{"as_of":"2026-08-08T14:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b2a67eecfeee4055076c4e6207a36422c0410b3d38d98dfc49c425098f51518d","coverage":[{"denominator":44,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":44,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:35:29.880979Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2505.24558/citation-record","integrity":"/paper/2505.24558/integrity","json":"/paper/2505.24558/citation-record.json","paper":"/paper/2505.24558"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:35:25.212355Z","title":"A new convolution neural layer based on weights constraints","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:25.212355Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:c81b8a6a701736b4c1d62097c9b05d44f19c62073b79f555401401cc7bb906a1","observation_id":"0c862815-76d6-43fd-94b8-4a4cf479bdf1","resolution":{"observed_at":"2026-08-07T12:35:25.212355Z","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":"2026-08-07T12:35:36.420010Z","title":"Ntire 2017 challenge on single image super-resolution: Dataset and study","venue":null,"work_id":"5b56e065-5100-45fa-9504-f5fbdb0c948e","year":2017},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:25.285660Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:9b423d2ccaf82fd4e784d8d63be584d04f108c4d852eb6c2a8d87a620939e4c1","observation_id":"6dfbf234-08f6-4ac2-a099-755c596fe0bb","resolution":{"observed_at":"2026-08-07T12:35:36.557264Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.11365","last_updated":"2019-06-08T23:46:25Z","snapshot_observed_at":"2026-08-03T13:56:40.739288Z","submitted_at":"2019-01-30T18:05:47Z","title":"Noise2Self: Blind Denoising by Self-Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.11365","snapshot_observed_at":"2026-08-07T12:35:25.410514Z","title":"Noise2self: Blind denoising by self-supervision","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:25.410514Z"},"links":{"cited_paper":"/paper/1901.11365","citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:90e7c537643a17797f927b86a250715c9ea2c4035739725a1ba8b2a191eb9725","observation_id":"6c5f7544-5a32-446e-894b-2d4d934a71cc","resolution":{"observed_at":"2026-08-07T12:35:25.410514Z","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":"2026-08-07T12:35:36.210512Z","title":"Simple baselines for image restoration","venue":null,"work_id":"e8ff201b-9ee2-431e-8585-968a56712838","year":2022},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:25.533576Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:505cb46960a917c7222fcb01ebae4c2391d041940e64d4613b1c797905d60fe5","observation_id":"7068a67d-6fdd-4833-9159-465867299575","resolution":{"observed_at":"2026-08-07T12:35:36.351169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:35.835960Z","title":"Dynamic convolution: Attention over convolution kernels","venue":null,"work_id":"18bb604b-5f7a-4260-bb84-6821e4b98746","year":2020},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:25.691446Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:630db19c8420dbe834f096c8c0fe6e750c393ce48e622f44b1718e49451ce759","observation_id":"8a51192f-76e6-49ae-990b-4ae6703cb12e","resolution":{"observed_at":"2026-08-07T12:35:35.991706Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:35.415754Z","title":"Convolutional kernel networks for graph-structured data","venue":null,"work_id":"7c884d8c-b0e4-496b-b165-8b5b84a58dbc","year":2020},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:25.837087Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:fb7066500538bca1064fc7356263a3b1e33541a745487ca2ad89ccff5ba76ffa","observation_id":"7e9dff81-1de5-4027-93ec-f18a5e800483","resolution":{"observed_at":"2026-08-07T12:35:35.567959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:35.088670Z","title":"Real-time denoising of ultrasound images based on deep learning","venue":null,"work_id":"67149a94-83c7-4a5a-aec5-e7df916fba67","year":2022},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:25.944765Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:76f8a22067731b2a59b60fa7e21b8e73f11b5aa4b240534a43a0114350f20274","observation_id":"52876c9b-9912-4916-99d3-cb77cd07ec00","resolution":{"observed_at":"2026-08-07T12:35:35.258912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:34.800592Z","title":"Analysis and comparison of high-performance computing solvers for minimisation problems in signal processing","venue":null,"work_id":"74bc24c0-5d0e-4fa8-bf26-f216a0044b43","year":2025},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.046547Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:4dd7a464496079f5f055a34cde79c825bc90a16c4d8bb45df445aeae77530de3","observation_id":"b9cc641c-a038-4f96-9c8d-01d59f457dd5","resolution":{"observed_at":"2026-08-07T12:35:34.959726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:26.123485Z","title":"Weighted convolutional neural network ensemble","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.123485Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:41338a3a36ccb8e9c226c307bdf1463d54d064c7ad67e691eafe0a0d0d72f406","observation_id":"131facff-1c23-4abf-8032-8094acc92c7a","resolution":{"observed_at":"2026-08-07T12:35:26.123485Z","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":"2026-08-07T12:35:34.542205Z","title":"Deep learning for computational chemistry","venue":null,"work_id":"69360ed1-0d49-4c82-8620-e4f6f5414664","year":2017},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.207701Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:ace300b382f313aaaf85d60f64c95a55a76bdf50c6e74870a234a9b8b47276e1","observation_id":"60c10684-c403-4816-b7cf-a92b337af826","resolution":{"observed_at":"2026-08-07T12:35:34.602062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.15235","last_updated":"2024-05-07T16:32:18Z","snapshot_observed_at":"2026-08-04T02:48:48.720010Z","submitted_at":"2024-01-26T22:59:51Z","title":"CascadedGaze: Efficiency in Global Context Extraction for Image Restoration","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.15235","snapshot_observed_at":"2026-08-07T12:35:26.309203Z","title":"Cascadedgaze: Efficiency in global context extraction for image restoration","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.309203Z"},"links":{"cited_paper":"/paper/2401.15235","citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:1b78d0888ac812cbb34d4ad3c0d11ac9f84177e3db249c797a48daf29d362edf","observation_id":"a16df642-2679-44cc-9d3a-d51e92a99d28","resolution":{"observed_at":"2026-08-07T12:35:26.309203Z","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":"2026-08-07T12:35:34.383351Z","title":"Generalizing the convolution operator in convolutional neural networks","venue":null,"work_id":"3e2bbab9-a217-4430-8e6b-f56945a04ced","year":2019},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.414295Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:62f6e174c25b85149b54a8ca02213547250f187f8c9a9a9718b3dfd83def7ef1","observation_id":"29cffc59-63ab-400f-ba31-c1f3c668bb2f","resolution":{"observed_at":"2026-08-07T12:35:34.463419Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:26.515263Z","title":"Weighted channel dropout for regularization of deep convolutional neural network","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.515263Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:ae9fcc68f040672deb56ce7bcc7a12c8b348300eeac489a7ac1b6164d9e7d16c","observation_id":"da1c4aa7-1109-4b92-8670-8f176ac3e191","resolution":{"observed_at":"2026-08-07T12:35:26.515263Z","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":"2026-08-07T12:35:34.143322Z","title":"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification","venue":null,"work_id":"efcf0a21-913d-4766-9f7b-66cee1fff523","year":2015},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.585219Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:754bd889a611bf48cd7fcce7eb7c0ac65e47d81395a9bbff38642bb0ac17f617","observation_id":"4d22760f-2bf6-4c33-bbd8-8c98594839d6","resolution":{"observed_at":"2026-08-07T12:35:34.253173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:26.693284Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.693284Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:f7d4b950dd80d911415487764d822a3b7f89277a15ed4da4ae18aca374383dfe","observation_id":"9c6ba03e-dec0-4426-975b-f68790e991a4","resolution":{"observed_at":"2026-08-07T12:35:26.693284Z","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":"2026-08-07T12:35:33.977664Z","title":"Backpropagation and stochastic gradient descent method","venue":null,"work_id":"886056f7-4f37-4cc9-b488-31b6af5791b5","year":1993},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.812137Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:edaac2e20c72fadd80316df226978aa46d6fb5a0b71fa1961a09f47eff535a0b","observation_id":"7058d6ff-a7d4-4928-a75c-e9388711d9eb","resolution":{"observed_at":"2026-08-07T12:35:34.053291Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:33.824081Z","title":"Variable weight algorithm for convolutional neural networks and its applications to classification of seizure phases and types","venue":null,"work_id":"9c61745e-9006-43f9-bf85-b9094bb9eab9","year":2022},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.911150Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:9a0a1d97647567a6c5c0d11a8ca2a50d3edf3b8377f271118cbed839bba98c32","observation_id":"769795f8-8ea7-431e-a477-5311a11ce8fb","resolution":{"observed_at":"2026-08-07T12:35:33.911969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:33.651429Z","title":"Hyper-parameter optimization of deep learning model for prediction of parkinson’s disease","venue":null,"work_id":"0939e496-917d-4692-ae9f-ba65142a03ef","year":2020},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:26.977361Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:7b7eb371a5ca98fa8e9f972c49853dd8c0922c56b6fe21df24f1b52f40de9ebe","observation_id":"2bd60ddb-a0d5-4e93-93fb-c28da60d7ac2","resolution":{"observed_at":"2026-08-07T12:35:33.745832Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:33.487569Z","title":"Air learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation","venue":null,"work_id":"83f143b6-463c-40cc-8d0a-e62b157b57a2","year":2021},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.075763Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:9f19a5a705e28222bdd9f08f0dfc93a693ee40988cd8e99a3f351761feb47546","observation_id":"fe8a3e90-c71f-4e53-8954-4e96a89271bc","resolution":{"observed_at":"2026-08-07T12:35:33.549872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:33.374470Z","title":"Noise2void-learning denoising from single noisy images","venue":null,"work_id":"2619a8e3-25f3-435a-9afe-6c2c64a6d96c","year":2019},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.190064Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:ceb63781f757eda9f501a4a14068fe9d8054f0924c88810a8b6c5633f06a312d","observation_id":"f40f5e99-6eee-458a-82be-bb328db9b8d7","resolution":{"observed_at":"2026-08-07T12:35:33.424438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:27.321348Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.321348Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:9d3c509ef5ecbc2b9f9b78b502eb262134c0c9be1ffd1600b52fa8cf2518ab6b","observation_id":"b7922a45-a255-4e9e-acb8-f1eeb7d566dc","resolution":{"observed_at":"2026-08-07T12:35:27.321348Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-07T12:35:27.400509Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.400509Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:3cddf3e148ea6ba5250d468caeb049b335db17b68a127d642a51ca5c29831f8e","observation_id":"08f23a47-0a3a-4792-b6b5-23a6d2ce49a4","resolution":{"observed_at":"2026-08-07T12:35:27.400509Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.4400","last_updated":"2014-03-04T05:15:42Z","snapshot_observed_at":"2026-08-02T17:47:13.893077Z","submitted_at":"2013-12-16T15:34:13Z","title":"Network In Network","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.4400","snapshot_observed_at":"2026-08-07T12:35:27.501349Z","title":"Network in network","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.501349Z"},"links":{"cited_paper":"/paper/1312.4400","citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:25147be761bf43753794bd1ac569733c3105463bce16a877d1229a5a03fc1511","observation_id":"72e061e4-6a5d-4333-a359-c485f976635c","resolution":{"observed_at":"2026-08-07T12:35:27.501349Z","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":"2026-08-07T12:35:33.193103Z","title":"Pay attention to mlp s","venue":null,"work_id":"fad37f74-eebb-4392-9758-fa5f8e0e2803","year":2021},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.656709Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:4e0b032fe7beba9f324fb1666038a70a57aab677e85535cebe0fb222ed5519df","observation_id":"50208763-f09c-4830-ac60-764531592009","resolution":{"observed_at":"2026-08-07T12:35:33.295282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.04189","last_updated":"2018-10-29T10:29:23Z","snapshot_observed_at":"2026-07-06T06:27:46.580255Z","submitted_at":"2018-03-12T11:07:58Z","title":"Noise2Noise: Learning Image Restoration without Clean Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.04189","snapshot_observed_at":"2026-08-07T12:35:27.763153Z","title":"Noise2noise: Learning image restoration without clean data","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.763153Z"},"links":{"cited_paper":"/paper/1803.04189","citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:b5c75e97c572f6c28d2373f4fd841e2f4db64659162ca866f4512023423f0d00","observation_id":"b0afc6df-ce2c-41e6-831a-8e07bae413f1","resolution":{"observed_at":"2026-08-07T12:35:27.763153Z","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":"2026-08-07T12:35:27.875719Z","title":"Omni-dimensional dynamic convolution","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.875719Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:fb29c4464f84c64dde13adc17b2fd7938a7d851cb64bb391333eeec1c0127c97","observation_id":"33e42c00-b922-4f94-9239-ee6e15223016","resolution":{"observed_at":"2026-08-07T12:35:27.875719Z","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":"2026-08-07T12:35:33.008392Z","title":"Convolutional kernel networks","venue":null,"work_id":"c7e9cbad-5d0f-4d13-b9ad-d5fe5245af28","year":2014},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:27.988793Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:585197d7870842de0f45774729793b89311bcb4a848ea85d8ec0ba39070626a3","observation_id":"5cb6baab-eaca-451b-828a-b4827d2ff8ee","resolution":{"observed_at":"2026-08-07T12:35:33.068378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:32.857096Z","title":"Gated attention coding for training high-performance and efficient spiking neural networks","venue":null,"work_id":"7a761b7f-2aea-45c5-8e36-be63f614e312","year":2024},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.145841Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:43688dbe5b9c21e597dc2a07d7c98b04e98971e003da95504c44fa84ccba83bf","observation_id":"8747053d-c541-4625-bbab-7d170425c008","resolution":{"observed_at":"2026-08-07T12:35:32.916592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:32.687158Z","title":"Deep CNN hyperparameter optimization algorithms for sensor-based human activity recognition","venue":null,"work_id":"d5e6aaeb-a41e-4493-9ca0-c0ee9874c571","year":2022},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.258767Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:afc2ffc6f27ce56d1ea8d9e556afaa989f33bdfd0511921eeeb69a93982d0f64","observation_id":"a8341eb7-fea2-410b-bc39-a537192e1eb9","resolution":{"observed_at":"2026-08-07T12:35:32.761978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:32.488511Z","title":"Artificial intelligence, machine learning and deep learning in advanced robotics, a review","venue":null,"work_id":"9816d3e8-156a-4454-ba9d-34a2615ee708","year":2023},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.401733Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:525cb2b4e107be72a5e6ba430ef779dc8258b4c58c63ed7b65c23e3890562214","observation_id":"8eb6903f-8508-4e16-ac24-77b5f47df5f3","resolution":{"observed_at":"2026-08-07T12:35:32.567480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:28.488036Z","title":"Mobilenetv2: Inverted residuals and linear bottlenecks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.488036Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:abac0c099ab14ed57d6efff2349974a8ad3514e4b27687ecb85416c20295c343","observation_id":"ffb82646-88ce-4d65-ad88-27716e55d248","resolution":{"observed_at":"2026-08-07T12:35:28.488036Z","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":"2026-08-07T12:35:32.296758Z","title":"Avoiding overfitting: A survey on regularization methods for convolutional neural networks","venue":null,"work_id":"461e064e-2410-4cb8-b6b3-5811233af25f","year":2022},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.599130Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:04494bebc9b20bee15611296b3584a0c6b72931a0c578d9ba4d4bc3d833a273d","observation_id":"267433b6-bcb1-4196-8874-f6bff69b7418","resolution":{"observed_at":"2026-08-07T12:35:32.385115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:32.084561Z","title":"Very deep convolutional networks for large-scale image recognition","venue":null,"work_id":"23c993c5-3970-4857-b014-d1cef6a62bfe","year":2015},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.705339Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:51372d5ed090727c5b588d97e839136bee67461af6fc9e24f0a3cc18935768fc","observation_id":"d0666ea6-4add-4e3f-9f6b-4e5ddd4a331f","resolution":{"observed_at":"2026-08-07T12:35:32.198520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:31.797097Z","title":"Efficientnet: Rethinking model scaling for convolutional neural networks","venue":null,"work_id":"657b9e7f-97a3-42de-8b26-365b7e6b9885","year":2019},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.811703Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:c010ca63278ed88dc99cf1ec1596f5144dc88eca6ea959915bcefcde50f95b19","observation_id":"a03a84a8-6090-4d74-91b9-9f80377a477f","resolution":{"observed_at":"2026-08-07T12:35:31.949477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:31.635816Z","title":"https://top500.org/lists/top500/2024/06/","venue":null,"work_id":"6152e1d4-6d8f-41c8-904e-1388e22969d9","year":2024},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.895857Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:2893821e2204b8cb911e1fa0a4017492e04a29009af775955e108d11c705141d","observation_id":"c271de96-bff0-438a-9fdf-ff535e6242da","resolution":{"observed_at":"2026-08-07T12:35:31.713700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:31.493188Z","title":"Attention is all you need","venue":null,"work_id":"f81db093-ad95-4630-8a72-3fc7e36be73b","year":2017},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:28.981550Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:35738390b379215361b0127fce6470ee9fd1a9897d26d53566f7ac5373351240","observation_id":"5215eeba-7180-4827-aa38-ae5db98f6150","resolution":{"observed_at":"2026-08-07T12:35:31.585133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:31.351304Z","title":"A universal image quality index","venue":null,"work_id":"c1e48c9b-3f9e-4182-bd81-9c921623e865","year":2002},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.069510Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:e87243383fbd72e7ef7e0d3ec78b06e5a6ed5bd6c4c137cb778a9c6d4d2c6b5b","observation_id":"009d6892-1e6c-4b85-a262-d26639ef145e","resolution":{"observed_at":"2026-08-07T12:35:31.416166Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:31.180151Z","title":"Regularization of neural networks using dropconnect","venue":null,"work_id":"44368298-df47-4184-8f42-1ce7387e9154","year":2013},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.180596Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:043d24eae1d27c3f249fce6c3d84039772056ae7785317a6f8b7c38136f7c0eb","observation_id":"84883c18-d9ef-47d7-8ceb-9d300f6d38e1","resolution":{"observed_at":"2026-08-07T12:35:31.252039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:31.020913Z","title":"Restormer: Efficient transformer for high-resolution image restoration","venue":null,"work_id":"4de4991e-b4ba-4eb3-bb80-0906cd5f701d","year":2022},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.259937Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:6c41af5c86b435e05ed3978434c8970e087091221b6224d08018415d434e3046","observation_id":"d13aa99e-346b-4a17-9b89-5af6b1e34879","resolution":{"observed_at":"2026-08-07T12:35:31.102146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:30.831469Z","title":"Hyperparameter optimization in cnn for learning-centered emotion recognition for intelligent tutoring systems","venue":null,"work_id":"febd7c86-2ecc-49bc-9647-1ba2a48e989a","year":2020},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.422807Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:89f84f87b08abcd0b6bf6b8640797fa352cf3c7a935b756520408536e9c33ebf","observation_id":"f28b163a-a017-40fa-a3e1-8aa075532e7c","resolution":{"observed_at":"2026-08-07T12:35:30.932229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:30.623027Z","title":"Improvement of generalization ability of deep CNN via implicit regularization in two-stage training process","venue":null,"work_id":"3cdfb66e-abfb-49bf-b937-ac69f4ca8fb6","year":2018},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.529968Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:bd4f9f9b5b8fc6071a101bb5b411d9a036c591da76779b5043c29088552e8ee9","observation_id":"265621c4-d161-47ee-9818-bd7b00ff100f","resolution":{"observed_at":"2026-08-07T12:35:30.702895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:30.424399Z","title":"Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising","venue":null,"work_id":"29477bea-1b70-4f78-a574-4bd37326c6fb","year":2017},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.663735Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:01f6111eef0be0231dbddae628724e438268acd0c4645ae086e10319a7f0aae7","observation_id":"1f6bccaa-356c-4bb6-afcb-3b594084049a","resolution":{"observed_at":"2026-08-07T12:35:30.520670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:30.265697Z","title":"Fsim: A feature similarity index for image quality assessment","venue":null,"work_id":"717f3946-673b-42c9-ae2c-03add7f7969b","year":2011},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.772007Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:4023fd7752f63b4cf08ec04e0dd347c533d27e2f224692fed752386e68216e3c","observation_id":"f5573f31-a8d0-43be-a6e5-be19db5e3d83","resolution":{"observed_at":"2026-08-07T12:35:30.342158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T12:35:30.059463Z","title":"Ffdnet: Toward a fast and flexible solution for CNN -based image denoising","venue":null,"work_id":"6be03d43-8567-4c60-8cd1-f510815dde02","year":2018},"citing_paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-07T12:35:29.880979Z"},"links":{"citing_paper":"/paper/2505.24558"},"observation_digest":"sha256:8fd0caac60081229f2420c066dbdddc112a3b08eec09a1e4f4df12e3a26644c5","observation_id":"15124851-ebd4-4dfb-8c28-0c266c911593","resolution":{"observed_at":"2026-08-07T12:35:30.147999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.24558","last_updated":"2025-05-30T13:10:46Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T12:16:01.861278Z","submitted_at":"2025-05-30T13:10:46Z","title":"Optimal Weighted Convolution for Classification and Denosing"},"reference_resolution":{"displayed":44,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":0,"verified_fuzzy":32},"total_outbound_references":44},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2505.24558."}