation_id":"68e8ecf6-98ca-4ad6-8424-43c25fe1dda2","resolution":{"observed_at":"2026-08-09T12:34:02.763003Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-09T12:34:02.173448Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.173448Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:7d133b5114ca825df81032f7e7f197ab0603c5e4ebb10bd19e260d0009f68b00","observation_id":"b73a4d95-1812-4e16-a3fc-61dde3f895c2","resolution":{"observed_at":"2026-08-09T12:34:02.173448Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.05747","last_updated":"2023-01-13T20:03:06Z","snapshot_observed_at":"2026-08-01T06:19:48.934752Z","submitted_at":"2023-01-13T20:03:06Z","title":"Laser: Latent Set Representations for 3D Generative Modeling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.05747","snapshot_observed_at":"2026-08-09T12:34:02.181387Z","title":"R., Strathmann, H., Zoran, D., Schneider, R","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.181387Z"},"links":{"cited_paper":"/paper/2301.05747","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:b031300fa69b91314fe6e4acbac6e330dd93a41b48416102edb96c16fe3e8f60","observation_id":"a453221d-4743-4c7f-a6ab-d079eb4054ac","resolution":{"observed_at":"2026-08-09T12:34:02.181387Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.04179","last_updated":"2023-02-07T23:50:34Z","snapshot_observed_at":"2026-08-03T17:53:40.854429Z","submitted_at":"2022-07-09T02:28:58Z","title":"Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.04179","snapshot_observed_at":"2026-08-09T12:34:02.189593Z","title":"and Grover, A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.189593Z"},"links":{"cited_paper":"/paper/2207.04179","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:0e94d76b0854d19d955f95f53b5d1a8ce8e553d64644bf9a29910d6913a055d4","observation_id":"7049cf1c-5879-4ef3-b377-950a8635c326","resolution":{"observed_at":"2026-08-09T12:34:02.189593Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-09T12:34:02.194742Z","title":"On first-order meta-learning algorithms","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.194742Z"},"links":{"cited_paper":"/paper/1803.02999","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:75a3cfbd19800b02bc22619e6edc2023c3e6cf36c0ab7530b3136a598c305504","observation_id":"00b7b4d2-cc67-4453-b5ac-63d541403f3d","resolution":{"observed_at":"2026-08-09T12:34:02.194742Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.15110","last_updated":"2023-10-23T17:18:59Z","snapshot_observed_at":"2026-07-06T16:37:19.963994Z","submitted_at":"2023-10-23T17:18:59Z","title":"Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.15110","snapshot_observed_at":"2026-08-09T12:34:02.200033Z","title":"Zero123++: a single image to consistent multi-view diffusion base model","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.200033Z"},"links":{"cited_paper":"/paper/2310.15110","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:fb1259db2de7b54220f00a977895a2857101ac4f05b7e940dfceac72b007e403","observation_id":"1bd10a8d-e103-4f93-bc3d-284c7ad2a70d","resolution":{"observed_at":"2026-08-09T12:34:02.200033Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14354","last_updated":"2024-01-25T17:58:51Z","snapshot_observed_at":"2026-08-03T17:22:12.977647Z","submitted_at":"2024-01-25T17:58:51Z","title":"Learning Robust Generalizable Radiance Field with Visibility and Feature Augmented Point Representation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14354","snapshot_observed_at":"2026-08-09T12:34:02.206170Z","title":"Learning robust generaliz- able radiance field with visibility and feature augmented point representation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.206170Z"},"links":{"cited_paper":"/paper/2401.14354","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:00b377b0c150fa9f9b0b08a07bf43101ea1fc87f197827ba2f5575b4cc3c64a2","observation_id":"2a3952a4-0238-4ce5-a9ae-eac5f88877e8","resolution":{"observed_at":"2026-08-09T12:34:02.206170Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.16364","last_updated":"2023-10-04T14:51:01Z","snapshot_observed_at":"2026-07-06T16:24:53.814969Z","submitted_at":"2023-09-28T12:05:08Z","title":"FG-NeRF: Flow-GAN based Probabilistic Neural Radiance Field for Independence-Assumption-Free Uncertainty Estimation","version":2},"cited_work":{"arxiv_id":"2309.16364","doi":null,"metadata_source":"pith","pith_arxiv_id":"2309.16364","snapshot_observed_at":"2026-08-09T12:34:02.324929Z","title":"FG-NeRF: Flow-GAN based Probabilistic Neural Radiance Field for Independence-Assumption-Free Uncertainty Estimation","venue":"cs.CV","work_id":"40c27794-e58a-4ab6-91fc-12638fef7a35","year":2023},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.218252Z"},"links":{"cited_paper":"/paper/2309.16364","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:fa6b97c41b7203c3783b4fff8b5b632d2f764ccd0a2069cf57b5d85ad375c4f2","observation_id":"5b3b5542-83ec-454c-a816-3fb4fd2c662a","resolution":{"observed_at":"2026-08-09T12:34:02.333411Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14855","last_updated":"2023-02-15T09:36:26Z","snapshot_observed_at":"2026-08-10T03:47:31.497653Z","submitted_at":"2022-09-29T15:17:50Z","title":"Continuous PDE Dynamics Forecasting with Implicit Neural Representations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14855","snapshot_observed_at":"2026-08-09T12:34:02.223667Z","title":"Continuous pde dynamics forecast- ing with implicit neural representations","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.223667Z"},"links":{"cited_paper":"/paper/2209.14855","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:f2a93f6d56fa4d345e764349f85157b4dadf9dacc7403523f68d66478be1f56a","observation_id":"bfd5d7ea-24b3-46d8-b9b3-d09cfd4c87e0","resolution":{"observed_at":"2026-08-09T12:34:02.223667Z","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":"2026-08-09T12:34:02.811394Z","title":"Neural Radiance Field Rendering In this section, we outline the rendering function of NeRF (Mildenhall et al., 2021)","venue":null,"work_id":"ae18e8db-68a6-4cf7-a823-0bb7662b1f1c","year":2021},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.229515Z"},"links":{"citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:ed4be21241520a95fd577487cb1a800c0b567bacf30162285a97fa6e138ff0f8","observation_id":"cf0a8bd4-27d9-480b-8d93-e5cf04ec9944","resolution":{"observed_at":"2026-08-09T12:34:02.816260Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T12:34:02.740916Z","title":null,"venue":null,"work_id":"27d4f9ec-9454-4f76-af6e-f7d882725c7b","year":1984},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.253211Z"},"links":{"citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:b1ccd3db031b0a25ec30511e9ee3158eef2e01ae3aeecfcd02ccaa92ae261613","observation_id":"9671d5a0-7cf1-4a1b-bad4-d7aa88cb4c15","resolution":{"observed_at":"2026-08-09T12:34:02.746364Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T12:34:02.723305Z","title":"overfitting","venue":null,"work_id":"4e73df34-013d-4d26-9511-4be92e4d318a","year":2021},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.258070Z"},"links":{"citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:4801039cec4efd01a0fa22df56c68aa4b11b2231d7144d6d4271079ff2af638f","observation_id":"01247e34-a78b-4740-b872-af539691f9f8","resolution":{"observed_at":"2026-08-09T12:34:02.728475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T12:34:02.706837Z","title":"We first represent each target location by integrating the geometric bases, i.e., < xn T , BC >, which aggregates the relevant locality and semantic information for the given input","venue":null,"work_id":"3b6840c4-6b7e-4a9d-b2e5-722a81409e27","year":2023},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.263194Z"},"links":{"citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:7af9061eea8976d60a43baddf4bf58aa903fdc1b1add5db426bcfe07331da978","observation_id":"17c5b765-9d7e-474a-ac81-dd61be932c1a","resolution":{"observed_at":"2026-08-09T12:34:02.712149Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T12:34:02.795083Z","title":"We use a self-attention module, denoted Att, to extract these Gaussian parameters from the context data","venue":null,"work_id":"bb2042f5-5ba5-4f3f-8472-7d07a6c2ecfa","year":2023},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.235343Z"},"links":{"citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:c9796b03e4e89cb552d6f6dea8fd6dcfd2f57cc168d97d271c9d5069307b7ebe","observation_id":"23aa7988-1592-42b0-a74d-66b4b979c895","resolution":{"observed_at":"2026-08-09T12:34:02.800538Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.04776","last_updated":"2022-02-17T16:18:12Z","snapshot_observed_at":"2026-07-06T10:39:45.855392Z","submitted_at":"2021-02-09T11:47:55Z","title":"Generative Models as Distributions of Functions","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.04776","snapshot_observed_at":"2026-08-09T12:34:02.112931Z","title":"W., and Doucet, A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":2009,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.112931Z"},"links":{"cited_paper":"/paper/2102.04776","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:219d4990236515e3b2741f50a0ab4659669a171225b1b843ec6f5ca51d54492d","observation_id":"c5441adf-da83-483e-bede-5496587cd6ac","resolution":{"observed_at":"2026-08-09T12:34:02.112931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.01622","last_updated":"2018-07-04T14:49:46Z","snapshot_observed_at":"2026-08-07T09:28:44.794062Z","submitted_at":"2018-07-04T14:49:46Z","title":"Neural Processes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.01622","snapshot_observed_at":"2026-08-09T12:34:02.126085Z","title":"W., Rezende, D., and Eslami, S","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.126085Z"},"links":{"cited_paper":"/paper/1807.01622","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:137cb50464536a6114a9fe37fc8fbfde5ad8983e57a871895ec6e4ed6467d035","observation_id":"a67630f1-fa95-47ab-88f8-ce8a6012bf28","resolution":{"observed_at":"2026-08-09T12:34:02.126085Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.05449","last_updated":"2022-04-11T23:57:19Z","snapshot_observed_at":"2026-07-06T12:59:19.821349Z","submitted_at":"2022-04-11T23:57:19Z","title":"Neural Processes with Stochastic Attention: Paying more attention to the context dataset","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.05449","snapshot_observed_at":"2026-08-09T12:34:02.165732Z","title":"Neural processes with stochastic attention: Paying more attention to the context dataset","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.165732Z"},"links":{"cited_paper":"/paper/2204.05449","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:cbf980da26fe94c1b3e0c43e354f3db73167dc2e73e57512a3af683e993a65e9","observation_id":"75a1015b-167f-4903-856c-985dc85927ee","resolution":{"observed_at":"2026-08-09T12:34:02.165732Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.13556","last_updated":"2020-06-25T13:20:06Z","snapshot_observed_at":"2026-08-06T11:02:17.970937Z","submitted_at":"2019-10-29T21:56:00Z","title":"Convolutional Conditional Neural Processes","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.13556","snapshot_observed_at":"2026-08-09T12:34:02.131057Z","title":"P., Foong, A","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.131057Z"},"links":{"cited_paper":"/paper/1910.13556","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:651897f990270df99b60b93d0d54d21454a0988804fd81a36d420162e1345eb8","observation_id":"0ca82820-9822-4adc-b30b-d3af30b0c3f6","resolution":{"observed_at":"2026-08-09T12:34:02.131057Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.14468","last_updated":"2023-03-25T13:34:12Z","snapshot_observed_at":"2026-07-06T15:07:56.586673Z","submitted_at":"2023-03-25T13:34:12Z","title":"Autoregressive Conditional Neural Processes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.14468","snapshot_observed_at":"2026-08-09T12:34:02.089499Z","title":"P., Markou, S., Requiema, J., Foong, A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.089499Z"},"links":{"cited_paper":"/paper/2303.14468","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:540115426d7c30eff67386f677cb2aafd68b7a529d6cf7e31d694e9745a01e4f","observation_id":"995b8949-fb15-4802-9dcf-65d0eba0b523","resolution":{"observed_at":"2026-08-09T12:34:02.089499Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.12294","last_updated":"2025-08-22T17:46:35Z","snapshot_observed_at":"2026-08-09T01:41:31.765617Z","submitted_at":"2023-04-24T17:46:01Z","title":"Explicit Correspondence Matching for Generalizable Neural Radiance Fields","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.12294","snapshot_observed_at":"2026-08-09T12:34:02.100815Z","title":"Explicit correspondence matching for generalizable neural radiance fields","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.100815Z"},"links":{"cited_paper":"/paper/2304.12294","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:a48078400e7033ccd4796d8da39432310e9b9cfb97016ec9735ce81795e50c1e","observation_id":"4f163327-dde6-4398-8581-9f508f2980a8","resolution":{"observed_at":"2026-08-09T12:34:02.100815Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1512.03012","last_updated":"2015-12-09T19:42:48Z","snapshot_observed_at":"2026-08-07T11:28:03.211074Z","submitted_at":"2015-12-09T19:42:48Z","title":"ShapeNet: An Information-Rich 3D Model Repository","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1512.03012","snapshot_observed_at":"2026-08-09T12:34:02.095566Z","title":"X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.095566Z"},"links":{"cited_paper":"/paper/1512.03012","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:6af5100f2968a57711d23b51750d2ba9025164122cd99e3f89b8fb9e01ef6521","observation_id":"8c757d50-2936-4827-a12d-d19aea77c7ef","resolution":{"observed_at":"2026-08-09T12:34:02.095566Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.13298","last_updated":"2023-03-02T04:54:00Z","snapshot_observed_at":"2026-08-08T23:14:54.385932Z","submitted_at":"2022-07-27T05:09:54Z","title":"Is Attention All That NeRF Needs?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.13298","snapshot_observed_at":"2026-08-09T12:34:02.211855Z","title":"Is attention all that nerf needs? arXiv preprint arXiv:2207.13298,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-09T12:34:02.211855Z"},"links":{"cited_paper":"/paper/2207.13298","citing_paper":"/paper/2502.02338"},"observation_digest":"sha256:c76b88b1015efe0f643d0e6041c7c98a1a50af25a633f96733708ede717884f6","observation_id":"ec7033b1-2968-4abe-b5db-844b298fe5bc","resolution":{"observed_at":"2026-08-09T12:34:02.211855Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.02338","last_updated":"2025-02-04T14:17:18Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-10T03:35:14.883201Z","submitted_at":"2025-02-04T14:17:18Z","title":"Geometric Neural Process Fields"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":23,"verified_exact":3,"verified_fuzzy":4},"total_outbound_references":30},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2502.02338."}