{"as_of":"2026-08-14T20:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:eabed843f8ed184ced61463fe76569f5eeabf1018090c0b1b8053a4cfb642aa1","coverage":[{"denominator":123,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T19:32:10.705712Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2411.10649/citation-record","integrity":"/paper/2411.10649/integrity","json":"/paper/2411.10649/citation-record.json","paper":"/paper/2411.10649"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2103.02690","last_updated":"2021-03-05T05:59:07Z","snapshot_observed_at":"2026-08-13T20:04:40.121160Z","submitted_at":"2021-03-03T21:17:06Z","title":"A comprehensive survey on point cloud registration","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.02690","snapshot_observed_at":"2026-08-12T19:32:09.993814Z","title":"A comprehensive survey on point cloud registration,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:09.993814Z"},"links":{"cited_paper":"/paper/2103.02690","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:76c2e5e3f19a19cf47020a389d5f1aa02ca61f4ed72461c074a8d876aafbfcc3","observation_id":"bf03b438-2235-4708-b39f-f82c5bf59cd2","resolution":{"observed_at":"2026-08-12T19:32:09.993814Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.000887Z","title":"Least-squares fitting of two 3-d point sets,","venue":null,"work_id":null,"year":1987},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.000887Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:92c3fa038ae42f4fb83a79aaea7c37a8402945b93df38daaabf99070c21c9eab","observation_id":"ab411911-31b8-4503-99e3-52b47e9d08c4","resolution":{"observed_at":"2026-08-12T19:32:10.000887Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.006995Z","title":"Prnet: Self-supervised learning for partial- to-partial registration,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.006995Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:9c660319b1c3264acf191842b57f5c7054deaa225986e56157cae51a1ab068b5","observation_id":"fd728c6f-ad29-4a53-9345-9b6811d072b5","resolution":{"observed_at":"2026-08-12T19:32:10.006995Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.013167Z","title":null,"venue":null,"work_id":null,"year":1980},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.013167Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:afde873960cefbd7ac713ffdd928bb0c36ccca7a646490cef305d49bbfdaccd5","observation_id":"8001cc0d-85ce-47d1-99cd-c0042b920265","resolution":{"observed_at":"2026-08-12T19:32:10.013167Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.021540Z","title":"A survey of optimization methods from a machine learning perspective,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.021540Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:d597e95aceb2e8aa72e748219f26312acae35547c9d5a07a3e8a8c48cc59afc7","observation_id":"98c6274a-5c3a-4ee7-8109-48e827e7bce9","resolution":{"observed_at":"2026-08-12T19:32:10.021540Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.06188","last_updated":"2021-11-26T17:35:36Z","snapshot_observed_at":"2026-08-13T20:47:51.155152Z","submitted_at":"2020-12-11T08:28:51Z","title":"Recent Theoretical Advances in Non-Convex Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.06188","snapshot_observed_at":"2026-08-12T19:32:10.026686Z","title":"Recent theoretical advances in non- convex optimization,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.026686Z"},"links":{"cited_paper":"/paper/2012.06188","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:0f8aa106a017d592f13e3cb633e1b6e6c4403c95c891a16f859d32f723110185","observation_id":"5fd7cef2-188b-4d7e-9ade-b9e2321b4255","resolution":{"observed_at":"2026-08-12T19:32:10.026686Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.034183Z","title":"The power of convex relaxation: Near-optimal matrix completion,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.034183Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:37712547524b4056f3031711529c09427eb420db3691a91bdd7c7f8878c96f51","observation_id":"2f8d6a29-0150-45d3-af71-27d2ac27ea8f","resolution":{"observed_at":"2026-08-12T19:32:10.034183Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.041006Z","title":"Non-convex optimization for machine learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.041006Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:1a64233818888c60d87d4eaa72d0f1cffdb83101c6d558f7aa4c69eb4a37e888","observation_id":"c057be11-783b-4cb0-aa6a-084db0e4b90f","resolution":{"observed_at":"2026-08-12T19:32:10.041006Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.046427Z","title":"Exact matrix completion via convex opti- mization,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.046427Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:cdee4030096f787acf5ff9aaa8b2d3da6d604f50e912532e560f47762bce4f8f","observation_id":"5d1d2948-ad54-4c62-b5f3-96ed8866aaa4","resolution":{"observed_at":"2026-08-12T19:32:10.046427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.052252Z","title":"An alternative view: When does sgd escape local minima?","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.052252Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:b7e44247c0b655a19b9b4f88a80bca206a879fbf133ab9529e66daed5023d89a","observation_id":"6f9af533-69bc-4000-a1f6-d434f9dacb4d","resolution":{"observed_at":"2026-08-12T19:32:10.052252Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.061128Z","title":"Visualizing the loss landscape of neural nets,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.061128Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:3bea00abf31705422beb4602f2d11f3bcc0cd615426990d4a9262546a3e7ebfe","observation_id":"1fac5cf2-c928-4c87-961c-7c71e272a07b","resolution":{"observed_at":"2026-08-12T19:32:10.061128Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.066596Z","title":"SGD converges to global minimum in deep learning via star-convex path,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.066596Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:69e064cba31d8074b5024f41b440552eb59dd8b7edad0533816a3de26004fde9","observation_id":"f36413c2-99f2-4cb1-a872-3c333b5dbabd","resolution":{"observed_at":"2026-08-12T19:32:10.066596Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.072949Z","title":"Near-optimal methods for min- imizing star-convex functions and beyond,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.072949Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:d8ad5d8a01b9701d8d36f33c0f6f698207b2cf79ff6aecdd5748fc8906c0f25f","observation_id":"c4737393-3722-448c-991b-7862121d4c9f","resolution":{"observed_at":"2026-08-12T19:32:10.072949Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12076","last_updated":"2018-05-30T16:50:28Z","snapshot_observed_at":"2026-08-14T19:09:24.654539Z","submitted_at":"2018-05-30T16:50:28Z","title":"Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.12076","snapshot_observed_at":"2026-08-12T19:32:10.080171Z","title":"To- wards understanding the role of over-parametrization in generalization of neural networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.080171Z"},"links":{"cited_paper":"/paper/1805.12076","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:1f44b7dd11cdab10060597a62ddfa85dd85e5e29a9f8d555cd8c8eee0ef60385","observation_id":"ed32c27d-6a20-4893-a39f-b213d2a7386f","resolution":{"observed_at":"2026-08-12T19:32:10.080171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.085538Z","title":"Cubic regularization of newton method and its global performance,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.085538Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:9fec9f3144c1212cc8b804d99ac3668a55c023856731921026c8a5f8aec2c413","observation_id":"29243b95-c66b-42ba-9b2b-2cad744e2bfc","resolution":{"observed_at":"2026-08-12T19:32:10.085538Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.091513Z","title":"Optimizing star-convex functions,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.091513Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:c1bef4a45fbed43e793b66f47f3a04ec416dfbd6cbf25f2fc7ce8e314e26624e","observation_id":"8b3b0b58-6e49-4e49-848a-c826f794e170","resolution":{"observed_at":"2026-08-12T19:32:10.091513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.096907Z","title":"Near-optimal methods for minimizing star-convex functions and beyond,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.096907Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:047c9d25e9f2699e4a15f7772ba1626d69ac14d0fdb321dab0f57ed07fc3006d","observation_id":"cd5a1c78-fb88-47f0-b75e-91eba87001f8","resolution":{"observed_at":"2026-08-12T19:32:10.096907Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.103965Z","title":"Sgd for structured nonconvex functions: Learning rates, minibatching and interpolation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.103965Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:5f0cd86eeb7be597bd9dd1b0c2bc81420994d9f993b4962db0ed2388d765eb87","observation_id":"9d5d1965-fcdf-476c-844b-121aa8f7a44f","resolution":{"observed_at":"2026-08-12T19:32:10.103965Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.121680Z","title":"Sequential subspace optimization for quasar-convex optimization problems with inexact gradient,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.121680Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:1aa84d1b9d7e8e9e8bfb09a450de4c10cc531ce3532da571f7eb9b722613ee4c","observation_id":"3e317dd3-bb88-4661-985f-b432dde7bd6b","resolution":{"observed_at":"2026-08-12T19:32:10.121680Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.130878Z","title":"Prise: Demystifying deep lucas- kanade with strongly star-convex constraints for multimodel image alignment,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.130878Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:2c3f4938ccbf21fc33607f63273d22d3081f4ff9c74cf658615aff88186904aa","observation_id":"6d33d787-d751-4a50-a32c-fde845402190","resolution":{"observed_at":"2026-08-12T19:32:10.130878Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.139776Z","title":"Adversarial weight perturbation helps robust generalization,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.139776Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:8af73c2df85683a90eec2cd4cf6f50e8d01447ce85710208bd7a261e00f64e4a","observation_id":"2fc78480-38e6-4538-aca8-1225c8bfe3b9","resolution":{"observed_at":"2026-08-12T19:32:10.139776Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15133","last_updated":"2021-11-30T05:29:46Z","snapshot_observed_at":"2026-08-13T17:25:03.371549Z","submitted_at":"2021-11-30T05:29:46Z","title":"LossPlot: A Better Way to Visualize Loss Landscapes","version":1},"cited_work":{"arxiv_id":"2111.15133","doi":null,"metadata_source":"pith","pith_arxiv_id":"2111.15133","snapshot_observed_at":"2026-08-12T19:32:11.509053Z","title":"LossPlot: A Better Way to Visualize Loss Landscapes","venue":"cs.LG","work_id":"9319a9fb-bd0c-4198-88fe-397326507571","year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.145302Z"},"links":{"cited_paper":"/paper/2111.15133","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:9a9d4858fff79e86ec8c72b586c2fe71dcd5aaec8dcf98a004ae04f5230e67ca","observation_id":"998774f4-6b1d-4fb8-9de4-65d5d422abf5","resolution":{"observed_at":"2026-08-12T19:32:11.514759Z","resolver_source":"local_arxiv","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.02757","last_updated":"2020-06-25T03:57:04Z","snapshot_observed_at":"2026-08-14T11:36:54.185930Z","submitted_at":"2019-12-05T17:48:18Z","title":"Deep Ensembles: A Loss Landscape Perspective","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.02757","snapshot_observed_at":"2026-08-12T19:32:10.151837Z","title":"Deep ensembles: A loss landscape perspective,","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.151837Z"},"links":{"cited_paper":"/paper/1912.02757","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:7f0e716198d5a7f7897e38d2cfa6866987af4ae6213b95febeb96c64ac99d182","observation_id":"2ac440c7-7f62-4ab7-bf67-45782f0580e9","resolution":{"observed_at":"2026-08-12T19:32:10.151837Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.165494Z","title":"Exploring the landscape of spatial robustness,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.165494Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:7dbdd76ef647b2d2757b7cda04c460b9a3aab0e4ca27ae8ac1cae92fe392d7fe","observation_id":"a1c33b3f-00da-401b-8553-942c79a0012c","resolution":{"observed_at":"2026-08-12T19:32:10.165494Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1804.10200","last_updated":"2018-04-26T17:58:45Z","snapshot_observed_at":"2026-08-14T19:21:30.108863Z","submitted_at":"2018-04-26T17:58:45Z","title":"The loss landscape of overparameterized neural networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.10200","snapshot_observed_at":"2026-08-12T19:32:10.175013Z","title":"The loss landscape of overparameterized neural networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.175013Z"},"links":{"cited_paper":"/paper/1804.10200","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:587f01c4b81c8b8d7298d1eb43969557f08e8d56a9621cf4499a5dd1f04984be","observation_id":"745fd17a-a398-4671-b6dd-1169524ae645","resolution":{"observed_at":"2026-08-12T19:32:10.175013Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.188206Z","title":"Geometry of the loss landscape in overparameterized neural networks: Symmetries and invariances,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.188206Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:37e630ac085a51477680c55e5e65958f80f65ea5135ff4ad4b0de470b0206358","observation_id":"9ee0ff08-c419-4d61-b250-058ca0b76446","resolution":{"observed_at":"2026-08-12T19:32:10.188206Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.196295Z","title":"Embedding principle of loss landscape of deep neural networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.196295Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:7e33ab79c71844e2add650eaac823c364170e5aed5305ac44f96827a1d3aacf3","observation_id":"7c53ed15-714b-4fff-9a2b-528b25f27e99","resolution":{"observed_at":"2026-08-12T19:32:10.196295Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.206582Z","title":"The global landscape of neural networks: An overview,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.206582Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:e4481e095b69c8f553945b35a4becd4e0b47e72e1735d4b4a983f86b6eb242dd","observation_id":"8a75dfde-08db-4dc5-9aea-0f3c809feda8","resolution":{"observed_at":"2026-08-12T19:32:10.206582Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.212917Z","title":"Low nonconvexity-rank bilinear matrix inequalities: algorithms and applications in robust controller and struc- ture designs,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.212917Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:209184d55297e5436db7bf71581a86306d35cf47772cdfb73261c92b2a472eee","observation_id":"e4f5e93c-8224-447b-a98c-2d8067a0f07b","resolution":{"observed_at":"2026-08-12T19:32:10.212917Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1305.2436","last_updated":"2015-01-01T21:24:26Z","snapshot_observed_at":"2026-07-06T03:13:08.675100Z","submitted_at":"2013-05-10T20:46:41Z","title":"Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima","version":2},"cited_work":{"arxiv_id":"1305.2436","doi":null,"metadata_source":"pith","pith_arxiv_id":"1305.2436","snapshot_observed_at":"2026-08-12T19:32:11.419684Z","title":"Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima","venue":"math.ST","work_id":"87c579f7-25f1-4691-842e-0a71a5c0c238","year":2013},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.219062Z"},"links":{"cited_paper":"/paper/1305.2436","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:9040d681023a944ca776b4bc210693ca380db41966ed83f066921f6d99070cbc","observation_id":"60261775-68b9-483b-ac41-f5671572fb15","resolution":{"observed_at":"2026-08-12T19:32:11.426145Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.225556Z","title":"Regularized m-estimators with nonconvexity: Statistical and algorithmic theory for local optima,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.225556Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:2f8a7b21d47a6c0cedb65db106261df52390cd0fcd1ba842406352f68c8bf0fa","observation_id":"e1e5f899-c443-4d95-804d-6cb7cda0ff35","resolution":{"observed_at":"2026-08-12T19:32:10.225556Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.232590Z","title":"Graduated non- convexity for robust spatial perception: From non-minimal solvers to global outlier rejection,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.232590Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:fc8fa47c3ee888a15f4835076f03e34e83f8a10956d14cdc7c58199eaee4fb8b","observation_id":"6f4555a0-6c07-4b95-8960-0afecf8935e4","resolution":{"observed_at":"2026-08-12T19:32:10.232590Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.13632","last_updated":"2020-12-25T21:51:48Z","snapshot_observed_at":"2026-07-06T10:27:54.164256Z","submitted_at":"2020-12-25T21:51:48Z","title":"Adaptively Solving the Local-Minimum Problem for Deep Neural Networks","version":1},"cited_work":{"arxiv_id":"2012.13632","doi":null,"metadata_source":"pith","pith_arxiv_id":"2012.13632","snapshot_observed_at":"2026-08-12T19:32:11.384731Z","title":"Adaptively Solving the Local-Minimum Problem for Deep Neural Networks","venue":"cs.LG","work_id":"4c89880c-60ef-4dca-9bfc-3d9458892313","year":2020},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.238336Z"},"links":{"cited_paper":"/paper/2012.13632","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:2fc02c319b8acbe4f2c10c5ceaff38ab572dde3ef9c3833a918729ad7a53c308","observation_id":"7ee423d7-97f9-4be1-9a69-f185a22156d3","resolution":{"observed_at":"2026-08-12T19:32:11.391815Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.245555Z","title":"Successive convexification of non-convex optimal control problems and its convergence properties,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.245555Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:d101970ac91f5f34f7d674e93b921cf92f7c4e6de07548024d6e77900806c570","observation_id":"31dc430b-a234-4e01-a1b9-e8fea8156255","resolution":{"observed_at":"2026-08-12T19:32:10.245555Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.252410Z","title":"Adaptive meth- ods for nonconvex optimization,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.252410Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:c1f0617ebbb678586241007222d3112679b19676a989ef6a37059575618d33ee","observation_id":"fc400d11-aaad-4d9b-a111-59ea8e426e41","resolution":{"observed_at":"2026-08-12T19:32:10.252410Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.05142","last_updated":"2020-11-19T19:56:37Z","snapshot_observed_at":"2026-07-06T08:20:55.549794Z","submitted_at":"2019-09-11T15:32:44Z","title":"Regularized deep learning with nonconvex penalties","version":4},"cited_work":{"arxiv_id":"1909.05142","doi":null,"metadata_source":"pith","pith_arxiv_id":"1909.05142","snapshot_observed_at":"2026-08-12T19:32:11.341315Z","title":"Regularized deep learning with nonconvex penalties","venue":"stat.ML","work_id":"b4336517-5341-4ed1-b4ec-4497cd7ee7b4","year":2019},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.258575Z"},"links":{"cited_paper":"/paper/1909.05142","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:d7f25c4c52f36092334880ed3bdff496ae5b9babe9d2eb9553ec0e004b9b1bbe","observation_id":"f710941c-e1e6-440e-8288-13d6e23c7888","resolution":{"observed_at":"2026-08-12T19:32:11.351464Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.264513Z","title":"Learning a similarity metric discriminatively, with application to face verification,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.264513Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:2be94626b6313787b31d37989106e71afb4a20ee2125d159a6dc637eccd59402","observation_id":"1dc4c968-423e-49fc-a30d-8304a56856b1","resolution":{"observed_at":"2026-08-12T19:32:10.264513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.269393Z","title":"Dimensionality reduction by learning an invariant mapping,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.269393Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:38f66177997fb65d2a0514fcb2af61193b5b0c0e684b437a44427d7fab483ed1","observation_id":"35124341-4706-499a-9056-8222ecc499d1","resolution":{"observed_at":"2026-08-12T19:32:10.269393Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-08-14T18:53:38.574749Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-08-12T19:32:10.280352Z","title":"Representation learning with contrastive predictive coding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.280352Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:8fedb9e67a5d04df81d80191d09ce1017845b5e01319721e2720a8cff0607891","observation_id":"004b4c1e-0a74-4cda-b388-968427235850","resolution":{"observed_at":"2026-08-12T19:32:10.280352Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.287024Z","title":"Contrastive multiview coding,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.287024Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:0a0c7ffa8c3546a696a68aed86838a8a07b6a32596451182e5c5e21cf019cc01","observation_id":"8bd7a589-7514-4c8f-99f4-4d56b9862bfe","resolution":{"observed_at":"2026-08-12T19:32:10.287024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.295501Z","title":"A simple framework for contrastive learning of visual representations,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.295501Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:b21202027103ea2eea6a260afbf92d103d4636c696109098c454cbb412ab2c2c","observation_id":"c057d541-fe7f-4c4f-9fef-296d16941004","resolution":{"observed_at":"2026-08-12T19:32:10.295501Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.300929Z","title":"Momentum contrast for unsupervised visual representation learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.300929Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:ac918dfb38974e4121e88da6adde7718b8714c9848e394121bd0d57876cd378a","observation_id":"bba0b00f-a9a9-42cf-a4aa-05847f2ff549","resolution":{"observed_at":"2026-08-12T19:32:10.300929Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.308187Z","title":"Understanding contrastive representation learning through alignment and uniformity on the hypersphere,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.308187Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:988c9fc0a043becdbdd5c57e9a891e4bc6b431f52059dca41fcad4f9f65f5f5c","observation_id":"13a35662-f939-4ab4-b524-6e73855430de","resolution":{"observed_at":"2026-08-12T19:32:10.308187Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.313875Z","title":"Understanding the behaviour of contrastive loss,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.313875Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:44d618351da1f531370e049ff248c92b658b12653725d9046b3a0e747b3e9fb2","observation_id":"487d6567-9857-4298-9082-7d341d03c1a7","resolution":{"observed_at":"2026-08-12T19:32:10.313875Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.321142Z","title":"Contrastive learning inverts the data generating process,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.321142Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:d29453a74ea8d757d99b237f15a65315170c221f568f6dc38bdb3fbe38f74b44","observation_id":"2d08ea07-93da-4908-8d66-fafa00239f71","resolution":{"observed_at":"2026-08-12T19:32:10.321142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.327310Z","title":"Contrastive boundary learning for point cloud segmentation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.327310Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:512a67cfa1176448a9fc8ab4cd287a0db7f42373b34ca1af7506ae5420db2779","observation_id":"918cc221-2ab9-49eb-b028-bdba651d9f5a","resolution":{"observed_at":"2026-08-12T19:32:10.327310Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.334044Z","title":"Unsupervised point cloud object co-segmentation by co-contrastive learning and mutual attention sampling,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.334044Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:ee799dc479ede4c0511061f9afab22ece834131f90ebae1914d5b2f783eb23e7","observation_id":"bc4bcb16-d320-4947-8f31-2a091905b3ee","resolution":{"observed_at":"2026-08-12T19:32:10.334044Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.340245Z","title":"Contrastive representation learning: A framework and review,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.340245Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:c77ba20871f9e075eebeb12eab7efcb8dedec2bfdc265fc3a221a5774e11d462","observation_id":"80110163-5b03-4ae7-890b-e16a98e865b3","resolution":{"observed_at":"2026-08-12T19:32:10.340245Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.349082Z","title":"Omnet: Learning overlapping mask for partial-to-partial point cloud registration,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.349082Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:c964c7f85a2e6e14ba75436dbed9c2cba430182e812e08f99d83f57ea5c3b003","observation_id":"09c5970c-8df4-4b8e-8934-dc408b61cb0f","resolution":{"observed_at":"2026-08-12T19:32:10.349082Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.385235Z","title":"Pointnetlk revisited,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.385235Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:01d0e99ac1290f2390a327fa3082034b37530ca7911ee9db3b1d279a45260efd","observation_id":"48c77d83-1321-439c-bd3b-186b6cf62a95","resolution":{"observed_at":"2026-08-12T19:32:10.385235Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.392631Z","title":"Geometric trans- former for fast and robust point cloud registration,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.392631Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:351eb7452e9b0471004295f148e8cb1a9a7ed708431e2c7c0ec78eae26609b68","observation_id":"6f1aeae3-c0f5-40f0-9dc2-5ff400bb4d77","resolution":{"observed_at":"2026-08-12T19:32:10.392631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.399863Z","title":"Regtr: End-to-end point cloud correspon- dences with transformers,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.399863Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:d7452c55b29ac7a1a34c121f73dc50bac9611492eeefa3d0f729e4d984f1d860","observation_id":"e4d29341-e172-4129-ae52-0a11bcc91939","resolution":{"observed_at":"2026-08-12T19:32:10.399863Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.408468Z","title":"Efficient sparse icp,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.408468Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:b498af0b0bbb63fd184350a9c371b448e6f582ee9611505d5a86d20e7ae26388","observation_id":"5d8cfd6c-a4c7-4dfb-8d5b-fa038ecdf81b","resolution":{"observed_at":"2026-08-12T19:32:10.408468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.392915Z","title":"Outlier robust icp for minimizing fractional rmsd,","venue":null,"work_id":"18101b6b-3173-4607-9d5c-e4673c07f5fd","year":2007},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.421413Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:09b2781f35eca6f1a8416d528ced75d77051a73e5e04726e40f1aca3ce83df9b","observation_id":"05b55700-478d-481a-bdac-395d5f2391ec","resolution":{"observed_at":"2026-08-12T19:32:12.398892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.377160Z","title":"A new point matching algorithm for non- rigid registration,","venue":null,"work_id":"8da8fd04-a8df-48e3-b433-3343ad8c85b8","year":2003},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.426407Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:423fe2768ae325ccc7bed7d6525cede830fe933c0980f7e3896d00abae973c3e","observation_id":"20c0468e-7f0d-4f54-94b8-6d0c64521fdc","resolution":{"observed_at":"2026-08-12T19:32:12.382188Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.08588","last_updated":"2019-06-30T15:37:56Z","snapshot_observed_at":"2026-08-14T16:59:20.194747Z","submitted_at":"2019-03-20T16:16:28Z","title":"A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.08588","snapshot_observed_at":"2026-08-12T19:32:10.434159Z","title":"A polynomial-time solution for robust reg- istration with extreme outlier rates,","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.434159Z"},"links":{"cited_paper":"/paper/1903.08588","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:c23b0bd7ae681a178f8b8788996f953230b40eb46fdda8727e85b6530e97b244","observation_id":"4c14a5c0-3076-4165-a9d9-043cc9147b01","resolution":{"observed_at":"2026-08-12T19:32:10.434159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.361998Z","title":"Teaser: Fast and certifiable point cloud registration,","venue":null,"work_id":"7cc5d370-99ce-4e46-a841-c8ddc234b7c6","year":2020},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.443702Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:fe9c6c8cd71e0a95733306a3e341cc542fda4b472be6b4c62e07bfc28d89e565","observation_id":"6bbc4e33-8f0a-4694-b097-dbe0cd6366be","resolution":{"observed_at":"2026-08-12T19:32:12.366897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.346315Z","title":"Pointnetlk: Robust & efficient point cloud registration using pointnet,","venue":null,"work_id":"26ba008b-d72c-4081-abfd-81255aeb1724","year":2019},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.450571Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:71bae935587b0b9eb124d63f2e21e2cc994959562c707cce94c54f6ff039cae7","observation_id":"f89088dd-527b-4074-b1b6-08711123dc6a","resolution":{"observed_at":"2026-08-12T19:32:12.351361Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.328707Z","title":"Deep closest point: Learning represen- tations for point cloud registration,","venue":null,"work_id":"4bba2a3a-cc4a-4b58-bad7-e0892542f164","year":2019},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.455915Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:67e266c764ebbb091ade9e81acb971ee88114450e6ff8603a7100f191149bbb7","observation_id":"5c1d8fa4-ed1d-475e-93c8-600e63090422","resolution":{"observed_at":"2026-08-12T19:32:12.334642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.07906","last_updated":"2019-11-04T04:52:47Z","snapshot_observed_at":"2026-08-14T11:51:31.943384Z","submitted_at":"2019-08-21T15:10:24Z","title":"PCRNet: Point Cloud Registration Network using PointNet Encoding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.07906","snapshot_observed_at":"2026-08-12T19:32:10.461379Z","title":"Pcrnet: Point cloud registration network using pointnet encoding,","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.461379Z"},"links":{"cited_paper":"/paper/1908.07906","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:661b6807acc7ef09c2227c470f9cd3997baf0e498cd580c917660c1786cc82f5","observation_id":"d0c4c6c9-5251-4c2d-850a-4ada54d21a37","resolution":{"observed_at":"2026-08-12T19:32:10.461379Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.312243Z","title":"Rpm-net: Robust point matching using learned features,","venue":null,"work_id":"f59f6879-cfdf-4081-9974-f45ea20f28d9","year":2020},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.467253Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:a760917205bab303821d92ef2eaa456e12043d9de6aada8eddfbbb41544ae022","observation_id":"d94aa56a-9311-463c-a78d-0e9689a5ccda","resolution":{"observed_at":"2026-08-12T19:32:12.317277Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.296015Z","title":"Deep global registration,","venue":null,"work_id":"42f5465c-0864-4063-8d0b-33aa8404d53e","year":2020},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.472677Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:c784a0dea9f1f5792642e30d97e2c4fbdb92215c6e184dfa7fa98abecfb0230a","observation_id":"8b9d0ba0-14eb-46f1-bb7e-e7439baa8ae2","resolution":{"observed_at":"2026-08-12T19:32:12.301206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.280220Z","title":"Predator: Registration of 3d point clouds with low overlap,","venue":null,"work_id":"bdffcd4f-55d7-4c07-b9c3-b7247b76c94a","year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.478318Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:83ca835ff1c8ac3ec39a5849b175d79738ac95da5cc4d26dba36c51a59cbac2b","observation_id":"4f7b2538-d958-44d8-9096-b73817ee5596","resolution":{"observed_at":"2026-08-12T19:32:12.285422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.263989Z","title":"Deep learning based point cloud registra- tion: an overview,","venue":null,"work_id":"c47cbb0e-a5eb-4d5e-a01b-1caa5a808b79","year":2020},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.486714Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:cd6fd97c9780d68a2907aae81b4135f5e0785288cf5d6f39d4e5f493bda8e61e","observation_id":"456db996-908a-4f50-bd67-8c744d4b656a","resolution":{"observed_at":"2026-08-12T19:32:12.269099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.246252Z","title":"Robust registration of multimodal remote sensing images based on structural similarity,","venue":null,"work_id":"1a1ff72b-309d-438e-8b91-0acdf6aba66b","year":2017},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.495012Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:9b1abf7492527847f1ae27713a100a9c48b6a1c487ff2048dab0572d96f1213e","observation_id":"96f417d6-b610-46a2-93e3-123787ccfc70","resolution":{"observed_at":"2026-08-12T19:32:12.251353Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.231324Z","title":"Adaptive context network for scene parsing,","venue":null,"work_id":"4f524a72-573f-4e25-9bc3-4df98c3bb69e","year":2019},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.505554Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:73493febafa7ede2ee6f43545b5314971eff8a0123bfe4ad3b803bb38d515195","observation_id":"277ab462-2a3a-47ab-bf74-aec7c926a98c","resolution":{"observed_at":"2026-08-12T19:32:12.236113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.215520Z","title":"Fast and robust matching for multimodal remote sensing image registration,","venue":null,"work_id":"86919fd0-5661-45d1-879e-1fd74ad61e7c","year":2019},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.511929Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:bb46be849a2bb955d6a4a38e58a6cf4fef19198a1001efd14fb863df4a5e5db0","observation_id":"0fcad4ed-9356-4db8-b54c-284387b351b1","resolution":{"observed_at":"2026-08-12T19:32:12.220735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.199490Z","title":"Homography estimation from image pairs with hierarchical convolutional networks,","venue":null,"work_id":"09519ad3-a58a-47d4-873c-4073f0dd3306","year":2017},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.517997Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:33c1ecf2c31123ce9a01f2dcf681b2df05e19f2b6874323387b6a2be0d1d3e35","observation_id":"fd86a1f5-73d7-4ad9-89b2-c3c7d7242e73","resolution":{"observed_at":"2026-08-12T19:32:12.204774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.183427Z","title":"Unsupervised deep homography: A fast and robust homography esti- mation model,","venue":null,"work_id":"86e6c9bd-63e3-484b-aa1e-44af800c5e77","year":2018},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.525041Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:9aaf9c2b104aed66e705319b3a1e4481cb445f64c0504fe0e19d0b3012386564","observation_id":"5c6f252b-30f4-4498-997f-556b7ffd4a62","resolution":{"observed_at":"2026-08-12T19:32:12.188572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.165754Z","title":"Deep homography estima- tion for dynamic scenes,","venue":null,"work_id":"c6f71671-0528-44b9-811f-0a90e150eea1","year":2020},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.532432Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:341b98c6ffb4e7f091aae813c7f51b4b5746730c1095a1f6faadbc18ed2432c7","observation_id":"ac240b02-b27d-4430-b35d-5d7c0a3bf280","resolution":{"observed_at":"2026-08-12T19:32:12.171679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.148491Z","title":"Content-aware unsupervised deep homography estimation,","venue":null,"work_id":"de90ad54-7bcb-4e87-981b-1d5f80008785","year":2020},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.538098Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:8902ecf72f0735c044fd3dfc80997d049705ea371299bc742113c50b039e8495","observation_id":"db4f7f4b-633c-4db2-bcea-fdc053a07957","resolution":{"observed_at":"2026-08-12T19:32:12.153735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.03798","last_updated":"2016-06-13T02:46:38Z","snapshot_observed_at":"2026-08-08T10:19:49.347959Z","submitted_at":"2016-06-13T02:46:38Z","title":"Deep Image Homography Estimation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.03798","snapshot_observed_at":"2026-08-12T19:32:10.543617Z","title":"Deep image homogra- phy estimation,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.543617Z"},"links":{"cited_paper":"/paper/1606.03798","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:e062ae96907bc37d3be6808b864a74f63c27628645ec5d0a87a8bf1b9be480bb","observation_id":"e4862497-dd9d-44ca-bede-d679656f2cbf","resolution":{"observed_at":"2026-08-12T19:32:10.543617Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.130459Z","title":"Clkn: Cascaded lucas- kanade networks for image alignment,","venue":null,"work_id":"67e66ab1-9e09-4e08-bd32-162e531bf06e","year":2017},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.548978Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:8170322384a253bdad4758a1deceba54c2619d8b7ff4cff8e86fd6c5eeb016f3","observation_id":"1e378ee0-8f25-4170-9138-b84016e6773b","resolution":{"observed_at":"2026-08-12T19:32:12.136133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.113179Z","title":"Deep lucas-kanade homography for multimodal image alignment,","venue":null,"work_id":"f4e6a654-7c5c-4c08-9543-054a0d956795","year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.556613Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:63b8b2077cea55e6294deb828605a733d1c896f5d589cb6cf2954b9179e349c9","observation_id":"50eafd13-5a83-432d-8080-087c3454b27e","resolution":{"observed_at":"2026-08-12T19:32:12.118372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.097123Z","title":"Iterative deep homography estimation,","venue":null,"work_id":"35037d64-e5cb-4162-913b-09c0f0968870","year":2022},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.561344Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:a60f1ed51056689195b293c0639c63d73289904c2d89eeccf831f27dd20a87a4","observation_id":"29423ea6-fa38-4b82-9ea9-dee7cc265f87","resolution":{"observed_at":"2026-08-12T19:32:12.102310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.080842Z","title":"A survey of planar homog- raphy estimation techniques,","venue":null,"work_id":"dd3bf473-db4e-4fd9-b113-2fe877585865","year":2005},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.566643Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:b67d80afc20cd68061345c5d8dcc987f4da093f4748d829ceaf49fbbb6a725b3","observation_id":"1f38ed67-256c-475f-9238-f42ac8c7c711","resolution":{"observed_at":"2026-08-12T19:32:12.086237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.11141","last_updated":"2023-08-30T09:57:57Z","snapshot_observed_at":"2026-08-13T14:58:47.113612Z","submitted_at":"2022-07-22T15:25:59Z","title":"Deep neural networks on diffeomorphism groups for optimal shape reparameterization","version":2},"cited_work":{"arxiv_id":"2207.11141","doi":null,"metadata_source":"pith","pith_arxiv_id":"2207.11141","snapshot_observed_at":"2026-08-12T19:32:11.183769Z","title":"Deep neural networks on diffeomorphism groups for optimal shape reparameterization","venue":"math.OC","work_id":"b4d425a4-e4b6-4ed1-9ed7-435af1a035f8","year":2022},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.571425Z"},"links":{"cited_paper":"/paper/2207.11141","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:dd216c54b651314c3ab150770d01611d7e4e1967d52aebaa5038609dc61e186c","observation_id":"3d1dbd1e-d013-410a-b49e-e775f16b1398","resolution":{"observed_at":"2026-08-12T19:32:11.195136Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.064564Z","title":"Deep reparametrization of multi-frame super-resolution and denoising,","venue":null,"work_id":"8de83b79-6d80-48c9-b958-1b1085c71325","year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.576839Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:06243eaf81948f5fca3320b1ed6edd054aa2501fa2ee6bea38f6933c356fed1f","observation_id":"cbe0c4f8-9753-43fd-a331-57f42e6974ee","resolution":{"observed_at":"2026-08-12T19:32:12.069653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.048213Z","title":"An iterative image registration technique with an application to stereo vision,","venue":null,"work_id":"b92245ff-85be-4a87-adbe-53e70de79c26","year":1981},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.582380Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:742aa6ad936c03aed86e1ae221d4e3abff90d992a534a3f2aa569dc22ca5d3bf","observation_id":"1185f861-e6dd-44a7-8ea6-ffb18a0c39a6","resolution":{"observed_at":"2026-08-12T19:32:12.053480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.587347Z","title":null,"venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.587347Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:173bb074cff70872c5503daf0e75450826a286bef5792d12ef5a9d818b0f4421","observation_id":"20658dd6-32db-41bc-a845-05daa4fb295f","resolution":{"observed_at":"2026-08-12T19:32:10.587347Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.022895Z","title":"Convergence analysis of two-layer neural networks with relu activation,","venue":null,"work_id":"d559dad1-b330-4781-b04f-e4eb3abc80a6","year":2017},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.592935Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:d818062b4d6132f8fdc4caf90e24e60a7c0878a7026e7812d428fed2751d5907","observation_id":"c8d2108e-eecc-4416-9ca0-0c3f0d2bdf27","resolution":{"observed_at":"2026-08-12T19:32:12.027826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:12.005666Z","title":"Smpconv: Self-moving point representations for continuous convolution,","venue":null,"work_id":"c55c8f4c-5e8b-4d0d-b864-ed17372d40b8","year":2023},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.597757Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:addb2a6fc9402546e5c11efd93ef5335bac1c05018fc8585acc25423be04c312","observation_id":"78eb7b1d-9e3e-4c0d-816a-4421f6bf4741","resolution":{"observed_at":"2026-08-12T19:32:12.012056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.00396","last_updated":"2022-08-05T17:54:38Z","snapshot_observed_at":"2026-08-14T01:02:41.198730Z","submitted_at":"2021-10-31T03:32:18Z","title":"Efficiently Modeling Long Sequences with Structured State Spaces","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.00396","snapshot_observed_at":"2026-08-12T19:32:10.602422Z","title":"Efficiently modeling long sequences with structured state spaces,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.602422Z"},"links":{"cited_paper":"/paper/2111.00396","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:1a4acedd0958a7d82976b05392c1a8270779a2817eb2cff8e6ec3ce2f15e1d62","observation_id":"1d725957-d611-4b60-910a-6272ed85db09","resolution":{"observed_at":"2026-08-12T19:32:10.602422Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.08059","last_updated":"2022-03-17T13:20:29Z","snapshot_observed_at":"2026-08-14T16:19:50.244094Z","submitted_at":"2021-10-15T12:35:49Z","title":"FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes","version":3},"cited_work":{"arxiv_id":"2110.08059","doi":null,"metadata_source":"pith","pith_arxiv_id":"2110.08059","snapshot_observed_at":"2026-08-12T19:32:11.139253Z","title":"FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes","venue":"cs.CV","work_id":"c9553401-9cac-40ba-92c3-b6b7645b4cb4","year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.607650Z"},"links":{"cited_paper":"/paper/2110.08059","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:5b9949a6f90d87edf3bb3898ab1c01d26fe65ca6289083a98412c8a4d04f3e89","observation_id":"7bddb3a0-31a0-4aac-a639-ab15550b13d0","resolution":{"observed_at":"2026-08-12T19:32:11.145428Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.612888Z","title":"Combining recurrent, convolutional, and continuous-time models with linear state space layers,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.612888Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:b237b8502f1b45cfe8806395877752904f96dfa31b8280fdcc85f617c8c7aace","observation_id":"4a670ed8-7944-44f4-bef0-eb4094b3c5fe","resolution":{"observed_at":"2026-08-12T19:32:10.612888Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.04744","last_updated":"2022-02-25T15:20:52Z","snapshot_observed_at":"2026-08-11T00:04:07.381761Z","submitted_at":"2021-10-10T09:43:02Z","title":"Long Expressive Memory for Sequence Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.04744","snapshot_observed_at":"2026-08-12T19:32:10.618913Z","title":"Long expressive memory for sequence modeling,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.618913Z"},"links":{"cited_paper":"/paper/2110.04744","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:e0350e92e5f071dba475155d33031880d6d4e61bf50269c004563e9a3c0f9425","observation_id":"ed847549-d00e-459e-9621-8d2e9d2d1b20","resolution":{"observed_at":"2026-08-12T19:32:10.618913Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.06251","last_updated":"2020-12-09T09:19:00Z","snapshot_observed_at":"2026-07-06T08:29:22.074079Z","submitted_at":"2019-10-11T09:43:49Z","title":"Deep Independently Recurrent Neural Network (IndRNN)","version":3},"cited_work":{"arxiv_id":"1910.06251","doi":null,"metadata_source":"pith","pith_arxiv_id":"1910.06251","snapshot_observed_at":"2026-08-12T19:32:11.093779Z","title":"Deep Independently Recurrent Neural Network (IndRNN)","venue":"cs.CV","work_id":"0f298d15-84bb-44a6-b688-af1f51258c92","year":2019},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.624969Z"},"links":{"cited_paper":"/paper/1910.06251","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:36b529f4910195fce1df88ec72ede6113cc647292a22f1d0884ab179dd834d4c","observation_id":"a2c41712-2271-4a82-8187-d248438337e4","resolution":{"observed_at":"2026-08-12T19:32:11.100104Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:11.977309Z","title":"Coupled oscillatory recurrent neural network (cornn): An accurate and (gradient) stable architecture for learning long time dependencies,","venue":null,"work_id":"901f0aca-e772-46e8-852c-33d018add333","year":2010},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.630010Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:e41ea83fe3d37c9bdae1fecf58ba1e988c716f42870dbcaef5a9ae6f61b6e640","observation_id":"ec1f5863-5bc9-44aa-bba6-ef6718ba4ab3","resolution":{"observed_at":"2026-08-12T19:32:11.982356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.12070","last_updated":"2021-04-24T03:31:59Z","snapshot_observed_at":"2026-07-06T09:31:19.187163Z","submitted_at":"2020-06-22T08:44:52Z","title":"Lipschitz Recurrent Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.12070","snapshot_observed_at":"2026-08-12T19:32:10.634787Z","title":"Lip- schitz recurrent neural networks,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.634787Z"},"links":{"cited_paper":"/paper/2006.12070","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:f113e445c896b12711719a22a3765d00e922adf55cc85a71ca2a35e8b571092c","observation_id":"a4a4a58b-f86b-41f3-a620-9d4547c9b573","resolution":{"observed_at":"2026-08-12T19:32:10.634787Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:11.960732Z","title":"Gating revisited: Deep multi-layer rnns that can be trained,","venue":null,"work_id":"bbb30b48-e798-49d6-9fc7-01816ff136c4","year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.640690Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:919a4266758bc8ae817c363b45aee9b7db9c7c5e41412cbde9c64ead6189fa4d","observation_id":"2fe03589-d8a7-44b1-ae09-15b013aca0da","resolution":{"observed_at":"2026-08-12T19:32:11.966353Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.02611","last_updated":"2022-03-17T13:26:32Z","snapshot_observed_at":"2026-08-09T18:30:59.254394Z","submitted_at":"2021-02-04T13:51:19Z","title":"CKConv: Continuous Kernel Convolution For Sequential Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.02611","snapshot_observed_at":"2026-08-12T19:32:10.650530Z","title":"Ckconv: Continuous kernel convolution for sequen- tial data,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.650530Z"},"links":{"cited_paper":"/paper/2102.02611","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:b54ca56a45597799df6b6de16a2bbcef7331104d319329d915455e81fa0615ab","observation_id":"faff8c90-2d5a-4553-b9f8-252104d62a18","resolution":{"observed_at":"2026-08-12T19:32:10.650530Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1603.09025","last_updated":"2017-02-28T00:59:42Z","snapshot_observed_at":"2026-07-06T04:51:04.562947Z","submitted_at":"2016-03-30T02:57:20Z","title":"Recurrent Batch Normalization","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1603.09025","snapshot_observed_at":"2026-08-12T19:32:10.659389Z","title":"Recurrent batch normalization,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.659389Z"},"links":{"cited_paper":"/paper/1603.09025","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:848ab081c890f40449228df1006256d71bd9a95f224751bf3b5596eb0ace6e83","observation_id":"26176e12-8734-4c2b-906f-072462c3589a","resolution":{"observed_at":"2026-08-12T19:32:10.659389Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:11.943856Z","title":"Unitary evolution recurrent neural networks,","venue":null,"work_id":"e0e14819-4536-49c8-bbe6-9aa3dc8e409d","year":2016},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.667453Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:5850511cb9c39e71036f47b1da20ab950f3a5e86ce4889f9c80f08af85b35fdc","observation_id":"4fcd6293-1fd1-4c21-b9a8-6c5eec386b85","resolution":{"observed_at":"2026-08-12T19:32:11.949669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.672719Z","title":"Long short-term memory,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.672719Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:e6e537077e3c185cbb4dd58c19ca187dab8e9f443a5fa3e088a295db4720857b","observation_id":"9d6fde05-2458-4203-92b4-be349966a340","resolution":{"observed_at":"2026-08-12T19:32:10.672719Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.677465Z","title":"MNIST handwritten digit database,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.677465Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:e3d256158f0e2855648b3fe4199271b1d449496f2d846b2a9dc171118c1341f1","observation_id":"6599214c-3825-4d11-bc53-95affce02de5","resolution":{"observed_at":"2026-08-12T19:32:10.677465Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:11.906600Z","title":"3d shapenets: A deep representation for volumetric shapes,","venue":null,"work_id":"87da85ec-cd78-48d8-a542-bf8c7cbc4649","year":2015},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.683349Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:a76911b1358aefc2f1f2d24f58a3b1f4f22c9f2c2414982e8bc852b741b1572f","observation_id":"17dcf888-a962-4993-b1f2-ca2c53f86dec","resolution":{"observed_at":"2026-08-12T19:32:11.912427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1512.03012","last_updated":"2015-12-09T19:42:48Z","snapshot_observed_at":"2026-08-13T14:41:45.148742Z","submitted_at":"2015-12-09T19:42:48Z","title":"ShapeNet: An Information-Rich 3D Model Repository","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1512.03012","snapshot_observed_at":"2026-08-12T19:32:10.689228Z","title":"Shapenet: An information- rich 3d model repository,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.689228Z"},"links":{"cited_paper":"/paper/1512.03012","citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:fd738c72513330a2ad2618d2457438bb0901395d4e4cdb09e4f98e8351a8be13","observation_id":"7f87d749-4f29-4f7b-bf51-d5eedb87120a","resolution":{"observed_at":"2026-08-12T19:32:10.689228Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.695100Z","title":"3dmatch: Learning local geometric descriptors from rgb-d reconstruc- tions,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.695100Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:6532d4db703d6112a8f9fb72178d2079a2cfc5991a8df1d13aa7be94d65167dc","observation_id":"c8808dea-30c1-40ac-80ca-cdf9426d977c","resolution":{"observed_at":"2026-08-12T19:32:10.695100Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:11.875936Z","title":"Fully convolutional geometric fea- tures,","venue":null,"work_id":"b4102f0c-cf34-473a-9846-9352ca667685","year":2019},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.701167Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:746da30ab8c4b53d682c69551dc819a5e01d97a99face48bb6451b02f9b92079","observation_id":"ea89f881-cc9e-4bed-9fcc-f4aa33904c3c","resolution":{"observed_at":"2026-08-12T19:32:11.883709Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:32:10.705712Z","title":"D3feat: Joint learning of dense detection and description of 3d local features,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-12T19:32:10.705712Z"},"links":{"citing_paper":"/paper/2411.10649"},"observation_digest":"sha256:a97dc72ce71cafe7403eeeaf52f64f1b64d444de998f50b2d40a557038d9fcb6","observation_id":"376729d0-4e58-4f15-a11e-9a3ed004d9bf","resolution":{"observed_at":"2026-08-12T19:32:10.705712Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.10649","last_updated":"2024-11-16T01:13:04Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T17:30:16.176921Z","submitted_at":"2024-11-16T01:13:04Z","title":"Deep Loss Convexification for Learning Iterative Models"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":64,"verified_exact":6,"verified_fuzzy":29},"total_outbound_references":123},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 100 of 123 outbound references and 0 inbound Pith citation observations for arXiv:2411.10649."}