{"as_of":"2026-08-17T18:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7d290226057ce1fece024182bf56ae449fb1f9ab78dc3ce94af59a875997ae34","coverage":[{"denominator":95,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":95,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T23:36:20.831364Z","state":"measured"},{"denominator":95,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":95,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2608.01653/citation-record","integrity":"/paper/2608.01653/integrity","json":"/paper/2608.01653/citation-record.json","paper":"/paper/2608.01653"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:36:10.956687Z","title":"The jpeg still picture compression standard,","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:10.956687Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:85ddd4586c3cbaf5ba35a86eb61c80351b0153c2e7e3b5b2b201af4ed8cb5790","observation_id":"7ab72861-ad95-4477-9d5b-93cda47a6c71","resolution":{"observed_at":"2026-08-04T23:36:10.956687Z","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":"2026-08-04T23:36:10.998717Z","title":"Overview of the high efficiency video coding (hevc) standard,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:10.998717Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:76392232064f889d073b3042daa86d0e9a2b37ddefc134d52cdb4b95fe8510dd","observation_id":"c8bdb960-e80c-4037-9304-ff090ee41c7b","resolution":{"observed_at":"2026-08-04T23:36:10.998717Z","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":"2026-08-04T23:36:11.078041Z","title":"Overview of the versatile video coding (vvc) standard and its applications,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:11.078041Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:640d82054db30bcd016ba3033db197a061b59ab058de91f46b750e76f006b7e1","observation_id":"e93cb7de-c7ab-4704-9ef9-be304d57d2a3","resolution":{"observed_at":"2026-08-04T23:36:11.078041Z","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":"2026-08-04T23:36:11.155912Z","title":"Variational image compression with a scale hyperprior,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:11.155912Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:a2d328e48e120c4172c69fd1fb00d87986d03b3c1950973e3f1acac82208b3e2","observation_id":"5e8d3c45-00c2-4a0b-8c56-17452c91720b","resolution":{"observed_at":"2026-08-04T23:36:11.155912Z","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":"2026-08-04T23:36:11.282872Z","title":"Joint autoregressive and hierarchical priors for learned image compression,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:11.282872Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:85630a9fdf7f2d60a4c90f6a94588afba9d8a037b5398762e5db6defc1c0a4a8","observation_id":"5f707150-3e5e-4a44-a396-21cf4504c490","resolution":{"observed_at":"2026-08-04T23:36:11.282872Z","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":"2026-08-04T23:36:11.391838Z","title":"Learned image com- pression with discretized gaussian mixture likelihoods and attention modules,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:11.391838Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:f8b016150f85095ba7f6fc2ac2c9c2f3c5e7b87a1e4470c546a3de17d38b6e5f","observation_id":"51839d06-7e30-49ff-afe1-62ba8b55b377","resolution":{"observed_at":"2026-08-04T23:36:11.391838Z","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":"2026-08-04T23:36:11.495817Z","title":"Elic: Efficient learned image compression with unevenly grouped space- channel contextual adaptive coding,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:11.495817Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:d9b363454b796cf52c0fb31b74d71b52ed4f9b29ccc58e0bab2573b8ea0e9abf","observation_id":"2fd05043-71a6-4146-9666-bafb7d0e34bf","resolution":{"observed_at":"2026-08-04T23:36:11.495817Z","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":"2026-08-04T23:36:11.611609Z","title":"Learned image compression with mixed transformer-cnn architectures,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:11.611609Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:96f8880cb0c2e119289c0406efa1c686f19e49878025075e658063c0cb31d786","observation_id":"b53d7019-5f9f-41fc-bdb4-69ba9c70a25b","resolution":{"observed_at":"2026-08-04T23:36:11.611609Z","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":"2026-08-04T23:36:11.767288Z","title":"Dit-ic: Aligned diffusion transformer for efficient image compression,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:11.767288Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:1d264860461f5e3099bf6f7c43089b8b4d14c6431023bbb36b0e3411c1b9aa4b","observation_id":"5c69e093-a078-46a3-8286-772c05e8f00a","resolution":{"observed_at":"2026-08-04T23:36:11.767288Z","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":"2026-08-04T23:36:11.837098Z","title":"Taming hierarchical image coding optimization: A spectral regularization perspective,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:11.837098Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:e808fd61ba1b0e805494718641ac382b730e1afd669a66166366e31fc28c14a6","observation_id":"c21a9794-8011-4427-a8f6-8828ef0f2f57","resolution":{"observed_at":"2026-08-04T23:36:11.837098Z","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":"2026-08-04T23:36:11.912997Z","title":"Information technology—JPEG AI learning-based image coding system—Part 1: Core coding system,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:11.912997Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:61986d6b3decdadcb888561e76c096ecc516a1cf387448373daf1ac84f9553ce","observation_id":"eaf8f1d4-5a54-40e0-b487-ec3a3900f25b","resolution":{"observed_at":"2026-08-04T23:36:11.912997Z","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":"2026-08-04T23:36:12.011137Z","title":"IEEE Standard for Neural Network-Based Image Coding,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:12.011137Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:f6f7af70617db8eba35ca54bc377995d8167a3e1d744eef4097d7f6b8d6a565c","observation_id":"6c89bb57-54d9-4273-8bcd-aa01fce4ce6c","resolution":{"observed_at":"2026-08-04T23:36:12.011137Z","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":"2026-08-04T23:36:12.082529Z","title":"Rate-distortion optimized post-training quantization for learned image compression,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:12.082529Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:00302ab2036cafb910dd3e4ed8e05bd46217138d5710aac2958a18f188ec075f","observation_id":"1e35d7f1-cdff-4b5a-a48c-57c18df158bf","resolution":{"observed_at":"2026-08-04T23:36:12.082529Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.08295","last_updated":"2021-06-15T17:12:42Z","snapshot_observed_at":"2026-08-02T11:19:40.664702Z","submitted_at":"2021-06-15T17:12:42Z","title":"A White Paper on Neural Network Quantization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.08295","snapshot_observed_at":"2026-08-04T23:36:12.154754Z","title":"A white paper on neural network quantization,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:12.154754Z"},"links":{"cited_paper":"/paper/2106.08295","citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:f92561f2b335720c0c85441317969267a3017457be535bcf7bafb5e1f63861ff","observation_id":"2fb98f64-0173-41b7-8a08-ac260833b598","resolution":{"observed_at":"2026-08-04T23:36:12.154754Z","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":"2026-08-04T23:36:12.259944Z","title":"Activation and weight distribution balancing for optimal post-training quantization in learned image compression,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:12.259944Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:9a223993129446b1c99b419edd04e1a8098656fad2b983f63122ab892058faa4","observation_id":"adbd49ff-6433-4ac6-8cf5-f8f2f2be1daf","resolution":{"observed_at":"2026-08-04T23:36:12.259944Z","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":"2026-08-04T23:36:12.372774Z","title":"Post- training quantization for cross-platform learned image compression,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:12.372774Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:b4ef6778e983e387353e0169a1f1351739db056cf2243a1d754f70e6ada0f4cd","observation_id":"dd088702-afe5-4b1a-ae1f-49f47ed54e75","resolution":{"observed_at":"2026-08-04T23:36:12.372774Z","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":"2026-08-04T23:36:22.074867Z","title":"Device interop- erability for learned image compression with weights and activations quantization,","venue":null,"work_id":"b72bede0-4d2b-4a45-aa8c-de1e72a26321","year":2022},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:12.447644Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:005f5a301b2fdd30662a108095cac1977dfb416b3125da0cbc990e40012c4180","observation_id":"744ba796-8e0d-4c14-a213-504a5dd89a7c","resolution":{"observed_at":"2026-08-04T23:36:22.078257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:22.065718Z","title":"Quantized decoder in learned image compression for deterministic reconstruction,","venue":null,"work_id":"56b08045-b7af-41cb-955c-7f77a448f248","year":2024},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:12.554790Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:ef6a9832178bc9672ff75b05f2cc1a06c2eeff6f38cd4c0d1df9979875bdeb20","observation_id":"0f360365-7f58-472d-a8b3-e97c92b256f5","resolution":{"observed_at":"2026-08-04T23:36:22.069470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:22.056636Z","title":"Efficient neural image decoding via fixed-point inference,","venue":null,"work_id":"79dd7b78-5cd5-42dd-a9c1-12b1ad598398","year":2021},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:12.637232Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:96c46354966bee1e250b41b9818fa498ca50729bfa9fd436793d847c6e197692","observation_id":"dd5fd6f0-39b6-4bf3-a452-5494865d7c2e","resolution":{"observed_at":"2026-08-04T23:36:22.060432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:22.047356Z","title":"Q-lic: Quantizing learned image compres- sion with channel splitting,","venue":null,"work_id":"2dc428f7-3aca-4918-b842-fe9cdec1b536","year":2025},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:12.705735Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:35dd8c7c15ece162776b551fcb1f5bf90b1c2be3127be90edc3a8b71da88952d","observation_id":"3bbe1caf-2988-4403-9d05-02d5b875fc21","resolution":{"observed_at":"2026-08-04T23:36:22.051259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:22.037346Z","title":"End-to-end learned image compression with fixed point weight quantization,","venue":null,"work_id":"402300de-cbce-445f-a0d8-ec2cc4d5aaa3","year":2020},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:12.808357Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:b9fb082e2056cce65bc354c5c9a68cde7f773cd99009b9753aa91d41b242d429","observation_id":"d62c5069-e86f-45ae-8741-be3157eed62f","resolution":{"observed_at":"2026-08-04T23:36:22.041140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:22.027913Z","title":"Learned image compression with fixed- point arithmetic,","venue":null,"work_id":"7d3bac65-1076-4d21-9fc8-b71986d82071","year":2021},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:12.889130Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:939d7660236f0451fe692a10974729fa708d72396c97c1b93de455d192e2008d","observation_id":"b90dd507-cf00-44b5-92b5-2a424af24ab2","resolution":{"observed_at":"2026-08-04T23:36:22.031528Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:22.019057Z","title":"Subset-selection weight JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 13 post-training quantization method for learned image compression task,","venue":null,"work_id":"cb768feb-2583-4cf3-8a22-7ac982f77718","year":2021},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:12.996724Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:d26515178512501746704eb73944e0633526924872752c7fde839d45c29b947d","observation_id":"c55f51af-3cb4-46db-97e1-31626f51acd3","resolution":{"observed_at":"2026-08-04T23:36:22.022369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:22.009903Z","title":"Mpp-lic: Mixed precision post-training quantization for learned image compression,","venue":null,"work_id":"6dbb18f3-88ad-4774-9575-0ea26928ebf8","year":2026},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:13.070061Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:caa979598bad4e13167945e8bd6e372c6e3b39e689cc802d659c32ffbc719a39","observation_id":"86ba1003-df6a-4251-873b-85a75d711b05","resolution":{"observed_at":"2026-08-04T23:36:22.013219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:22.000809Z","title":"Dynaquant: Dynamic mixed-precision quantization for learned image compression,","venue":null,"work_id":"0b885506-ff87-4115-8012-dfb96cc7f6bc","year":2026},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:13.147476Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:4d8ef15b51c16010e69aadceb261e4062e80499886dc899438a45d91ab513243","observation_id":"80479050-01ae-4fe5-ae6f-c7def37f8f68","resolution":{"observed_at":"2026-08-04T23:36:22.004381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.991387Z","title":"Flexible mixed precision quan- tization for learned image compression,","venue":null,"work_id":"7834ddb8-1e41-4fe7-bd05-5024e619b414","year":2024},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:13.221860Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:2e79419e82ac87cd67463c4971c184bfbb957a05bcf6eedd771876f9a884131f","observation_id":"2858b9c2-9945-444f-9aae-6d7c751efbf0","resolution":{"observed_at":"2026-08-04T23:36:21.995184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.982958Z","title":"Mixed-precision post- training quantization for learned image compression,","venue":null,"work_id":"9f17e071-9a11-4a4b-ace5-fb57308f3a92","year":2025},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:13.297456Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:a2bda05f470655ddcfa5c5bbe0bb2945df8469820efeda030157a52e4ef2dbab","observation_id":"4cf22242-560b-4c36-a9f1-e89c947e7bf9","resolution":{"observed_at":"2026-08-04T23:36:21.985992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.974444Z","title":"Uniq: Uniform noise injection for non-uniform quantization of neural networks,","venue":null,"work_id":"1c091cdb-9ded-4e3e-a0ed-57b4f2883e59","year":2021},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:13.400309Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:fcb762c665d6896455954b1607585ce7683bc7d3ced17b627276b0b6ae26ed12","observation_id":"3b8fe9ce-cc21-4c1b-b428-6e87b5d0e019","resolution":{"observed_at":"2026-08-04T23:36:21.977676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.964914Z","title":"Clarke,Transform coding of images","venue":null,"work_id":"17d4b111-596f-4f99-93ff-15c7ffbb6ae3","year":1985},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:13.459917Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:b79a89200d273e04b17ecf5d54aed5e7c43ffe60897570c58831c8298d085e5d","observation_id":"f55b437b-79da-4d31-a4b3-fe2ae8a260e6","resolution":{"observed_at":"2026-08-04T23:36:21.968473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.956493Z","title":"Theoretical foundations of transform coding,","venue":null,"work_id":"3729a96d-5cb6-4079-ad04-f5b8e96463b0","year":2001},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:13.564974Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:2363c3983c1ba1919c6c8ca4c4f2f77acbbc81e1670508433c307810533794ab","observation_id":"fd5b4790-2e0e-48d6-b5c0-09427883606f","resolution":{"observed_at":"2026-08-04T23:36:21.959565Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:13.614272Z","title":"Hadamard transform image coding,","venue":null,"work_id":null,"year":1969},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:13.614272Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:f74cb9ebf78e9ccc47f9cf0116e8e1481fb3de61094d6297a846d3d9340aa1b8","observation_id":"e2dc381d-5107-427e-8ef4-13db0160aef1","resolution":{"observed_at":"2026-08-04T23:36:13.614272Z","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":"2026-08-04T23:36:21.941942Z","title":"Vector quantization,","venue":null,"work_id":"9223d4ed-90cd-4864-82b9-ac9b34b9cd0b","year":1984},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:13.673434Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:6c58c0c5c9317fc418d14a36e39595bf92a6d18a0031e32346bb911c34a956e2","observation_id":"4b361cad-275d-41b7-8329-d801cb57556a","resolution":{"observed_at":"2026-08-04T23:36:21.945190Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.933505Z","title":"Gersho and R","venue":null,"work_id":"63cc5724-238a-4e71-a3b6-9e1ede933046","year":1992},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:13.706086Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:7b1cc72d0570f29d004ab2c1f45ba0a5af98a6687bf3025e67c3a2b3e27b41e8","observation_id":"7638276f-7ef3-4fe7-a128-7f70628fcf2a","resolution":{"observed_at":"2026-08-04T23:36:21.936434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:13.779112Z","title":"Nonlinear transform coding,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:13.779112Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:873dc3fd78830279248127ff35303e1790002753ee115cff85ada4e59438f95a","observation_id":"40d4aa92-91bc-4e2d-8d27-a097dff14616","resolution":{"observed_at":"2026-08-04T23:36:13.779112Z","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":"2026-08-04T23:36:21.920427Z","title":null,"venue":null,"work_id":"75e4b40b-2e68-4ebc-87c8-c254b95f02d1","year":1984},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:13.957906Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:beffa8a811c250a90f7b752d0e8b1ad95867e4c70e9aec7a5032081c5f6b6cae","observation_id":"034ff2c6-7d24-4e20-8860-cebbbea98ecd","resolution":{"observed_at":"2026-08-04T23:36:21.923308Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:14.117980Z","title":"A method for the construction of minimum-redundancy codes,","venue":null,"work_id":null,"year":1952},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:14.117980Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:234dcce2f8d641453e3803d974bb5e4920b68f730bcb58390cbe51609473ee33","observation_id":"5347479e-fdba-477c-9c9d-b49636454b8a","resolution":{"observed_at":"2026-08-04T23:36:14.117980Z","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":"2026-08-04T23:36:14.256665Z","title":"Discrete cosine transform,","venue":null,"work_id":null,"year":1974},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:14.256665Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:598e2dfc237f8c3a5834b1a93bf22d99096659f9a9f4cc11afe229ba42d624c4","observation_id":"c58f4bb5-1038-460c-a885-047349c25f2d","resolution":{"observed_at":"2026-08-04T23:36:14.256665Z","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":"2026-08-04T23:36:21.900369Z","title":"Complete discrete 2-d gabor transforms by neural networks for image analysis and compression,","venue":null,"work_id":"397764b6-d4f5-4383-beda-02702350c83b","year":1988},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:14.470456Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:1251c8568b1407cbc59ebe43946f69a951e38965b8a61ceb96e6bcea183ab882","observation_id":"41eebc5a-5201-4bf1-9fe4-cc77b2a33996","resolution":{"observed_at":"2026-08-04T23:36:21.904754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.890468Z","title":"Image compression by back propagation: A demonstration of extensional programming,","venue":null,"work_id":"f2c3a3df-8729-4930-aeee-9138385fc279","year":1987},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:14.609103Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:53f1d041296e2f4c91856b191ecd01942b9730fb11c745e9c07c8995bcbb58bf","observation_id":"cddb71bb-2ea8-419f-9335-be8b831aff1c","resolution":{"observed_at":"2026-08-04T23:36:21.893875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.881171Z","title":"Neural network approaches to image compression,","venue":null,"work_id":"86696658-5b30-4243-b9b4-cc25e854987a","year":1995},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:14.731473Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:a7932c7c4024771002590a6ede2705e6a75b6e6e142871d81f5b00e2ef50151f","observation_id":"3eaffa43-d753-44c6-9f32-35ed3f1a302a","resolution":{"observed_at":"2026-08-04T23:36:21.884470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.871460Z","title":"Image compression with neural networks: A survey,","venue":null,"work_id":"c2e5e870-6a6c-47ae-82b2-140312040978","year":1999},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:14.894322Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:acdec4bd02dd548e4021643f5189341bb3383573dece741a839f4e1805760f3d","observation_id":"ed4543bd-a927-48dd-a61c-e5d7f3e4c482","resolution":{"observed_at":"2026-08-04T23:36:21.875531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.862666Z","title":"Variable rate image com- pression with recurrent neural networks,","venue":null,"work_id":"e81b47f0-b4a7-45c6-ab5b-5f559621e2b0","year":2016},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:15.026287Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:29a670827725c3b163d77310c71dea82c234581e4d8510ca9493565d48b31777","observation_id":"f2a84b58-6e88-4dce-8aa1-e964b6090a3c","resolution":{"observed_at":"2026-08-04T23:36:21.866103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.852752Z","title":"End-to-end optimized image compression,","venue":null,"work_id":"8fdaf302-aafc-40c2-87b8-d83a09e67a84","year":2017},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:15.184929Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:bc3a13134259391f8e3cea816f32d06be279307a1068f71f5c10b565bc585e21","observation_id":"9f3abfbe-5dd2-4579-9ef5-de332fd399a5","resolution":{"observed_at":"2026-08-04T23:36:21.855860Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:15.383559Z","title":"Channel-wise autoregressive entropy models for learned image compression,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:15.383559Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:7f92080ba820a6a21f8e0c49b1ef736151fbfc0833095a47a8fbbee29a082b3d","observation_id":"4737435c-8e27-47ea-be08-9ed041df3b06","resolution":{"observed_at":"2026-08-04T23:36:15.383559Z","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":"2026-08-04T23:36:21.838600Z","title":"Deep residual learning for image compression","venue":null,"work_id":"eb9e6f3f-4c5a-4948-9817-9cd8150ded4d","year":2019},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:15.438114Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:d0d4e7de5bbe3c8e062498171c7c9bef85f41ab109800aaf4ca22b36fa24c33a","observation_id":"99d472d3-6acf-4e88-9863-58f42fa81bfb","resolution":{"observed_at":"2026-08-04T23:36:21.842310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.830447Z","title":"The devil is in the details: Window- based attention for image compression,","venue":null,"work_id":"090894ef-8a67-4871-976e-164384418c90","year":2022},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:15.560044Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:c699bb3eccdf323ff26173a0e85d63c6e5ccfcde5d6c6e3c1f865af1362242c0","observation_id":"3122f9c8-4b32-4148-80e7-24ee149a81ed","resolution":{"observed_at":"2026-08-04T23:36:21.833403Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.821556Z","title":"Linear attention modeling for learned image compression,","venue":null,"work_id":"b45f9270-bc24-4216-a2c5-cf26d0fc4336","year":2025},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:15.731710Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:137bc55377713eaba9e481c6bbf94bba7b0e88e8d38f5c8daa0d8f7be65c77b3","observation_id":"d6f5da61-d568-42ee-9bff-9d498ab3a6f0","resolution":{"observed_at":"2026-08-04T23:36:21.825130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.813005Z","title":"Mambaic: State space models for high-performance learned image compression,","venue":null,"work_id":"17a0faba-db02-4463-a3f6-5d9e15694157","year":2025},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:15.859796Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:840526dfeb8e915497bb7e1e2e9e698af48710a24c7c7a15da24f60ca8e7eb63","observation_id":"d23b4504-2179-423c-9817-cf5ca0d2e8a9","resolution":{"observed_at":"2026-08-04T23:36:21.816218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.803039Z","title":"Learning convolutional networks for content-weighted image compression,","venue":null,"work_id":"e1e6b583-8375-4b14-a27a-7574d916d85a","year":2018},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:15.947344Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:fb1d46ef007cda8f91dfac2b7ea0b1f128445bd1c4ee059c2db68a3c42107eda","observation_id":"d0c2b6b6-f3cb-4918-9a53-adbaec7e44bf","resolution":{"observed_at":"2026-08-04T23:36:21.806862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.792897Z","title":"Entroformer: A transformer-based entropy model for learned image compression,","venue":null,"work_id":"2b28ff95-fe79-4e11-a1eb-8901c97ed6b7","year":2022},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:16.018411Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:43b89f6dda0ea8ed54a1815be2310c95efb8cf905eda16cd05874372e80d3b5d","observation_id":"d9e5a7cf-5aba-4c36-b4c0-8a3c3484c0d4","resolution":{"observed_at":"2026-08-04T23:36:21.795956Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.783963Z","title":"Contextformer: A transformer with spatio-channel attention for context modeling in learned image compression,","venue":null,"work_id":"bee868af-18f2-4e45-85b3-c59506bf5b7e","year":2022},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:16.112624Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:2bbe4c7a9a6bf7c53035b8d8e2b1731f01c4f1dbf373921c788874e363a81a92","observation_id":"b60369d9-34f3-4e54-8596-7ec452835d0d","resolution":{"observed_at":"2026-08-04T23:36:21.787173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.06550","last_updated":"2018-06-18T08:45:36Z","snapshot_observed_at":"2026-08-14T19:02:37.094745Z","submitted_at":"2018-06-18T08:45:36Z","title":"Charge tunable structural phase transitions in few-layer tellurium: a step toward building mono-elemental devices","version":1},"cited_work":{"arxiv_id":"1806.06550","doi":null,"metadata_source":"pith","pith_arxiv_id":"1806.06550","snapshot_observed_at":"2026-08-04T23:36:21.226828Z","title":"Charge tunable structural phase transitions in few-layer tellurium: a step toward building mono-elemental devices","venue":"cond-mat.mtrl-sci","work_id":"41c4548b-03c4-4487-b557-631fa7017bc4","year":2018},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:16.183656Z"},"links":{"cited_paper":"/paper/1806.06550","citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:775b6ecbd3f482eb330a04fee0a07dd4a8d52574751448c9604b86012a8956c6","observation_id":"11c5bf83-8739-44d9-bc9f-9349347901e2","resolution":{"observed_at":"2026-08-04T23:36:21.282159Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.775597Z","title":"QARV: Quantization-aware resnet vae for lossy image compression,","venue":null,"work_id":"1540bc25-5ed9-4109-896e-6a122d47f2a8","year":2024},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:16.281197Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:4df7b691fffe7b08aeab1436d76f1caed316bdfe0836609f1cb8c2110e0346d6","observation_id":"4a5a4362-31c2-4919-81b4-e7053b4732af","resolution":{"observed_at":"2026-08-04T23:36:21.778817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.764873Z","title":"Qarv++: An improved hierarchical vae for learned image compression,","venue":null,"work_id":"12a297fd-03bf-464c-9f97-4fe9021ad415","year":2026},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:16.342021Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:91e4f7d0ea2a3d686b9db3ffc869ee670019468a9e8bd1de0230e87c3fff9f1d","observation_id":"51fb62ba-19b6-45bc-b486-d8ef5af76ff0","resolution":{"observed_at":"2026-08-04T23:36:21.769358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.756501Z","title":"Lossy compression for lossless prediction,","venue":null,"work_id":"0939b3aa-f585-48fa-b69a-01729d4f5345","year":2021},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:16.440340Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:28bdb80e19817927255ba583bcaf022ed59275d7d05faa814d4dcf4c4f45286d","observation_id":"d49bc6c2-79c3-4ff8-9cf3-861a2fb67805","resolution":{"observed_at":"2026-08-04T23:36:21.759660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:16.528731Z","title":"Deep joint source- channel coding for wireless image transmission,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:16.528731Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:31d4c4a9c036b439ab12b459cd43b29a32b60f6ddc80e814d3916238f0ab2d95","observation_id":"ace756e7-0f46-4faf-903e-d937c9ccd1b8","resolution":{"observed_at":"2026-08-04T23:36:16.528731Z","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":"2026-08-04T23:36:21.742897Z","title":"Transtic: Transferring transformer-based image compression from human perception to machine perception,","venue":null,"work_id":"3d46b2c4-2a45-40e4-9314-8d60e61cbf05","year":2023},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:16.633033Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:255407f1597a08b93cc9816f7a5789145e1ca1f9dc236109d9e661445ed04dee","observation_id":"a6aaed90-8de6-4418-8e2e-69344c8bcafd","resolution":{"observed_at":"2026-08-04T23:36:21.746200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.734292Z","title":"Perception-oriented latent coding for high-performance compressed domain semantic inference,","venue":null,"work_id":"1248be3c-fa7f-45bf-b03c-6d15ff8dc92d","year":2025},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:16.736144Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:d31ec810c9c361e63a8f9db7a6f4b5c79e519dbb03ad240a6e026bfdf8700945","observation_id":"301ff777-7217-4cf4-9abb-0669642f6390","resolution":{"observed_at":"2026-08-04T23:36:21.737608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.725590Z","title":"Lossy image compression with conditional dif- fusion models,","venue":null,"work_id":"20ddc2ec-c4b2-4555-9eea-047aa69671a2","year":2023},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:16.831856Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:dd73da5c8dfbfd7a67f63106696fffb1f584c2731b34e32b01600eea3b8979e2","observation_id":"949b9cd8-fd90-4000-9ef4-57d9ec71fb24","resolution":{"observed_at":"2026-08-04T23:36:21.728705Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.716472Z","title":"Lossy image compression with foundation diffusion models,","venue":null,"work_id":"71a829dc-b8e0-448e-a859-30eda4128069","year":2024},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:16.902194Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:36201ee700a3b6f5908a206a3ef21b00b2f953fea70866846fed4847f541268c","observation_id":"549045ea-0a72-4bcb-bf65-a2af02424491","resolution":{"observed_at":"2026-08-04T23:36:21.719741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:16.996120Z","title":"Yoda: Yet another one-step diffusion-based video compressor,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:16.996120Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:f8d95393027195dfa6daadce887e6b314f9afeac0f5d3245efefc79769096293","observation_id":"025f3c53-6f51-44c5-9dc0-01ee4fcc9088","resolution":{"observed_at":"2026-08-04T23:36:16.996120Z","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":"2026-08-04T23:36:21.707789Z","title":"Toward extreme image compression with latent feature guidance and diffusion prior,","venue":null,"work_id":"8fd7acdc-5b9f-4196-82cf-7b1b135d74af","year":2024},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:17.108602Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:96eb2ff67a0970e8769f7b62b648e3c2d0c2bb9959614f8398d7715106ecb777","observation_id":"94851c27-cdd0-4252-a770-5545715cefb7","resolution":{"observed_at":"2026-08-04T23:36:21.710966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.698959Z","title":"1.1 computing’s energy problem (and what we can do about it),","venue":null,"work_id":"824830b0-f8f6-47d1-abfb-989b5ca26440","year":2014},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:17.346690Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:eaa06ba29fb4f9bcecf5b17949dd0b1a050e9c6e9dc8eb13071975cdfd0faa52","observation_id":"5688a875-89d5-4260-9d42-b05a49cbc288","resolution":{"observed_at":"2026-08-04T23:36:21.702548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.689733Z","title":"Low-bit quantiza- tion of neural networks for efficient inference,","venue":null,"work_id":"d6294a63-c438-4013-a02e-a7c3678e72cb","year":2019},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:17.572914Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:67cfce55df12f02b2f16c0e3766b33a80df7228b43905128272e64bf6c8992a6","observation_id":"b4f21d5e-9b2c-49ca-a984-ef86802f77e7","resolution":{"observed_at":"2026-08-04T23:36:21.692866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.00532","last_updated":"2019-06-07T16:27:47Z","snapshot_observed_at":"2026-08-14T16:22:12.102152Z","submitted_at":"2019-06-03T02:29:22Z","title":"Efficient 8-Bit Quantization of Transformer Neural Machine Language Translation Model","version":2},"cited_work":{"arxiv_id":"1906.00532","doi":null,"metadata_source":"pith","pith_arxiv_id":"1906.00532","snapshot_observed_at":"2026-08-04T23:36:21.056312Z","title":"Efficient 8-Bit Quantization of Transformer Neural Machine Language Translation Model","venue":"cs.LG","work_id":"b4fe88b3-1b3e-40a5-9176-9d53003f4aed","year":2019},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:17.721553Z"},"links":{"cited_paper":"/paper/1906.00532","citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:feefd5d6a22567bb3d6a7066de495376df22fe16a04289267f3f5f16b319ce61","observation_id":"f04f5448-cad6-49df-a76f-a190d1d5aa7a","resolution":{"observed_at":"2026-08-04T23:36:21.101736Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:17.946597Z","title":"Advances in the neural network quantization: A comprehensive review,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:17.946597Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:a8cf5b1e40760d1c0c237548f83b65c9b1c426724b96f6c6d0a2e2e90faa2dd6","observation_id":"88a25232-f930-4fa5-bc9d-7d0aac8b9082","resolution":{"observed_at":"2026-08-04T23:36:17.946597Z","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":"2026-08-04T23:36:18.063751Z","title":"Gpt3. int8 (): 8-bit matrix multiplication for transformers at scale,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:18.063751Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:becac1c2f1e53c39bd16723750e3e15de73823ceb18ebd1183d75fb12455bb4b","observation_id":"d49937c4-3ee1-41d4-bea4-a12a3f082308","resolution":{"observed_at":"2026-08-04T23:36:18.063751Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.11729","last_updated":"2025-02-17T12:13:21Z","snapshot_observed_at":"2026-08-16T12:57:45.062330Z","submitted_at":"2025-02-17T12:13:21Z","title":"On Quantizing Neural Representation for Variable-Rate Video Coding","version":1},"cited_work":{"arxiv_id":"2502.11729","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.11729","snapshot_observed_at":"2026-08-04T23:36:20.895360Z","title":"On Quantizing Neural Representation for Variable-Rate Video Coding","venue":"eess.IV","work_id":"099ab610-601f-4511-9743-4855da6decce","year":2025},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:18.286752Z"},"links":{"cited_paper":"/paper/2502.11729","citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:e6174685f294b71055fa960073f6f560fa11983c86ff4bb1df5ae03e6888f787","observation_id":"8a209113-84f0-4dcb-a697-93a7b6b5b624","resolution":{"observed_at":"2026-08-04T23:36:20.987529Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.669899Z","title":"Hawq: Hessian aware quantization of neural networks with mixed-precision,","venue":null,"work_id":"b689e780-7bb7-4244-950f-542815462407","year":2019},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:18.572612Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:4cc294aaf5371ee1f25dfc20d1cc7cbd3a4ca8733b16c0765c6acfd60965d7f8","observation_id":"c9a3eed1-fade-4487-ba47-0b87b176a349","resolution":{"observed_at":"2026-08-04T23:36:21.674283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.02915","last_updated":"2022-06-06T21:31:32Z","snapshot_observed_at":"2026-08-16T22:50:46.445252Z","submitted_at":"2022-06-06T21:31:32Z","title":"8-bit Numerical Formats for Deep Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.02915","snapshot_observed_at":"2026-08-04T23:36:18.802917Z","title":"8-bit numeri- cal formats for deep neural networks,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:18.802917Z"},"links":{"cited_paper":"/paper/2206.02915","citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:d8a7f455b55f7b772c608cdd679b80b6f80f8b5a75e27af557066350438f88b3","observation_id":"3424be5e-032d-435d-a309-a2ae40253e98","resolution":{"observed_at":"2026-08-04T23:36:18.802917Z","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":"2026-08-04T23:36:21.659965Z","title":"Squeezellm: Dense-and-sparse quantization,","venue":null,"work_id":"cc6fdc38-cf51-4156-8d50-f7a984fba381","year":2021},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:18.875107Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:9cb73fcdd5f6b5f47ddfb2e651c5ba395e0162046da202e4920711c3685e130d","observation_id":"417754dc-f9be-4bfb-a466-df0eed12b46f","resolution":{"observed_at":"2026-08-04T23:36:21.664178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:18.925631Z","title":"Smoothquant: Accurate and efficient post-training quantization for large language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:18.925631Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:d81c9ab51b1a57384e2dc7d9cbeb66fd305dd3b6eeb6aec2181efd5d42f36ce3","observation_id":"bc082521-5fcf-43a3-93c6-160bced0591e","resolution":{"observed_at":"2026-08-04T23:36:18.925631Z","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":"2026-08-04T23:36:19.006158Z","title":"Quarot: Outlier-free 4-bit inference in rotated llms,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:19.006158Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:353c5b27ace3609e6056b70d56e5019d295fa13f18f55bcc64cad070a5d1ca5a","observation_id":"00256c05-f978-4e6f-95af-f3dfff10424b","resolution":{"observed_at":"2026-08-04T23:36:19.006158Z","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":"2026-08-04T23:36:21.641185Z","title":"Spinquant: Llm quantization with learned rotations,","venue":null,"work_id":"52f0fa1c-c760-43d6-9c57-92a948a275b7","year":2025},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:19.058982Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:9edebe33b20a5ac01dbf57ee99abf318862781660959df82e54e50fb7c347780","observation_id":"feb69c20-761a-486b-b8e4-090075b4e777","resolution":{"observed_at":"2026-08-04T23:36:21.645160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.632668Z","title":"QuIP#: Even better LLM quantization with hadamard incoherence and lattice codebooks,","venue":null,"work_id":"2db6f77f-9315-4dcb-b3fb-6cab4bbb3500","year":2024},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:19.110874Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:24738d5489c8cf47b23ee3512830fe531889920b3a802e2b21074caa8413cad0","observation_id":"0525b83a-7096-4acd-89ad-a03aa56ac4d6","resolution":{"observed_at":"2026-08-04T23:36:21.635793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.623563Z","title":"Integer networks for data compression with latent-variable models,","venue":null,"work_id":"e7766649-5b0e-4f8b-854e-be6a85a25aa8","year":2019},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:19.163284Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:70a9844813cf0fab5e56bfdb474a6d55db257ec9aac1f819fc1b2c80292135bc","observation_id":"85269343-a634-41a0-92f6-949f6f1eabf2","resolution":{"observed_at":"2026-08-04T23:36:21.626729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.613833Z","title":"Integer quantized learned image compression,","venue":null,"work_id":"a848f57a-faf8-41e6-9cc1-9408bc33e4ac","year":2023},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:19.215125Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:41bdb4d78c30a0d92a0ee143948f29c252e4e441074d72494f510cf3de07f0d3","observation_id":"79ba9ab2-7c19-4442-ae93-e7feec88e978","resolution":{"observed_at":"2026-08-04T23:36:21.617346Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.603055Z","title":"Structured pruning and quantization for learned image compression,","venue":null,"work_id":"87550686-db47-479a-8454-b3ad81420cff","year":2024},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:19.318228Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:97dcbede30652effbfea0533965af623c79d2bad3f0fdc9447b8be84b298da52","observation_id":"2b153be8-9f4b-459d-be06-19013b6f490a","resolution":{"observed_at":"2026-08-04T23:36:21.607450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.592946Z","title":"Variable-rate learned image compression with integer-arithmetic-only inference,","venue":null,"work_id":"51925324-b1e4-438e-b813-309199531c43","year":2025},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:19.372078Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:624acd741a20b4f455e4bed4d9316560a63056c1ea870519d483a6bc9cdf693a","observation_id":"916afe06-bb3a-4686-aff6-a9be9258ccde","resolution":{"observed_at":"2026-08-04T23:36:21.596715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.583369Z","title":"Seberry and M","venue":null,"work_id":"0f4f2075-ac07-4eca-9f10-f7557667f853","year":2020},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:19.444784Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:3d3f48d487c57b25122faf9e7d2f54e41cf493c5f8d8d237e0b9144f2e3337ae","observation_id":"eff9f145-234d-455e-b46e-aecb63c5eeb9","resolution":{"observed_at":"2026-08-04T23:36:21.586673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.568390Z","title":"On the meaning and use of kurtosis","venue":null,"work_id":"79a0874a-7bac-4c31-b0b1-5a7925012eeb","year":1997},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:19.501007Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:e46f415c748cd6e91ebb6ce018ad63fef781f9da11e823fcecea3b289c9cd76e","observation_id":"34d4a19c-d5fb-4c3b-ad6f-c5b90a079664","resolution":{"observed_at":"2026-08-04T23:36:21.574843Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.546021Z","title":"Normality testing methods and the importance of skewness and kurtosis in statistical analysis,","venue":null,"work_id":"068c7595-cdba-4843-a13e-b48282751794","year":2022},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:19.554846Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:58bb7a3370424740287340d003a7154a02021c5c9091a86bcdd49bb9f67b83f8","observation_id":"59d383b0-cb3f-4f2a-ba00-2f524d6da0f3","resolution":{"observed_at":"2026-08-04T23:36:21.557763Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.08415","last_updated":"2023-06-06T01:53:32Z","snapshot_observed_at":"2026-08-13T19:48:28.322536Z","submitted_at":"2016-06-27T19:20:40Z","title":"Gaussian Error Linear Units (GELUs)","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.08415","snapshot_observed_at":"2026-08-04T23:36:19.634016Z","title":"Gaussian error linear units (gelus),","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:19.634016Z"},"links":{"cited_paper":"/paper/1606.08415","citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:f97dd9117e8ce4c6b6b305234456d1197cc4eecc64d46f5619eb376008399c5a","observation_id":"d61c76d7-d7b9-4a18-a18d-e97454300134","resolution":{"observed_at":"2026-08-04T23:36:19.634016Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.06281","last_updated":"2016-02-29T21:07:30Z","snapshot_observed_at":"2026-08-14T22:22:11.341571Z","submitted_at":"2015-11-19T17:52:01Z","title":"Density Modeling of Images using a Generalized Normalization Transformation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.06281","snapshot_observed_at":"2026-08-04T23:36:19.658730Z","title":"Density modeling of images using a generalized normalization transformation,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:19.658730Z"},"links":{"cited_paper":"/paper/1511.06281","citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:f393638fe583a07a414661c18c138f76fd763fab4f942513c6d345160aa34148","observation_id":"ce127892-92e6-488a-8e59-14f1d87cee54","resolution":{"observed_at":"2026-08-04T23:36:19.658730Z","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":"2026-08-04T23:36:19.818327Z","title":"Hadamard matrices and their applica- tions,","venue":null,"work_id":null,"year":1978},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:19.818327Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:6cd3ac3d0c460c057bb898de10775405af01bb33aefae524db0c268c841c5a09","observation_id":"a70b463d-4849-4c65-b30f-0ebb4880f6db","resolution":{"observed_at":"2026-08-04T23:36:19.818327Z","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":"2026-08-04T23:36:21.518780Z","title":"Up or down? adaptive rounding for post-training quantization,","venue":null,"work_id":"a20db01e-4145-4055-aa12-94b5165ea455","year":2020},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:19.962277Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:d172fa07e65a30ba15985a8f93c65c7461c0cbe7d74500d7ca31ab382f7babc6","observation_id":"a6c74245-6412-454c-a4cf-6cb87781fd83","resolution":{"observed_at":"2026-08-04T23:36:21.527911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.497821Z","title":"BRECQ: Pushing the limit of post-training quantization by block reconstruction,","venue":null,"work_id":"7a615242-4eb6-43e3-a4e3-0754065c4ea9","year":2021},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:20.034545Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:e294ee3b0c0ff23f0588ce845a41b042cd50ae89322ddd08ccf38686bab873a0","observation_id":"64c38d8e-a953-4769-abbf-586e0dcf1773","resolution":{"observed_at":"2026-08-04T23:36:21.507041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.478850Z","title":"On quantizing neural representation for variable-rate video coding,","venue":null,"work_id":"15cc9282-df2d-480e-98b6-b068a3b451c6","year":2025},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:20.149082Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:25e40974721be867f34bd19d65f3aab3940cb743421a38bd94103104f2059e11","observation_id":"9ffa37cc-ba7f-4318-b7fb-1b0bbe45e01e","resolution":{"observed_at":"2026-08-04T23:36:21.487372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.462995Z","title":"Mlic: Multi- reference entropy model for learned image compression,","venue":null,"work_id":"f14aa7ca-bc46-4ad5-b993-773a4bf29588","year":2023},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:20.260439Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:28b7f7a8f935e35530c3796e4b1642dced2f9e18d77f1ebdf16520c47dd87eb7","observation_id":"e2689950-fe67-4aa1-ac07-2acb052d3780","resolution":{"observed_at":"2026-08-04T23:36:21.467913Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.445073Z","title":"Ntire 2017 challenge on single image super-resolution: Dataset and study,","venue":null,"work_id":"476e26d9-11d8-4e6b-a88b-f35f3c828b03","year":2017},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:20.333420Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:f9b535cdf2db827df59942c23ab19c642b70275cc42510d387e026f3dc9750ba","observation_id":"20db8065-7164-4896-bf09-eb6da0a5d9dd","resolution":{"observed_at":"2026-08-04T23:36:21.453391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.424139Z","title":"Kodak lossless true color image suite (photocd pcd0992),","venue":null,"work_id":"c44deb5f-5e60-4254-a84d-de7d666c8b9e","year":1993},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:20.443594Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:387db611560b4159ebf8b6c67ca852708991dccf31014b4603cbcf91cf4ed803","observation_id":"7e013a9c-685f-4b80-99ed-73c03a604cdd","resolution":{"observed_at":"2026-08-04T23:36:21.433174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.403540Z","title":"Testimages: a large-scale archive for testing visual devices and basic image processing algorithms","venue":null,"work_id":"bccafcd3-ef57-42e0-bb2b-498aabd265ea","year":2014},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:20.552499Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:e238d80c81ffe0deb70b99e5ef0550f890a152fa7d04d358e824696b1f746054","observation_id":"59e31f7c-ea08-4b68-ae69-d4e60e6aa483","resolution":{"observed_at":"2026-08-04T23:36:21.410448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:21.389288Z","title":"Workshop and challenge on learned image compression (clic2020),","venue":null,"work_id":"41a86cf0-ab6f-45f7-8c65-d45f6a987780","year":2020},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:20.663997Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:8bac2f17740d3cd2912739bba91361de5d4f979a3563de10babd33a61122a6e5","observation_id":"efaab4dc-4501-4780-8320-4c1df271a4a0","resolution":{"observed_at":"2026-08-04T23:36:21.395276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-04T23:36:20.778882Z","title":"Calculation of average psnr differences between rd- curves,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:20.778882Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:9a0cd981ae87f950363364469f68f3760f2ff6a713d309a3f8e0f7edcd924e02","observation_id":"fa8ebb7a-23ae-4d0b-840e-178b591024b3","resolution":{"observed_at":"2026-08-04T23:36:20.778882Z","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":"2026-08-04T23:36:21.361421Z","title":"Constructions of hadamard matrices,","venue":null,"work_id":"1d1b1683-3f41-4029-ab8b-56cde8ba33f5","year":2019},"citing_paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-04T23:36:20.831364Z"},"links":{"citing_paper":"/paper/2608.01653"},"observation_digest":"sha256:49c697890fbc6b8a7f7096a2b6ce66b24b8b1caa65820cfe6c9de5e678b51455","observation_id":"bb5d7ee8-51ea-4aea-a422-2048c8994b32","resolution":{"observed_at":"2026-08-04T23:36:21.372057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.01653","last_updated":"2026-08-03T03:44:13Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-17T01:06:57.578869Z","submitted_at":"2026-08-03T03:44:13Z","title":"Hadamard-Domain Model Quantization for Learned Image Coding"},"reference_resolution":{"displayed":95,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":33,"verified_exact":2,"verified_fuzzy":59},"total_outbound_references":95},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 95 of 95 outbound references and 0 inbound Pith citation observations for arXiv:2608.01653."}