{"as_of":"2026-08-06T17:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:48345db053b5cb336b07047bb70acd1d019b32d879da85ca1b133d31d51e6a77","coverage":[{"denominator":76,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":76,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-19T14:26:58.288381Z","state":"measured"},{"denominator":76,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":76,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+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/2605.15456/citation-record","integrity":"/paper/2605.15456/integrity","json":"/paper/2605.15456/citation-record.json","paper":"/paper/2605.15456"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Preconditioning strategies for nonlinear conjugate gradient methods, based on quasi-newton updates, in: AIP conference proceedings, AIP Publishing","venue":null,"work_id":"b0c3cdb6-8c37-4601-af7a-ba524443353b","year":2016},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:880fe94bad636cc4be1d35cfdeb0394bd2f911e74f5919118b7664b90ac70d21","observation_id":"aca967f1-2950-4f30-ba77-7065b2bb2b56","resolution":{"observed_at":"2026-05-19T14:27:24.471942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Adaptive precision in block-jacobi preconditioning for iterative sparse linear system solvers","venue":null,"work_id":"a98e3a6c-eb1e-4c0d-9f02-ec9d8f5ed005","year":2019},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:312a94c4aae99e4cd95b62a6c05a553a094d3bdcfe8bc6c53d51de50abb9c1c0","observation_id":"09a9edd1-4a00-41f9-893c-2f159c1608ce","resolution":{"observed_at":"2026-05-19T14:27:24.464012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Contour detection and hierarchical image segmentation","venue":null,"work_id":"7d2097fe-65f8-428d-9188-30c3edb142ea","year":2010},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:61c13a5f5c8771e90bf5651aeb4d0c197fe402f52bbb06d3e58e739a494e21cc","observation_id":"302237d9-2a43-4fd3-9442-1d014f799886","resolution":{"observed_at":"2026-05-19T14:27:24.467881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Deep learning methods for solving linear inverse problems: Research directions and paradigms","venue":null,"work_id":"4bf3e1ff-4dad-48fd-a5ea-d5e815316164","year":2020},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:c81c589cd746a0edeefb1c2fc156db79f6eb1438ac56588d64100e6dc02ee314","observation_id":"dff2054d-bd17-4e84-83d8-c6c966db020b","resolution":{"observed_at":"2026-05-19T14:27:24.458337Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"A fast iterative shrinkage-thresholding algorithm for linear inverse problems","venue":null,"work_id":"4c1a2ec3-590e-4402-a74f-cb6644af209b","year":2009},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:a317e9a679031b67e13d32cc9452bbfd16a17d57d50a2ae5239696ddf86bc600","observation_id":"9a696c8d-8ac5-4266-9741-87785f271b2b","resolution":{"observed_at":"2026-05-19T14:27:24.454499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Introduction to inverse problems in imaging","venue":null,"work_id":"348fcae2-2944-4759-a68f-0c3a509019a3","year":2021},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:2408bf3a09e09e7c9027f0bc38ccf14a8680c4f3e79730ce1238abbcc50efc28","observation_id":"ce2aa0f9-d604-4196-82cc-8eb3689eb545","resolution":{"observed_at":"2026-05-19T14:27:24.446594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Linear inverse problems with discrete data","venue":null,"work_id":"ea750f73-27b3-46b8-b449-8b8b49fcb512","year":1985},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:3e9616ad68632a45e88bc8bef40377cf0f43037728105d55ccb56c97ffa5cfed","observation_id":"39a9e367-2c1f-4ccf-95df-4c7bd78a42d4","resolution":{"observed_at":"2026-05-19T14:27:24.477969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Distributed optimization and statistical learning via the alternating direction method of multipliers","venue":null,"work_id":"f3596451-6293-4fe0-a4c8-3a1ea1a46f3f","year":2011},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:6ab8ba50c9a5b3a6208e1753660972c931e7a990279f405d58b94d02f0f9c0f2","observation_id":"a806b163-63e5-4922-943c-4398a8f5a6a4","resolution":{"observed_at":"2026-05-19T14:27:24.416182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Non-Local Means Denoising","venue":null,"work_id":"f75011fa-2b6c-4c3c-9bbf-6b5517ad4a34","year":2011},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:e7207a73101662155b0fadd21015e4e3df355be2e14b68ebc2cb381a56dcff2e","observation_id":"eb3f6b9f-f58f-4c00-835c-74450952461e","resolution":{"observed_at":"2026-05-19T14:27:24.479780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Image denoising: Can plain neural networks compete with bm3d?, in: 2012 IEEE conference on computer vision and pattern recognition, IEEE","venue":null,"work_id":"62e21422-2282-445a-8462-9196501c6086","year":2012},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:1f0d8ebfd5075a6a28d5e6cde2d74cbad146395ccb215c0333bb78553625dac8","observation_id":"ae75050f-a1dd-4983-9167-532802bc9629","resolution":{"observed_at":"2026-05-19T14:27:24.418747Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1109/msp","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T11:22:02.664551Z","title":"A developer’s guide to audit logging","venue":null,"work_id":"10927a28-39b2-4b86-a739-82d7fe73a8ef","year":2020},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:ca137ddbcf3c7c1155dc9d0fd168474c99548b219c4d51ade4a07b1788db53ad","observation_id":"34ca18da-a874-45d7-9e9c-dd7e29f0c2dc","resolution":{"observed_at":"2026-05-19T14:27:23.798543Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Plug-and-play admm for image restoration: Fixed-point convergence and applications","venue":null,"work_id":"da322bc0-9cdc-4c05-afaa-d7bec2f327be","year":2016},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:baff772e1e0fbd81e8e9841a6f9aa8cd4e7c4777b23e607f12b03b88deee303b","observation_id":"258d4d6a-b8a3-4f82-b797-e0110acf762d","resolution":{"observed_at":"2026-05-19T14:27:24.411727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Nonlinearly preconditioned krylov subspace methods for discrete newton algorithms","venue":null,"work_id":"9c2f80f8-1127-4c0c-87cf-6ffee6fb6d28","year":1984},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:97c0b1fc213c655302883ee4685bed0b941ed554fe3b63a431c914d7875d9342","observation_id":"495c2bad-f047-488d-8841-9ba593ef86fc","resolution":{"observed_at":"2026-05-19T14:27:24.408579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Learning efficient object detection models with knowledge distillation, in: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R","venue":null,"work_id":"f3e156d6-a0df-40af-8342-76593dc87a69","year":2017},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:4825bd6e68af0ebaeaeec66362be062694a996878819c16fcc18d141c9240504","observation_id":"cf4bebf2-8bd2-4709-b3c8-eb2a413dd13e","resolution":{"observed_at":"2026-05-19T14:27:24.432712Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Multiscale cholesky preconditioning for ill-conditioned problems","venue":null,"work_id":"700e0185-dd09-456f-aabb-9cbe8cf4c4b0","year":2021},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:2c6b462e13cf08fd50c98dea7c05fe0730de0d0799e87b70a6af522164c32cf9","observation_id":"d9fea5d7-49df-4b9d-9843-7e356cff5848","resolution":{"observed_at":"2026-05-19T14:27:24.373762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Imagedenoisingwithblock-matchingand3dfiltering,in:Imageprocessing:algorithms and systems, neural networks, and machine learning, SPIE","venue":null,"work_id":"01892fa3-c7a2-4a5b-b9eb-1777e2462152","year":2006},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:7bd1b9faa99138f4a13ad8a5065759e7869944415613a5e54a482640225314bb","observation_id":"32f50263-0509-41ab-bcb4-b898ee8e39c6","resolution":{"observed_at":"2026-05-19T14:27:24.435067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Multilevel preconditioning and adaptive sparse solution of inverse problems","venue":null,"work_id":"6da3e693-59fd-4167-aff8-d90520787e33","year":2012},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:e718e25e705fbecef452e31c00696976e0f96a777cbb285f71d090a6e92a0b14","observation_id":"f221a579-7b84-402b-884b-c08f70477d7c","resolution":{"observed_at":"2026-05-19T14:27:24.440799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"A preconditioner for a primal-dual newton conjugate gradient method for compressed sensing problems","venue":null,"work_id":"075792ee-65e3-4cb2-9db4-4394ebd1f3ab","year":2015},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:79e4d00e8d8f6b8913ec84c51634aec59c62cd6fe688101666d3bdf43b771654","observation_id":"b17d21e1-ec85-4c08-ab59-bb247f826bd5","resolution":{"observed_at":"2026-05-19T14:27:24.380469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"An iterative thresholding algorithm for linear inverse problems with a sparsity constraint","venue":null,"work_id":"52575f47-74be-4b38-a7e5-3961ce445162","year":2004},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:9572032026084dff6272d90c5b03c113a1d2b0084e7bfa760b4986848003a1fc","observation_id":"273afaf1-cb59-4b65-b510-6167f617c654","resolution":{"observed_at":"2026-05-19T14:27:24.450390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.11435","last_updated":"2024-02-02T18:52:51Z","snapshot_observed_at":"2026-08-06T06:26:04.600331Z","submitted_at":"2023-03-20T20:28:17Z","title":"Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration","version":5},"cited_work":{"arxiv_id":"2303.11435","doi":"10.48550/arxiv.2303.11435","metadata_source":"arxiv_reference","pith_arxiv_id":"2303.11435","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Inversion by direct iteration: An alternative to denoising diffusion for image restoration","venue":"arXiv (Cornell University)","work_id":"cf4ab637-697b-4cfb-8fbc-d1bcf44b58b9","year":2023},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"cited_paper":"/paper/2303.11435","citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:b34a1b85b2d5c61e8cd1b5e9aa271b2c5fe5eae327b31a2dac966b52146dce46","observation_id":"80b32f8d-c5cf-452d-b67b-b85763801610","resolution":{"observed_at":"2026-05-19T14:27:24.094214Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"The mnist database of handwritten digit images for machine learning research [best of the web]","venue":null,"work_id":"21f15569-f7a0-4aeb-bada-9ac605546f7a","year":2012},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:a122cd8cfb0dbea67654ef95f04022104b0787ee2f98aabe4a9d4f57217c544d","observation_id":"e25a4a53-01cd-4b92-a5d3-594973a75e44","resolution":{"observed_at":"2026-05-19T14:27:24.513382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Numerical methods for unconstrained optimization and nonlinear equations","venue":null,"work_id":"7d0b2eb0-8e76-4082-a074-203c8986f778","year":1996},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:d32de2be9e9f76e377c250ff93e4c2ab2e32ec41d8412af85993740702743a1c","observation_id":"67aebde4-0cb5-4d40-8c94-c2b2832a3186","resolution":{"observed_at":"2026-05-19T14:27:24.503603Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":"2010.11929","doi":"10.1175/jcli-d-22-0357.1","metadata_source":"pith","pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","venue":"cs.CV","work_id":"e96730e3-129b-4db6-b981-15ab7932e297","year":2020},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:8cc451b5696bdb6e15c6af33b3dfab91626ba7d4c3e926774f647a8df2e5c518","observation_id":"384eaa89-7f09-4c14-ae33-da45e154d0b3","resolution":{"observed_at":"2026-05-19T14:27:24.083463Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Single-pixel imaging via compressive sampling","venue":null,"work_id":"374892bc-b4c6-48d7-a0b5-1e704df3dd7e","year":2008},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:fd69f4ba5c873bc206d04bbd235b0722db9d83902c998413fdb9d66fd3e758b8","observation_id":"c439c6eb-a574-4afc-a3ea-529bba666e60","resolution":{"observed_at":"2026-05-19T14:27:24.428151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00260","last_updated":"2026-07-06T22:40:17Z","snapshot_observed_at":"2026-08-02T09:02:29.128494Z","submitted_at":"2024-06-01T01:49:37Z","title":"Greedy Learning to Optimize with Convergence Guarantees","version":10},"cited_work":{"arxiv_id":"2406.00260","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.00260","snapshot_observed_at":"2026-07-08T01:18:17.138077Z","title":"Learning preconditioners for inverse problems","venue":null,"work_id":"baae4dae-4b6a-4cb5-92db-e81c13ad6752","year":2024},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"cited_paper":"/paper/2406.00260","citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:a07cca66a39bfdbab5c3137b237c83236030077f4359f665530ff1955e28f95d","observation_id":"ecc1e445-7dd8-4720-a421-0ebec5efb3c0","resolution":{"observed_at":"2026-07-08T01:18:17.138077Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Conjugate-gradient preconditioning methods for shift-variant pet image reconstruction","venue":null,"work_id":"32e8a208-9343-434b-8ebc-06614178fef5","year":1999},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:eed0791848d08014cd78b29e0944f57d116707f3949f6f755a50c077c697b7ca","observation_id":"adc9a36c-0ec2-4c7e-8abe-0269ba0518ef","resolution":{"observed_at":"2026-05-19T14:27:24.423906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"On the origins of linear and non-linear preconditioning, in: Domain decomposition methods in science and engineering XXIII, Springer","venue":null,"work_id":"b8b8c3c5-8813-4ae5-8315-5a491bf3b26a","year":2017},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:554f994baee22784d994d1ceede183d50b51da4d5f5432943038930bdd75e3b2","observation_id":"ea1b1a62-953d-49ce-aeb4-c389d92b0782","resolution":{"observed_at":"2026-05-19T14:27:24.426168Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Image restoration by denoising diffusion models with iteratively preconditioned guidance, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp","venue":null,"work_id":"ee7698c1-cf25-4abf-985c-c0cdcf47ad03","year":2024},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:153d6271595bc717dc26c080796ee6e374b85753d1b4e1da8e8de3e0ebcaa1d3","observation_id":"ac2b58ae-7f33-4278-a6bf-528619a63152","resolution":{"observed_at":"2026-05-19T14:27:24.430389Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"4921.196496","doi":"10.1145/1964921.1964964","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Domain transform for edge-aware image and video processing, in: ACM SIGGRAPH 2011 Papers, Association for Computing Machinery, New York, NY, USA","venue":null,"work_id":"a62659bc-437a-4e3c-a115-362e6c0a615a","year":2011},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:e1e1d375dd49d1c236438ecf6a33ed512ef1351825205e916afbe1c66b40c47f","observation_id":"74304cc6-5470-4f7d-a55e-5905943ceee0","resolution":{"observed_at":"2026-05-19T14:27:23.827928Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-05-19T15:23:30.724336+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-19T15:23:30.724336+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Tikhonov regularization and total least squares","venue":null,"work_id":"8ea007fb-43d9-4f68-b742-0e4e4e8f4827","year":1999},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:01a399e793243bbf3008c441ae1d95731e781e1c90b1672bba0f86c3081b02ac","observation_id":"a6ebb12f-9e89-4381-bf3d-5d76e5b16380","resolution":{"observed_at":"2026-05-19T14:27:24.489650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Knowledge distillation: A survey","venue":null,"work_id":"c7d33242-a435-4905-a887-894d9920dc1f","year":2021},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:073fafa9ab2e31af14816989612aca1c4ed40d94d2447a9ec3f16089cdc448ac","observation_id":"be98738b-6eb1-43c1-bbaf-17715b6864b5","resolution":{"observed_at":"2026-05-19T14:27:24.505418Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2602.20328","last_updated":"2026-04-15T22:18:57Z","snapshot_observed_at":"2026-07-06T22:46:49.536946Z","submitted_at":"2026-02-23T20:24:00Z","title":"GSNR: Graph Smooth Null-Space Representation for Inverse Problems","version":2},"cited_work":{"arxiv_id":"2602.20328","doi":null,"metadata_source":"pith","pith_arxiv_id":"2602.20328","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"GSNR: Graph Smooth Null-Space Representation for Inverse Problems","venue":"cs.CV","work_id":"75ca6d01-eefc-412c-8fcd-aa32e79a63a4","year":2026},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"cited_paper":"/paper/2602.20328","citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:702f77bc3ed9564470f5f128da5267977a146ae6ae21691dfc2667a88ec6c8ec","observation_id":"7762a3c0-2f8b-4de4-ba72-39de1165670f","resolution":{"observed_at":"2026-05-19T14:27:24.089595Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"6162.2025","doi":"10.1109/camsap66162.2025.11423951","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":null,"work_id":"b7f61732-3fcd-4f89-b852-e93b2566f03a","year":2025},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:5db1caf66d1bf3b3f4f152faa1520f8ab6f8800ec803186f45a5742332ad3a35","observation_id":"b78c1d4c-5ceb-49f8-9450-9a963bc3f1fb","resolution":{"observed_at":"2026-05-19T14:27:23.819934Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-05-19T15:23:31.080917+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-19T15:23:31.080917+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Image restoration","venue":null,"work_id":"f4a44e4d-e899-4ce0-a778-a6cd00900b8e","year":2018},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:d9d4527325a991440c03656159a304dc26461c196285b33c4feff46cfbdd94ad","observation_id":"93862609-9b69-4d7c-953c-77005e5a846e","resolution":{"observed_at":"2026-05-19T14:27:24.421439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Half-quadratic-based iterative minimization for robust sparse representation","venue":null,"work_id":"6d95b37d-d14b-424c-8117-a5dda26096bb","year":2013},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:b9d92e0ba55704cb3695172bde7cf710aaa9d2e778f4d82580a91798863f457a","observation_id":"899602b2-bcdd-4441-a94c-e7a4fc25b07d","resolution":{"observed_at":"2026-05-19T14:27:24.462187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-07-06T04:11:24.157003Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":"1503.02531","doi":"10.1109/cvpr52733.2024.01515","metadata_source":"pith","pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Distilling the Knowledge in a Neural Network","venue":"stat.ML","work_id":"d927ab1f-17b8-4002-9d09-c3d55764fbad","year":2015},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:fd7259de564cf6fdd195f27f11f80726eb8f83b839d53937951b46d36e8eb8b5","observation_id":"d38f56d0-5d0c-446d-9776-674b1151c501","resolution":{"observed_at":"2026-05-19T14:27:24.069861Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Polynomial preconditioners for regularized linear inverse problems","venue":null,"work_id":"b9646670-c1af-4140-8e4d-cc0e6bf4a6d4","year":2024},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:5047f2f08c4fbba6cb8c76c6a01836f3918207a6ccf9a39445e84bf7780c41e3","observation_id":"230d68cf-d593-4827-868b-160a7f2873d4","resolution":{"observed_at":"2026-05-19T14:27:24.401556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"NPN: Non-Linear Projections of the Null-Space for Imaging Inverse Problems","venue":null,"work_id":"f3e0ad2a-ae62-4130-93b8-eb13aabb1af6","year":2026},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:08d4abca7e71fa3c19d1ff435f64bed646ce30197dd43633b4e3f137d7e2b357","observation_id":"601a5094-d3d8-4936-aa8f-572d737dd4fc","resolution":{"observed_at":"2026-05-19T14:27:24.377006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Sparsity regularization in inverse problems","venue":null,"work_id":"7eebeb7f-0e61-4512-830a-fc9897d163c4","year":2017},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:91683a9f4bae4595b7adc2bbc57b73ce573730a0d1053b33e59743eaa7b9c1fa","observation_id":"f4598fc1-3bc8-46dc-8b21-8caf61e2058d","resolution":{"observed_at":"2026-05-19T14:27:24.437014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Polynomial preconditioners for conjugate gradient calculations","venue":null,"work_id":"726145af-84fc-427b-bdb4-c88fa1a1aec1","year":1983},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:ea8b79296dd925657609ca799de3f1f82de3f33bac3ec1d692a59210b7679b0b","observation_id":"386115ed-2033-446c-bce6-e1901c779389","resolution":{"observed_at":"2026-05-19T14:27:24.481809Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Plug-and-play methods for integrating physical and learned models in computational imaging: Theory, algorithms, and applications","venue":null,"work_id":"7afe2a17-1fb2-48d6-b7a0-f801842c0af8","year":2023},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:bcc17c45212ccdc31aed7d7c74a344fe4a8e273874f8584bb4bac53effdc53b6","observation_id":"ec919eb9-a0a7-4b4b-9923-482d3d54bdb9","resolution":{"observed_at":"2026-05-19T14:27:24.483729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":"1412.6980","doi":"10.1002/mrm.28086","metadata_source":"pith","pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Adam: A Method for Stochastic Optimization","venue":"cs.LG","work_id":"1910796d-9b52-4683-bf5c-de9632c1028b","year":2014},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:1f8403048043ac58b091b45c934f7a4c4bf2ebdc80d4bd07a500fb2759345039","observation_id":"e06fa90c-ecc2-43a7-bef5-1c04a07309b4","resolution":{"observed_at":"2026-05-19T14:27:24.080548Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"FastMRI:Apubliclyavailable rawk-space andDICOMdataset ofknee images foraccelerated MRimagereconstruction using machine learning","venue":null,"work_id":"73ccd694-f99a-4f39-bf46-c5a5ba06d61c","year":2020},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:918c30a5fd2cc6da6c25f69ebfa67da1e672db4a19bc41ae25e7aa3abe52567b","observation_id":"5a43ffe7-8cca-4665-a012-443f9a6476a7","resolution":{"observed_at":"2026-05-19T14:27:24.404947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Gradient-based learning applied to document recognition","venue":null,"work_id":"40d71a30-23fc-48fd-8757-cd7ccffa024c","year":1998},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:990ff395b4dd8db29078dad26414d9220295594548cd6abded036cdbfa27df19","observation_id":"2df5c679-603b-48c0-941a-51809f095825","resolution":{"observed_at":"2026-05-19T14:27:24.392659Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"76749d1b-6029-4097-81c8-94fe94e1b039","year":2022},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:5aa994ea9faa54641610a099f276c5364d4351780a6a88e439a1eb8c9c004552","observation_id":"2648589a-4aac-4e6e-bde2-417f8aa4a0f9","resolution":{"observed_at":"2026-05-19T14:27:24.414098Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1137/17m1128502","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A note on adaptive nonlinear preconditioning techniques","venue":"SIAM Journal on Scientific Computing","work_id":"1db3eb3f-4a00-42ac-b801-46a1f08432b1","year":2018},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:5878b869c6487c6b1627998fa22e919d4b28138c686abf22dabf92a50c4a9b5c","observation_id":"c9794e12-745b-41f3-ac18-bbf157547e4c","resolution":{"observed_at":"2026-05-19T14:27:23.823113Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-05-19T15:23:31.517103+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-19T15:23:31.517103+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Structured knowledge distillation for semantic segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","venue":null,"work_id":"605db546-b62f-444e-8722-c61f1a9a5441","year":2019},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:7e80cf88937289f27b01aaaa2d4b48146dbca418209a8e3b3fee2b4bedf1173f","observation_id":"81b7fc6b-38f4-4c20-b3fb-dac35827f257","resolution":{"observed_at":"2026-05-19T14:27:24.495438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Deep learning face attributes in the wild, in: Proceedings of International Conference on Computer Vision (ICCV)","venue":null,"work_id":"81cb48e6-9f84-4911-8d62-35b3f5cfb4ee","year":2015},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:868cfacc58f2b0dd4edf37a90942b3bb93a38c8d449e62e9c6a18df09897867b","observation_id":"899bbd22-5bf3-4cdc-9983-bef347fcbcd8","resolution":{"observed_at":"2026-05-19T14:27:24.491676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"A convnet for the 2020s, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp","venue":null,"work_id":"c81666dd-7819-4820-b436-739b0a05979e","year":2022},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:9136d02e6fe0a415eb8fc68c991533c35d1d8d72f0b31ac3915020b6b280364a","observation_id":"1a9166dd-f205-4fd6-a24f-9913c44671e8","resolution":{"observed_at":"2026-05-19T14:27:24.501373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":"1711.05101","doi":"10.1137/1.9781611972825.47","metadata_source":"pith","pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Decoupled Weight Decay Regularization","venue":"cs.LG","work_id":"07ef7360-d385-4033-83f7-8384a6325204","year":2017},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:dabc35cc7f2c5c3ddc55760f14174cf57ffecef156ce41c4745664ebb02c0bb9","observation_id":"7bbbdb3b-213b-4a61-aaf0-b3b1fc7c0b1b","resolution":{"observed_at":"2026-05-19T14:27:24.073920Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Compressed sensing mri","venue":null,"work_id":"54d69c71-6708-4ed0-a84d-0a89f2615df9","year":2008},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:e70b34044f20d6548f64084e6bfac38c0fce6b7c6e214fb58a85410ac4112f6f","observation_id":"7f073500-29e1-4d72-8ace-77dc646b6545","resolution":{"observed_at":"2026-05-19T14:27:24.497337Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Improving medical image denoising via a lightweight plug-and-play module, in: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE","venue":null,"work_id":"baa7dd90-e991-44ad-b647-fe15c73c9e7e","year":2023},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:f0d2cee7ab133a1dcabb51d67e320a9f2ad32360dd37c46a70f79e99d91f0ec9","observation_id":"039675f4-0a76-4963-9853-13a8a6d04216","resolution":{"observed_at":"2026-05-19T14:27:24.465937Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Kd-mri: A knowledge distillation framework for image reconstruction and image restoration in mri workflow, in: Medical imaging with deep learning, PMLR","venue":null,"work_id":"68d00c5b-b960-438f-a116-0cfc476acc85","year":2020},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:522676fd65647b5a3b47a834790bc2c1fc0436c0953f80d612361afed1a61367","observation_id":"abfc4659-603a-4997-9e4c-aa18844a8ece","resolution":{"observed_at":"2026-05-19T14:27:24.469803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Efficient preconditioners for optimality systems arising in connection with inverse problems","venue":null,"work_id":"61532a38-a50e-4710-b2d1-0a11aaef755a","year":2010},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:9de5d81494f9583e4e3ff82463e5f9ae68d85756190489002a3eb19e9484efe5","observation_id":"8c9da995-1d57-43d3-b828-78a5537a9420","resolution":{"observed_at":"2026-05-19T14:27:24.476151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Deep learning techniques for inverse problems in imaging","venue":null,"work_id":"d161f4a7-6992-4fab-8f9f-4a28f9fb796f","year":2020},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:06e24c155ff5f01053d563fb3fc6fd5fc1ad895c04e1e9b5eec5da940ad87996","observation_id":"3feb3e5d-b047-4fa1-93d6-1d1a223fc699","resolution":{"observed_at":"2026-05-19T14:27:24.398526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Proximal algorithms","venue":null,"work_id":"13ec1ac0-f294-474f-bc15-b9095ac6eff7","year":2014},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:223a2df8867d7d97e5264aac1fa8c91f5c46ef482d2f9581c08f8668a9a71a2e","observation_id":"3cc71a2d-2e36-4127-8fd4-ed7188b9caca","resolution":{"observed_at":"2026-05-19T14:27:24.383755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Preconditioners for krylov subspace methods: An overview","venue":null,"work_id":"6f90e1a3-8662-4248-a3b5-9d2a6c63dabc","year":2020},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:1a9c04d5915f06530e5ee31b9471508240d8627aeaeb392cfe054b2e1362caac","observation_id":"de4d7b6a-3e3b-4b03-aa90-31f0c7094bef","resolution":{"observed_at":"2026-05-19T14:27:24.460148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Coil sensitivity encoding for fast mri, in: Proceedings of the ISMRM 6th Annual Meeting, Sydney","venue":null,"work_id":"06d3df14-552a-4969-bd15-8920d643d552","year":1998},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:7e9cebad208c5b9c1dcfcaee0dd1a54e0e96fc6f1b10afa11d4baa17bcd3b8ab","observation_id":"3a5da1a7-2a14-40da-a3b4-2ccbad6d4432","resolution":{"observed_at":"2026-05-19T14:27:24.442892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"On the expressive power of deep neural networks, in: international conference on machine learning, PMLR","venue":null,"work_id":"bb645767-af48-41fa-bd71-f7ad4d88853f","year":2017},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:5112775842bdf3d7dbbae014dce84c7388b29f097aad5ed4e8cc362ab4eca0d8","observation_id":"c3353fc8-49de-4c49-be48-2c2c3cc02017","resolution":{"observed_at":"2026-05-19T14:27:24.444767Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"The little engine that could: Regularization by denoising (red)","venue":null,"work_id":"80393724-4697-47f7-b4c7-4b2e4adefe7c","year":2017},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:f8e15abd9012a5438c6c2410d7640858122d5966cd661d040be872a2ade4194f","observation_id":"5e9893f0-c95b-4eda-b653-0b7f621c101f","resolution":{"observed_at":"2026-05-19T14:27:24.448529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"3ba0fe08-d553-46c6-84c7-752c2890efd4","year":2015},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:5a3f0254ec87c04e621c4657c92204db420b5ee4318b6c914a39ff27a68149da","observation_id":"3e66b105-b92d-45f7-9909-2709c26610db","resolution":{"observed_at":"2026-05-19T14:27:24.452440Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"The perceptron: a probabilistic model for information storage and organization in the brain","venue":null,"work_id":"fc2f2e36-aaee-40a2-bcb0-2605291c1cac","year":1958},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:44021205b29b9596bbf0c6b7e629480fbd0eae099a7ccad943cfff467dafe7ed","observation_id":"0456f2db-48e8-432c-a517-895a593ee19e","resolution":{"observed_at":"2026-05-19T14:27:24.456410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"End-to-end variational networks for accelerated mri reconstruction, in: MICCAI 2020, Springer","venue":null,"work_id":"150a533a-d4e2-40e4-a152-86b5750f353c","year":2020},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:805e925549e5545b215e9c68f1433655640a2317fdf64e1658c010f32feede60","observation_id":"ad1316bd-6dfa-4608-8f31-6e056f6f08ec","resolution":{"observed_at":"2026-05-19T14:27:24.473948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Edge-preserving and scale-dependent properties of total variation regularization","venue":null,"work_id":"1b1a9f39-ab14-4892-9845-ab103de10045","year":2003},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:9203ebaedf87c2703364ceff8915ed096ac98b23dc8499037186efa36bd83f32","observation_id":"4043aa51-deb1-41cb-adc4-c87bdb24bf3a","resolution":{"observed_at":"2026-05-19T14:27:24.507347Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5281/zenodo.7982256","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"DeepInverse: A deep learning framework for inverse problems in imaging","venue":"Zenodo (CERN European Organization for Nuclear Research)","work_id":"3c73f0d3-9185-473b-9349-fb10e55b8cc1","year":2023},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:2ea4769c12486467126a5bea832068c09b1e59e3cae306dac65fe261a4055a31","observation_id":"518ef31a-81c9-46b1-be4c-ba92909aec4e","resolution":{"observed_at":"2026-05-19T14:27:23.807937Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-05-19T15:23:31.881023+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-19T15:23:31.881023+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Provably convergent plug-and-play quasi-newton methods","venue":null,"work_id":"26479b3e-9d8e-4223-bcb5-4928dd541f8f","year":2024},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:b6d555bc93475d2e39f97efaca8b2257ba1bc4b4efc050606071ad93254cdd55","observation_id":"377246b5-b8eb-46cc-aa58-0ad70b989c15","resolution":{"observed_at":"2026-05-19T14:27:24.395579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Image restoration and reconstruction using targeted plug-and-play priors","venue":null,"work_id":"0c52a0d3-e71c-4b02-91e6-d7d042294fb8","year":2019},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:a731ce043c140c304fce6edcd1ea38d50ea78f9e8ad56d321bd6f4853a3aadda","observation_id":"d5b1a2d2-b8e4-47cb-b7a2-3806b185530c","resolution":{"observed_at":"2026-05-19T14:27:24.388723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"A survey on super-resolution imaging","venue":null,"work_id":"2a018c6c-9c6e-4eff-b727-32c73bbb09b3","year":2011},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:b8707bd1d52447ac7df2947da10ef068f63b884731ad15913d3c3a1ce3d5d48b","observation_id":"34b69593-e761-4c20-816b-4974c62b954e","resolution":{"observed_at":"2026-05-19T14:27:24.438919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2013.673704","doi":"10.1109/globalsip.2013.6737048","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Plug-and-play priors for model based reconstruction, in: 2013 IEEE Global Conference on Signal and Information Processing, pp","venue":null,"work_id":"81a98ae7-e426-4d01-aa31-4132ba8c299d","year":2013},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:68106a3765d32cf615bee1421bf5bf1f994a29d0a69b452fcd604578452952af","observation_id":"5877a31c-88e8-49ac-adbd-97fd71a54401","resolution":{"observed_at":"2026-05-19T14:27:23.814389Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-05-19T15:23:32.353823+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-19T15:23:32.353823+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Cbam: Convolutional block attention module, in: Proceedings of the European conference on computer vision (ECCV), pp","venue":null,"work_id":"649dd31b-b80d-4d4e-8573-d7fd7fedac1c","year":2018},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:a21f182139b4b8d9cdb67fa6d672bb81656dc65cb23afc77c206ce21c4cf668f","observation_id":"ae62d576-af6b-46a1-b2f1-9054a0226a60","resolution":{"observed_at":"2026-05-19T14:27:24.499340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Fista-net: Learning a fast iterative shrinkage thresholding network for inverse problems in imaging","venue":null,"work_id":"ad50b650-8c33-4d99-9695-31a9aba3cdf1","year":2021},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:a7a98ec0fa482fd15c9e17d1ea32adc1ce6aac2db7ef405526711bec541ac9f9","observation_id":"c4e080ea-8ec8-4e70-b95a-792f22a892cb","resolution":{"observed_at":"2026-05-19T14:27:24.509186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening","venue":null,"work_id":"92a21b58-c7a9-472f-99c7-2c3d6674fdad","year":2018},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:c148ad5a4fd25f3b24aa6f4586efcdb2a12769b068437e32b2401b8a0d271149","observation_id":"d4f0d4ab-7547-4c59-8135-d51a0b03c5b6","resolution":{"observed_at":"2026-05-19T14:27:24.493504Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1612.03928","last_updated":"2017-02-12T22:05:47Z","snapshot_observed_at":"2026-07-06T05:22:25.273447Z","submitted_at":"2016-12-12T21:15:57Z","title":"Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer","version":3},"cited_work":{"arxiv_id":"1612.03928","doi":"10.48550/arxiv.1612.03928","metadata_source":"pith","pith_arxiv_id":"1612.03928","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer","venue":"cs.CV","work_id":"1e2bd9b4-867c-484f-bb3d-f2f9be85419a","year":2016},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"cited_paper":"/paper/1612.03928","citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:de5c256a45f061187317d564cce498e99807acf2235dd94a59579f42e102936c","observation_id":"ae11759b-4bd8-452c-a267-4b07d1db848c","resolution":{"observed_at":"2026-05-19T14:27:24.086626Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T19:50:22.086713+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T19:50:22.086713+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Restormer: Efficient transformer for high-resolution image restoration, in: IEEE/CVF CVPR, pp","venue":null,"work_id":"7c49106f-334e-46af-98ec-e9b53bf98ff8","year":2022},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:aaef58184165647b7b13d8fb12caa0db942ff2ad407875bccd393ff1779a1f12","observation_id":"e2d53299-eb65-499b-b3ae-189ef51b4745","resolution":{"observed_at":"2026-05-19T14:27:24.511289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Learning nonlocal sparse and low-rank models for image compressive sensing: Nonlocal sparse and low-rank modeling","venue":null,"work_id":"721d09f8-3951-4a7c-a9d2-058afd860219","year":2023},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:7fe6cecf752113688d129e0b87b8d65437acf64cb28cb6e0d1e973e483a1177b","observation_id":"f8abe46e-9657-4c07-add9-8a51e41b772f","resolution":{"observed_at":"2026-05-19T14:27:24.485916Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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":"2026-06-05T21:23:00.469572Z","title":"Plug-and-play image restoration with deep denoiser prior","venue":null,"work_id":"69dd022b-63d4-4b9d-9659-fed972153923","year":2021},"citing_paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:58.288381Z"},"links":{"citing_paper":"/paper/2605.15456"},"observation_digest":"sha256:7a8b9dac5e188023accca10d56f1d0aa08127f0084551931ff1dc2557019632d","observation_id":"73d814c8-a9b7-4dba-892c-2b43fddfab53","resolution":{"observed_at":"2026-05-19T14:27:24.487865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.15456","last_updated":"2026-05-14T22:39:21Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-07-06T23:26:46.595393Z","submitted_at":"2026-05-14T22:39:21Z","title":"DIPA: Distilled Preconditioned Algorithms for Solving Imaging Inverse Problems"},"reference_resolution":{"displayed":76,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":13,"verified_fuzzy":60},"total_outbound_references":76},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 0 inbound Pith citation observations for arXiv:2605.15456."}