{"as_of":"2026-08-07T02:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:72779ee2762708a1f4fde13e107fc16f64d8699f6903490a6b769c751e486768","coverage":[{"denominator":45,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":45,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-24T00:33:22.959973Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T18:47:08.624927Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-04T02:59:25.081929Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"cited_work":{"arxiv_id":"2405.13901","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.13901","snapshot_observed_at":"2026-07-04T02:59:25.081929Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","venue":"cs.CV","work_id":"429bb409-d0d5-4a78-a5fc-906ba7713ef9","year":2024},"citing_paper":{"arxiv_id":"2606.19574","last_updated":"2026-06-17T20:28:16Z","snapshot_observed_at":"2026-07-06T23:54:51.451358Z","submitted_at":"2026-06-17T20:28:16Z","title":"FrequencyFormer: A Co-Designed Sensor-to-Processor Pipeline for Frequency-Domain Vision Transformer Inference","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-26T18:47:08.624927Z"},"links":{"cited_paper":"/paper/2405.13901","citing_paper":"/paper/2606.19574"},"observation_digest":"sha256:12ce5b23bbfee7bbc438ff770ad09903e7fa01a69334dbf23d1d9198a99fec83","observation_id":"cc143847-4546-48da-b7d3-cbe108ad72bd","resolution":{"observed_at":"2026-07-04T02:59:25.083890Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2405.13901/citation-record","integrity":"/paper/2405.13901/integrity","json":"/paper/2405.13901/citation-record.json","paper":"/paper/2405.13901"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Discrete cosine transform.IEEE trans- actions on Computers, 100(1):90–93","venue":null,"work_id":"802764f7-5041-47ba-bf22-68f9d48d2dd8","year":1974},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:0c90d743127cf853fa9798087a62035ebfec0e92edf67860d04ce34bf1af8001","observation_id":"40499097-381c-4585-b35e-ecd8d49e2f8d","resolution":{"observed_at":"2026-05-24T00:33:40.327852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Toeplitz approximation to empirical correlation matrix of asset returns: A signal processing perspective","venue":null,"work_id":"54a2ecde-6a7a-4d9d-8f24-7860566360ce","year":2012},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:5c2d2559e914aab7d2a6d00cf8dc1bcdef040c1653dded01f03c7c2e590dc9ed","observation_id":"8590d013-797f-4cbb-983a-5126317f8100","resolution":{"observed_at":"2026-05-24T00:33:40.331232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Hydra at- tention: Efficient attention with many heads","venue":null,"work_id":"3dbc83dc-1b13-4aa5-9c08-3d40bc413e5f","year":2022},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:b0cf98835d2da8c025bc8a381c21438cb65aa7b925966e5a54620e9eed8ce001","observation_id":"d6be680d-ff84-4972-9918-c4cafe022bf0","resolution":{"observed_at":"2026-05-24T00:33:40.324164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Reminder of the first paper on transfer learning in neural networks, 1976.Infor- matica, 44(3)","venue":null,"work_id":"92481afc-4248-4da6-81ba-227c94dd61ca","year":2020},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:a1ee3ee609ba1169618343231a6a9250496c1d4e57f290ab6b54493fc3bb80f7","observation_id":"1075b532-aa99-4077-a50b-6c5c0b122698","resolution":{"observed_at":"2026-05-24T00:33:40.343765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Fourier image transformer","venue":null,"work_id":"3215c540-d1ec-491c-80d8-67a3b0021152","year":2022},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:2e5c553e6ea1182a582175203d61ba78fa399223161b10992fc7e9905fb8b464","observation_id":"7952c76c-5090-4a21-b4ec-46076a4d465e","resolution":{"observed_at":"2026-05-24T00:33:40.339423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Cascade r-cnn: Delving into high quality object detection","venue":null,"work_id":"6803e1b6-123d-42d7-a3a9-ad54588ec906","year":2018},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:ca444ea5ab2714af9156e56992169ea80f396e7e3b043bcc9a033ce7c84b41ce","observation_id":"fd0b0c57-6421-40a6-b0fe-3ff4faf6e0f5","resolution":{"observed_at":"2026-05-24T00:33:40.350692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A fast computational algorithm for the discrete cosine transform.IEEE Transactions on communications, 25(9):1004–1009","venue":null,"work_id":"2e66b038-94b2-495f-9b48-e7a5fbe9942c","year":1977},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:a0b14362902f5696fcf0db7f16a21d882355fb765c05b19dc6197b2262739951","observation_id":"75d80bae-096f-4d5c-8da5-ea5c0f9c72c5","resolution":{"observed_at":"2026-05-24T00:33:40.260426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Fast fourier convolution.Advances in Neural Information Pro- cessing Systems, 33:4479–4488","venue":null,"work_id":"28965ea2-e8a1-4ec2-8f7e-e6499276ddc5","year":2020},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:377c0ebd7be6a5b6fcdcab46fa629d9b82f758fb8b82000aca3dd779bec64b47","observation_id":"b06833dc-335f-41eb-a007-20637a68b189","resolution":{"observed_at":"2026-05-24T00:33:40.256079Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.09501","last_updated":"2019-04-11T22:39:27Z","snapshot_observed_at":"2026-08-03T21:58:34.348182Z","submitted_at":"2018-05-24T04:05:42Z","title":"AutoAugment: Learning Augmentation Policies from Data","version":3},"cited_work":{"arxiv_id":"1805.09501","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.09501","snapshot_observed_at":"2026-07-03T13:58:21.851521Z","title":"AutoAugment: Learning Augmentation Policies from Data","venue":"cs.CV","work_id":"9cfcaaf4-6f01-4522-b146-cf16d4be7b90","year":2018},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"cited_paper":"/paper/1805.09501","citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:2db3ca7c3c14ec4dc8b1810afa7f70d72d4202fc30d35468f0ec710ec7e7201d","observation_id":"07247c1a-2adb-49ef-8066-9bce04c12de1","resolution":{"observed_at":"2026-05-24T00:33:39.799856Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Karhunen-loeve transform.The transform and data compression hand- book, 1(1-34):29","venue":null,"work_id":"db881d23-cd10-48d6-80f5-ad3799a48ed9","year":2001},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:83c6c610347c549a39292ef739bdcb8bce4d9dc5d3e562f7e08fbb6dd7c764c2","observation_id":"cdc9412d-8799-4a87-970f-cce60d2708ed","resolution":{"observed_at":"2026-05-24T00:33:40.334977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":"08c102c0-223f-42b3-8021-0e1ece94a774","year":2020},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:c08ca481c4eb45eba1c630f3e04879841e0add85e5cabca886d6e7fa68a66b3c","observation_id":"f5a23c9f-0811-4905-bb57-a57c968235a1","resolution":{"observed_at":"2026-05-24T00:33:40.220075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mpeg-4 natural video coding–an overview.Sig- nal Processing: Image Communication, 15(4-5):365–385","venue":null,"work_id":"c5c6f39c-0547-4e68-b6f0-67843e55d18d","year":2000},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:8f87a47c49d58ff9d68a03b48376208bc5cbc0caccb2d9e1c61c82b7922ef936","observation_id":"a61941f5-d72d-4ff2-a209-d3785308c6a6","resolution":{"observed_at":"2026-05-24T00:33:40.236845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Understanding the difficulty of training deep feedfor- ward neural networks","venue":null,"work_id":"32161d4a-ac8e-474a-b73e-30c93a9bc0f3","year":2010},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:0224d62ce1f7e679ae38847659e710e78a0c8d700cedf18d7ada073d162d2ab4","observation_id":"560bdd26-4296-4ead-a7b0-5d43cf48a013","resolution":{"observed_at":"2026-05-24T00:33:40.194833Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"gswin: Gated mlp vision model with hi- erarchical structure of shifted window","venue":null,"work_id":"1525715f-8719-4e16-a5ad-f246252c145c","year":2023},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:902e165c033bc33ddadafc5e06e36e2f402af912c16545f9fc833f33e57501a9","observation_id":"4927b0bf-7205-4f44-a666-6a1fa8504f81","resolution":{"observed_at":"2026-05-24T00:33:40.282055Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Delving deep into rectifiers: Surpass- ing human-level performance on imagenet classification","venue":null,"work_id":"cacf11ea-9ff7-44b3-baa9-7edde161aec6","year":2015},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:b624a785d601634a88f736eac0d64e10f2344906834e81167688a97238f4a890","observation_id":"0235cdba-e0be-4426-baf3-73f4b4e00c67","resolution":{"observed_at":"2026-05-24T00:33:40.215502Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-07T08:23:31.419370Z","title":"Deep residual learning for image recog- nition","venue":null,"work_id":"66f3159f-a2e6-4d41-8dcf-714b47454bf0","year":2016},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:908a02c64c5269360635f7494a2c3dc0fa59c365616a0fd3bfb7a2d5ff74cfb3","observation_id":"0fb722c1-92b2-42bb-b6ea-d3b57568694b","resolution":{"observed_at":"2026-05-24T00:33:40.285700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Colorformer: Image colorization via color memory assisted hybrid-attention transformer","venue":null,"work_id":"3cdce256-7faa-4e92-ab1a-ee45ddf9420f","year":2022},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:dfd883bdb197ca0c1fe186e67c077903f9ef951aa48f1d4b1c781a26f41a3db4","observation_id":"40ec8ce1-c27a-44ed-a10a-0a738711c100","resolution":{"observed_at":"2026-05-24T00:33:40.292766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Discrete cosin transformer: Im- age modeling from frequency domain","venue":null,"work_id":"fe537e4b-0e82-4ceb-affd-da71277ed286","year":2023},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:f7bbdae596e097df8ee855ef8fd32d2bd322a53baad3476fad7ee27391e75c62","observation_id":"2db5d130-9286-4e29-80d4-29cea069a97a","resolution":{"observed_at":"2026-05-24T00:33:40.268110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Microsoft coco: Com- mon objects in context","venue":null,"work_id":"62b86239-8a76-4571-9b57-3f6b6a1d778f","year":2014},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:8e2437b6090558681d5414e718f330983c14b4177fc5f78fdf35bc07c7fdda25","observation_id":"083c1f5b-5a2d-4fa6-b976-e32f46058504","resolution":{"observed_at":"2026-05-24T00:33:40.264411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows","venue":null,"work_id":"db21fd65-0d90-43a4-b9e8-529ec0354872","year":2021},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:5dd43b8943ea87d38a5b42266c41d43ba58d28146c4068f91f7a9293b905fed2","observation_id":"7912a83f-590e-4801-9175-212c2e222cab","resolution":{"observed_at":"2026-05-24T00:33:40.278657Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep learn- ing via hessian-free optimization","venue":null,"work_id":"f7ba74fa-2c77-4ca1-bcc0-2a7ef17fbfac","year":2010},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:9742df8dfbf0c35112d080b25680ec95f76e613aeffe71f24b4c69381383e3e2","observation_id":"6f92e9dd-a337-4698-8bf5-d1d33646c5e2","resolution":{"observed_at":"2026-05-24T00:33:40.224595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Training neural network with zero weight ini- tialization","venue":null,"work_id":"9caf81f0-8b79-43de-81f4-976a452dd18e","year":2012},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:7e6b84df57b120c319c1ab9020738c2eb7e18a4bd2843f3eeb1de6aab67079f4","observation_id":"a033a052-bb58-417f-ac50-f5f3f99dde66","resolution":{"observed_at":"2026-05-24T00:33:40.271809Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer","venue":null,"work_id":"979bde26-a652-4fdd-b005-1953a5b7f1a5","year":2021},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:7acf1184966505b38e57642462c68489d32a6dc4a43b0c7642bba5f7b3fd75af","observation_id":"5cabb2c0-6808-48ca-a405-9c6d1f8f4b0e","resolution":{"observed_at":"2026-05-24T00:33:40.244664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A hybrid quantum-classical approach based on the hadamard transform for the convolutional layer","venue":null,"work_id":"49c15923-12e5-4613-add4-72392b58ae9c","year":2023},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:041a9372aa74af1edfb6eea3d8d74fcf6f39d8fd56f7d09597f590e10f623fb7","observation_id":"9561427b-1f5e-4947-bbef-15ea58b6295b","resolution":{"observed_at":"2026-05-24T00:33:40.228954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Pytorch: An imperative style, high- performance deep learning library.Advances in neural in- formation processing systems, 32","venue":null,"work_id":"73c16712-cbe2-4929-a0ad-5a00f5234b6f","year":2019},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:013d91385723cde0e60af1fbebbd686ed7472297dab2935661ea8e58bc456d4b","observation_id":"6f47854e-0fba-40c2-8874-c72b8bbaf807","resolution":{"observed_at":"2026-05-24T00:33:40.188934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Spectformer: Frequency and atten- tion is what you need in a vision transformer","venue":null,"work_id":"61786801-6ca7-4bd3-854f-1974249069c6","year":2025},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:44744bac65f9fb6d29557ceaf303b99ad56d2a899de6fd16f02dd509b4af35da","observation_id":"1698a8a1-8006-4857-ae5b-bde3ba9e581b","resolution":{"observed_at":"2026-05-24T00:33:40.296517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Low-complexity rounded klt approx- imation for image compression.Journal of Real-Time Im- age Processing, pages 1–11","venue":null,"work_id":"c58c19bf-ff98-4963-8620-5b5acf59686a","year":2022},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:dbae56f1f69eed784cf43d4224d768016d4dcf48c4e3c2f75e12b1c4289d5e37","observation_id":"eca3ff06-2bac-4802-85d8-eddb46eab583","resolution":{"observed_at":"2026-05-24T00:33:40.301071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mobilenetv2: Inverted residuals and linear bottlenecks","venue":null,"work_id":"779f4634-df4a-47de-84ed-1550af782476","year":2018},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:f9bd81c3707658696713ffd989246b2a10ae27f2be122684c3b6dadfaf8a6466","observation_id":"8545fb89-dab4-4fd6-b328-5d9f748efe58","resolution":{"observed_at":"2026-05-24T00:33:40.211422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6120","last_updated":"2014-02-19T17:26:57Z","snapshot_observed_at":"2026-08-01T19:05:53.906854Z","submitted_at":"2013-12-20T20:24:00Z","title":"Exact solutions to the nonlinear dynamics of learning in deep linear neural networks","version":3},"cited_work":{"arxiv_id":"1312.6120","doi":"10.48550/arxiv.1312.6120","metadata_source":"pith","pith_arxiv_id":"1312.6120","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Exact solutions to the nonlinear dynamics of learning in deep linear neural networks","venue":"cs.NE","work_id":"adbbf9c7-c3a4-4cb7-9a00-c98b12f8a315","year":2013},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"cited_paper":"/paper/1312.6120","citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:e3fc6c883fb874b428e1c831a899abf36238e9c61918027a58b460ef0d49636e","observation_id":"17f8aa6e-2386-4178-a1e0-d02e5f016e7e","resolution":{"observed_at":"2026-05-24T00:33:39.794169Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-07-11T04:49:31.258619+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T04:49:31.258619+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dct-former: Ef- ficient self-attention with discrete cosine transform.Jour- nal of Scientific Computing, 94(3):67","venue":null,"work_id":"35d758e5-8a79-44b4-8395-cb6f9e23be6d","year":2023},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:5925604e353d4f7025259986a73100d5d4d40c8c1d6b81fb13474b129841e90b","observation_id":"7ac4414b-711f-495d-a034-a6c6095ca088","resolution":{"observed_at":"2026-05-24T00:33:40.312393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mi- crovit: a vision transformer with low complexity self atten- tion for edge device","venue":null,"work_id":"e1301647-3105-4b13-b4e1-6138f0f19708","year":2025},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:40ffc43b5e4b6e9d55afa583714e8345d32a7915a00d591fbf7a0cc5c8868cb2","observation_id":"b5fe1205-3982-4af8-b72a-5f5388451c9a","resolution":{"observed_at":"2026-05-24T00:33:40.240791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Efficient attention: Attention with linear complexities","venue":null,"work_id":"9fdc0a1c-fc88-41fb-8814-0cec558ab709","year":2021},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:96ff49067e445dfa7a9b482f5796662ef8cc2d1d05a91911ee38d6f0fa665dbf","observation_id":"f8771983-2c38-46b9-a645-f0c9b996caca","resolution":{"observed_at":"2026-05-24T00:33:40.304770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Compressive estimation and imaging based on autoregres- sive models.IEEE Transactions on Image Processing, 25(11):5077–5087","venue":null,"work_id":"c767acbd-967d-417a-9dc2-ace0cb4f28da","year":2016},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:def484be92b455c7191d6f1f560ebe3de6d5845fd8458946aeba0e411f48369f","observation_id":"747a6959-6b51-44de-a046-5fb60cbff418","resolution":{"observed_at":"2026-05-24T00:33:40.308552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.09828","last_updated":"2023-05-16T22:12:25Z","snapshot_observed_at":"2026-08-05T20:29:14.196208Z","submitted_at":"2023-05-16T22:12:25Z","title":"Mimetic Initialization of Self-Attention Layers","version":1},"cited_work":{"arxiv_id":"2305.09828","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.09828","snapshot_observed_at":"2026-07-04T13:09:50.650340Z","title":"Mimetic initialization of self-attention layers","venue":null,"work_id":"2ae9be55-b786-4958-b25a-3c5dc4a29783","year":2023},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"cited_paper":"/paper/2305.09828","citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:ecc8e59d4e75844ef9bb522fbb7d5e6c8178cf59a94d58c0d9a3a1aced6ad90e","observation_id":"42993867-c768-4e6f-95b0-09f70b137391","resolution":{"observed_at":"2026-05-24T00:33:39.812038Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-07T10:13:40.911049Z","title":"Attention is all you need.Advances in neural information processing systems, 30","venue":null,"work_id":"ce925c33-3953-4a3a-bec2-824c6d3446fe","year":2017},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:e32863bf2dd3dde5e8e93f6680647158e1427e5bd76a715f92da7931ec0adadc","observation_id":"b6932c60-e759-43dc-8a50-65e352b963b1","resolution":{"observed_at":"2026-05-24T00:33:40.248673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"The jpeg still pic- ture compression standard.Communications of the ACM, 34(4):30–44","venue":null,"work_id":"e0b86d83-d067-4d95-af1b-c1f46e2ee507","year":1991},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:b5d3775f37db932f5c262c49405e9e5fe8b354723d3504fa960c13c1e29f2978","observation_id":"d28f8729-9d75-405e-8dcc-2bb9736737f4","resolution":{"observed_at":"2026-05-24T00:33:40.252462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A survey of transfer learning.Journal of Big data, 3(1):1–40","venue":null,"work_id":"a06a7784-db19-4351-b632-59699b7779be","year":2016},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:a5891f1f80f366a44dcff51e907bde194f79bdc52de9cc06aa580d9959252203","observation_id":"a250b8f7-28b3-4f14-bfa9-e4a88c371750","resolution":{"observed_at":"2026-05-24T00:33:40.207462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Initializing models with larger ones","venue":null,"work_id":"9325a7b0-41b9-418d-babb-1eef61393f70","year":2023},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:303b279462c50e28f264812badc363d458e6a1dd5f96cdfc7a935581cfac59b5","observation_id":"ae2535d0-ab10-4b62-a65c-b9fce348a3b8","resolution":{"observed_at":"2026-05-24T00:33:40.316585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Metaformer is actually what you need for vision","venue":null,"work_id":"b28c7028-3d83-49b3-9ab8-7ecff5d72253","year":2022},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:2369216b6c6be24c32fe8a4ecf3a408e4a13d4b41b5ceb11aeb758908a713df7","observation_id":"5ee20aaf-96ff-48ad-b185-c4d065979262","resolution":{"observed_at":"2026-05-24T00:33:40.203138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Cutmix: Regularization strategy to train strong classifiers with localizable features","venue":null,"work_id":"fc6b961c-3474-499b-952e-3d95ed7f2de4","year":2019},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:a9e7c2d527edd63c7f6da230bbe47c57222b3a3a7d021966f38d4a4ace63b4d3","observation_id":"67816cd1-f0e1-41f6-bf57-06e1bbdc102e","resolution":{"observed_at":"2026-05-24T00:33:40.320410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"mixup: Beyond empirical risk minimization","venue":null,"work_id":"c453e3c2-b8b6-478a-aad3-6db0fa0f09f9","year":2018},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:6250e50807de98e4629aabdfc6afb3925239bcbee4b8f9b37f1b6b111db9e476","observation_id":"fa05e2ca-f7c2-4ebb-9742-a76eed9166e7","resolution":{"observed_at":"2026-05-24T00:33:40.275359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Improving deep transformer with depth-scaled ini- tialization and merged attention","venue":null,"work_id":"41fa733c-1bb1-46e4-a818-bd721f358878","year":2019},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:ea863325a061afcdff07fe43ce52ef04e2d8d130704c2816d3b584f115603315","observation_id":"1b10d02c-549c-4af7-8803-8cafd1443e82","resolution":{"observed_at":"2026-05-24T00:33:40.199064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Zero initialization: Initializing neural networks with only zeros and ones.Transactions on Machine Learning Research","venue":null,"work_id":"8c293047-e8ad-4514-b6ca-22d16746fb3a","year":2022},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:8ecd01464efdc252a20e3978acd88bc195bee67e24f74b3d47f9d088f4347ed9","observation_id":"aa821ee9-b734-45db-8d0b-e79c561db64d","resolution":{"observed_at":"2026-05-24T00:33:40.289357Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01139","last_updated":"2024-04-01T14:34:47Z","snapshot_observed_at":"2026-08-05T19:50:02.141622Z","submitted_at":"2024-04-01T14:34:47Z","title":"Structured Initialization for Attention in Vision Transformers","version":1},"cited_work":{"arxiv_id":"2404.01139","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.01139","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Structured initialization for attention in vision transformers.arXiv preprint arXiv:2404.01139","venue":null,"work_id":"a65738b6-152b-4267-b6bd-521a1b876081","year":2024},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"cited_paper":"/paper/2404.01139","citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:09340b525168d09e5944dda2d94e820c98d260e186603fba2c1351123fe3cc87","observation_id":"ea854e97-1489-4080-a830-d3a43e804b1a","resolution":{"observed_at":"2026-05-24T00:33:39.806228Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Random erasing data aug- mentation","venue":null,"work_id":"bc783feb-d6e6-4fa9-b93b-d69c3d4fbccf","year":2020},"citing_paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers","version":6},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-24T00:33:22.959973Z"},"links":{"citing_paper":"/paper/2405.13901"},"observation_digest":"sha256:4603c826e7f03475b35838d89b5e9c08715b3301d451801b6dbeab4f6f9fe7d5","observation_id":"a978fd43-613c-4159-9759-d1a3fdf1ba5c","resolution":{"observed_at":"2026-05-24T00:33:40.233032Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2405.13901","last_updated":"2026-05-15T17:05:43Z","latest_version":6,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T18:18:07.839497Z","submitted_at":"2024-05-22T18:15:42Z","title":"Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers"},"reference_resolution":{"displayed":45,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":4,"verified_fuzzy":41},"total_outbound_references":45},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2405.13901."}