{"as_of":"2026-08-15T02:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3170ee5a73aacc1117be67f869dd2881879d8c6c178ab2027dde5478704636f0","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":"2026-08-11T10:39:48.552963Z","state":"measured"},{"denominator":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:11:54.554029Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T21:11:56.274815Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"cited_work":{"arxiv_id":"2412.16462","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.16462","snapshot_observed_at":"2026-08-06T21:11:56.274815Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","venue":"cs.LG","work_id":"338496a3-5c70-487a-a43f-3797f57ecde7","year":2024},"citing_paper":{"arxiv_id":"2507.02991","last_updated":"2025-07-01T18:45:34Z","snapshot_observed_at":"2026-08-12T23:31:45.546624Z","submitted_at":"2025-07-01T18:45:34Z","title":"Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T21:11:54.554029Z"},"links":{"cited_paper":"/paper/2412.16462","citing_paper":"/paper/2507.02991"},"observation_digest":"sha256:93b0ebbb9ed079ba8ff7b01404dbdfa94d5c378dd9a430e2faefd4e2fe1b2897","observation_id":"7bf125fd-00c9-42b0-80e7-e878c43ab3b2","resolution":{"observed_at":"2026-08-06T21:11:56.374572Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.16462/citation-record","integrity":"/paper/2412.16462/integrity","json":"/paper/2412.16462/citation-record.json","paper":"/paper/2412.16462"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.870225Z","title":"Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics","venue":null,"work_id":"257809fb-7b58-470a-83a8-aa187d77cedd","year":2024},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.427457Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:98a9ed2b577b99364900c7776a57a42ab8f41cb2ad89b056b1edeb42a059b48c","observation_id":"0d939e5c-dafd-4791-8682-c836d47e6540","resolution":{"observed_at":"2026-08-11T10:39:48.874080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.860431Z","title":"Handbook of uncertainty quantification , volume 6","venue":null,"work_id":"b6783899-cbd8-4f06-a157-4b9676be8d3f","year":2017},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.435560Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:2c95ce4006f1053442e70778500ab9d00991d507b68e1473f86f15dc58b23e34","observation_id":"c1ba10df-38e8-4516-8c39-52a44c38a00c","resolution":{"observed_at":"2026-08-11T10:39:48.863969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.850308Z","title":"Large sample properties of simulations using latin hypercube sampling","venue":null,"work_id":"1f4e006f-faaf-484c-aad5-661c6355c86d","year":1987},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.439434Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:567751c6de0232d099ab0383227489bf3d801a5677ead95411a90deb3aea5575","observation_id":"2d5c4bde-8e4e-44f9-b85b-a4ad9804c40d","resolution":{"observed_at":"2026-08-11T10:39:48.853837Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.442958Z","title":"Stein variational gradient descent: A general purpose bayesian inference algorithm","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.442958Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:fe1d440c146346bac7cf3eacb0e79b3363c9fee30ce5fb3ed7f2b9f17a5b06b3","observation_id":"1c25b13d-f8a9-498e-a892-7c0376d4cc91","resolution":{"observed_at":"2026-08-11T10:39:48.442958Z","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":"2026-08-11T10:39:48.833813Z","title":"Projected stein variational newton: A fast and scalable bayesian inference method in high dimensions","venue":null,"work_id":"029fc718-66af-4d38-84f5-86a3a13eb16b","year":2019},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.447564Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:1eca1efe40ba26713d811dc842986ae0939b1b379c1d10bc8dc7dfd61b99110d","observation_id":"357abc27-eb67-46d9-95bc-a2ce5eaf48c3","resolution":{"observed_at":"2026-08-11T10:39:48.837266Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.824537Z","title":"A stein varia- tional newton method","venue":null,"work_id":"33c27891-e7a0-4a2b-90e3-7409ea9b8b8f","year":2018},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.451205Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:8e84125a171d98225b5a6d44c30411cd852067881c10ac234be5d889a8025842","observation_id":"bdc4080b-8178-417c-a310-d5a8692efbe9","resolution":{"observed_at":"2026-08-11T10:39:48.827947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.814487Z","title":"Improv- ing the performance of stein variational inference through extreme sparsification of physically-constrained neural network models","venue":null,"work_id":"12597d9d-439a-49d1-a81e-e293847883bc","year":2024},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.454815Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:f0ec0e4759eb0143e01ff92345d04f78bde88a3f0abed31f2ea02309c6c9c2fc","observation_id":"40854e05-6aa0-4772-b5e9-546e41ece82c","resolution":{"observed_at":"2026-08-11T10:39:48.818121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.01312","last_updated":"2018-06-22T14:54:59Z","snapshot_observed_at":"2026-08-14T20:07:02.711956Z","submitted_at":"2017-12-04T19:20:27Z","title":"Learning Sparse Neural Networks through $L_0$ Regularization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.01312","snapshot_observed_at":"2026-08-11T10:39:48.458279Z","title":"Learning sparse neural networks through l 0 regular- ization","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.458279Z"},"links":{"cited_paper":"/paper/1712.01312","citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:fbd9604f9dd4b129f6318a353bc640dcb651324d3508f26aa3fa5993f9aefa73","observation_id":"f73309d5-f251-4aa0-95d6-6354dd6b6521","resolution":{"observed_at":"2026-08-11T10:39:48.458279Z","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":"2026-08-11T10:39:48.462059Z","title":"Stein variational gradient descent as gradient flow","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.462059Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:22c9487aae87dec7a2b46ff64cfabe454d7eb9c41dd463878d576b4ee824074f","observation_id":"20502381-6473-49e6-8e92-f4066cc1bb88","resolution":{"observed_at":"2026-08-11T10:39:48.462059Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.09039","last_updated":"2022-04-19T17:57:36Z","snapshot_observed_at":"2026-08-13T15:59:28.165897Z","submitted_at":"2022-04-19T17:57:36Z","title":"A stochastic Stein Variational Newton method","version":1},"cited_work":{"arxiv_id":"2204.09039","doi":null,"metadata_source":"pith","pith_arxiv_id":"2204.09039","snapshot_observed_at":"2026-08-11T10:39:48.581391Z","title":"A stochastic Stein Variational Newton method","venue":"stat.ML","work_id":"8b5d27a3-1537-46f2-8ff1-f3265058a45d","year":2022},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.466020Z"},"links":{"cited_paper":"/paper/2204.09039","citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:62a2f1f3658078d5d205b0341e2101380da641a27d47e377e2a1ac183ee19550","observation_id":"bd11d2a9-93c4-4b27-ab75-f753a1f7e8a3","resolution":{"observed_at":"2026-08-11T10:39:48.587143Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.799700Z","title":"Pierre, Kevin Linka, and Ellen Kuhl","venue":null,"work_id":"645d0773-c2a8-424e-8c1d-400e4d140174","year":2024},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.469651Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:f8cf51edc5608fc0698d70b93858b5f809cb1da09558540c8a0849ac40e40e46","observation_id":"159d5132-04f4-44dc-b7dd-23dc91861dcb","resolution":{"observed_at":"2026-08-11T10:39:48.803426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.473016Z","title":"Discovering governing equations from data by sparse identification of nonlinear dynamical systems","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.473016Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:30e1f3285301a6312b7f1a68a4d90b88f7282ba9f59404e445289a95bb3112a9","observation_id":"84aca22c-a0dd-498c-8d5d-6c9b5003ff2a","resolution":{"observed_at":"2026-08-11T10:39:48.473016Z","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":"2026-08-11T10:39:48.476503Z","title":"Regression shrinkage and selection via the lasso","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.476503Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:a590abe93e92749bf64d3130a7974c9403764eda60fb62fbecbff427d211065e","observation_id":"33ff2508-cb5c-41b7-a65e-af2eb1fdbd0b","resolution":{"observed_at":"2026-08-11T10:39:48.476503Z","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":"2026-08-11T10:39:48.779491Z","title":"Bayesian compressive sensing.IEEE Transactions on signal processing, 56(6):2346–2356, 2008","venue":null,"work_id":"ebf85230-c92e-43ff-b405-9a87b5eaed51","year":2008},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.479841Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:21263618735efda45f0bacd73457774c6fe76ebb9db3a73e5e1c9c26fd753b88","observation_id":"3b3c0a5e-249c-4740-b8ba-82006c9290bd","resolution":{"observed_at":"2026-08-11T10:39:48.782765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.770248Z","title":"Bayesian compressive sensing using laplace priors","venue":null,"work_id":"f41ccc42-8bad-4c1f-b3aa-c77b04712dc4","year":2009},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.483573Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:467e9cc5b4a3797573b7da2f39fef2c9eca833ce7be83877fae68c55c213457a","observation_id":"a1202106-9efd-4e2e-a56a-5f753819f9db","resolution":{"observed_at":"2026-08-11T10:39:48.773557Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.761096Z","title":"Bayesian compressive sensing via belief propagation","venue":null,"work_id":"3b2708f0-c375-42a0-88a5-0bc864b5144a","year":2009},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.486680Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:73a5d4f9402650a791a55b16760eb4402fa921f169c8b3e8d3f2b57e00fb3bcb","observation_id":"85f10d00-4827-48d9-955c-9d85400f9ac1","resolution":{"observed_at":"2026-08-11T10:39:48.764328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.751905Z","title":"Active sets, nonsmoothness, and sensitivity","venue":null,"work_id":"4a07864c-976b-4e72-a2b8-6543cfba2843","year":2002},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.490031Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:88ae4a8e5d96e6eb12b83afd7484fc6188538bba479450760f6e27f7f72124e7","observation_id":"93e5a897-3866-42c5-ad78-2fd0bf4a734d","resolution":{"observed_at":"2026-08-11T10:39:48.755215Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.741908Z","title":"An online active set strategy to overcome the limitations of explicit mpc","venue":null,"work_id":"91b20f7f-bc6c-4056-823d-a308dc106af7","year":2008},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.493027Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:e96a71282d8355c36f8ffad92bb59680cf5ad19a2f0079d7b9c26adf40606a4a","observation_id":"135f760a-5634-49ae-8415-fe0116d4fdac","resolution":{"observed_at":"2026-08-11T10:39:48.745369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.730791Z","title":"On the convergence of an active-set method for ℓ1 minimization","venue":null,"work_id":"637641d6-547d-49c7-a052-3dfc7202ace2","year":2012},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.495825Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:65fab78bd09508991738b9876ea8b0987ee614c076c9d2c9d63817b115769f09","observation_id":"667313e1-d17e-49b7-9217-32ec7b076c90","resolution":{"observed_at":"2026-08-11T10:39:48.735501Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.719624Z","title":"Learning for constrained optimization: Identifying optimal active constraint sets","venue":null,"work_id":"bdc4d1ae-62db-408e-b4bf-6c7c12d853bf","year":2022},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.499062Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:0f8230b8ec34da3ceb5764339da496a91ed452cf9b70201a8dc708374076d4f1","observation_id":"5b7f518b-ee17-4445-bbe0-632451b67076","resolution":{"observed_at":"2026-08-11T10:39:48.723702Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.709883Z","title":"Introduction to markov chain monte carlo","venue":null,"work_id":"386c2436-947c-4e4e-8462-c3c190f9dd11","year":2011},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.502396Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:4c8b8f5988250f28cad03c1123c242e70cc483cb144b9c8d80b003eb9d2b26c0","observation_id":"bda374e1-d2ae-4797-ab9a-fcb067a061f6","resolution":{"observed_at":"2026-08-11T10:39:48.713414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.700302Z","title":null,"venue":null,"work_id":"1358cf3f-3a43-40cc-b34b-656af3bb6856","year":1986},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.505417Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:db87dfcbbed94b4d2342b7d3462f8e0382754d900cdd05b647490c2c01789a41","observation_id":"f121b207-9806-45bf-8755-d84d705f1e1b","resolution":{"observed_at":"2026-08-11T10:39:48.703549Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.690749Z","title":"A review on data-driven constitutive laws for solids","venue":null,"work_id":"eab6ff05-cfc4-4e14-88e6-bd301decd85b","year":2024},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.508517Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:bc8413707d4f8599067888461c93c4e260ab215e70487b365858a9f4cc253bff","observation_id":"23d93ec3-ad10-449e-9431-9e9d79197389","resolution":{"observed_at":"2026-08-11T10:39:48.694138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.680506Z","title":"Convexity conditions and existence theorems in nonlinear elasticity.Archive for rational mechanics and Analysis, 63:337–403, 1976","venue":null,"work_id":"f95db749-09c4-45cb-92ac-51bc7e0d6877","year":1976},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.512509Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:00d660cd7c8251d16482f28bb84d4d85bf5e6b3c03bb90934f68c27de1b70261","observation_id":"e0fe5fc8-d677-4dbb-81b4-653be652dc3c","resolution":{"observed_at":"2026-08-11T10:39:48.684314Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.516523Z","title":"Data-driven tissue mechanics with polyconvex neural ordinary differential equations","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.516523Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:2dc89d401679a049194d92c937ee69991e78346f9043f8a7f8a9cd7bd47a3bd8","observation_id":"7948aba3-79b7-43fd-a0b3-f708b7971723","resolution":{"observed_at":"2026-08-11T10:39:48.516523Z","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":"2026-08-11T10:39:48.519807Z","title":"Polyconvex neural networks for hyperelastic constitutive models: A rectifi- cation approach","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.519807Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:046dc04c3441779ce8c6389250603e26a4fe7d9f5c4f4f906d6ab390292bf3a2","observation_id":"10c1b049-0de2-4e55-a2d2-35b865473e70","resolution":{"observed_at":"2026-08-11T10:39:48.519807Z","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":"2026-08-11T10:39:48.522878Z","title":"A mechanics-informed artificial neural network approach in data- driven constitutive modeling","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.522878Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:a1394a1a40001b12430ca70a464550249534c0152232a7769356fa0b1ce5e2f2","observation_id":"4ce808fb-cb34-46cc-a35b-c382d4ab0866","resolution":{"observed_at":"2026-08-11T10:39:48.522878Z","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":"2026-08-11T10:39:48.655304Z","title":"Learning constitutive relations using symmetric positive definite neural networks","venue":null,"work_id":"6d1dd0aa-c0ab-4148-9e1d-028eb8c17f27","year":2021},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.526083Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:cd149480f726ac73ea14bbdd1ae99a9ac020800234e9f0542b8f0afa4eab8bfe","observation_id":"d144c0d3-5b03-4416-a0e5-8afea48f3acc","resolution":{"observed_at":"2026-08-11T10:39:48.658686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.529448Z","title":"Polyconvex anisotropic hyperelasticity with neural networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.529448Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:6344133b746f042015e40deeb87add745939630916f47e18c4189678f382e8d3","observation_id":"f7345688-6f98-4d94-8db0-6d15567abecb","resolution":{"observed_at":"2026-08-11T10:39:48.529448Z","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":"2026-08-11T10:39:48.532657Z","title":"Parametrized polyconvex hyperelasticity with physics-augmented neural networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.532657Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:7691de29ecc67436024fffac28c239a0bad4fa94c5b8327871d1c11db34398f6","observation_id":"000d6c15-a26e-4b35-9507-ae4a52bebe00","resolution":{"observed_at":"2026-08-11T10:39:48.532657Z","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":"2026-08-11T10:39:48.535723Z","title":"Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.535723Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:4a275a18a24cc0b9aac6d3226e819cf878d82175ff6b513bfc0db40c8c384dfc","observation_id":"b4d41d86-0c47-4aaa-aa03-121c793854e5","resolution":{"observed_at":"2026-08-11T10:39:48.535723Z","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":"2026-08-11T10:39:48.539049Z","title":"Learning hyperelastic anisotropy from data via a tensor basis neural network","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.539049Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:a0f3815380644f8fd9fff540f22c3df295ad57660e86564c5b814b553ed459d2","observation_id":"f729f497-4c7c-4b4a-9540-dd097f91cb42","resolution":{"observed_at":"2026-08-11T10:39:48.539049Z","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":"2026-08-11T10:39:48.624597Z","title":"Machine- learning convex and texture-dependent macroscopic yield from crystal plasticity simulations","venue":null,"work_id":"aea9282b-7c24-4679-9b5d-b5d4ee472a53","year":2022},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.542506Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:76ab6db157dc3767bec70b7c9935021bdd5e5f2898f1ba100b94444c048b1d8e","observation_id":"7cb6b9aa-7a72-4edb-bb98-991315bfec9b","resolution":{"observed_at":"2026-08-11T10:39:48.628200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:39:48.545782Z","title":"Input convex neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.545782Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:d930d0e2c3173b32747a25a329bd5499a25c548a4a235f9a67413825d968f052","observation_id":"4c020c53-dc15-4ecb-914e-d0918436e06c","resolution":{"observed_at":"2026-08-11T10:39:48.545782Z","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":"2026-08-11T10:39:48.549532Z","title":"Pytorch: An imperative style, high-performance deep learning library","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.549532Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:e398daac1ee14e023d1d53820047835f5ec6153e14c9d46dd910ce7ad24eecd2","observation_id":"dc29da05-96ff-4e3e-83fa-d0fe1c7af306","resolution":{"observed_at":"2026-08-11T10:39:48.549532Z","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":"2026-08-11T10:39:48.603929Z","title":"Robust estimation of a location parameter","venue":null,"work_id":"34141dd3-8a7d-4438-bf81-280212673395","year":1992},"citing_paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T10:39:48.552963Z"},"links":{"citing_paper":"/paper/2412.16462"},"observation_digest":"sha256:28a1ff001a4b0841e77e651a9659ce8bd0277647ebe60c350130af0f89313a2c","observation_id":"fd3d5a3d-886a-474f-b719-95b675a202be","resolution":{"observed_at":"2026-08-11T10:39:48.607420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.16462","last_updated":"2024-12-21T03:28:07Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T00:39:35.006317Z","submitted_at":"2024-12-21T03:28:07Z","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":1,"verified_fuzzy":20},"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":"2026-08-14T06:32:32.682623+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 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2412.16462."}