{"as_of":"2026-08-18T02:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a2768621e42746efac63251dc32e24df65e8237618a816cb7e227b97a2f889ef","coverage":[{"denominator":51,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:55:14.383990Z","state":"measured"},{"denominator":51,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":51,"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/2507.09687/citation-record","integrity":"/paper/2507.09687/integrity","json":"/paper/2507.09687/citation-record.json","paper":"/paper/2507.09687"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:55:15.588279Z","title":"Pattern recognition and machine learning","venue":null,"work_id":"2e6c44d9-b210-49fe-88fb-d653bc2f6464","year":2006},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.103523Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:d34d438b34ab19aabac33f3854e5e7bc9b708dfeafa725f463642bd5715789a7","observation_id":"65f7db7a-ffd4-43ae-9783-bad233b30ecf","resolution":{"observed_at":"2026-08-06T17:55:15.593925Z","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":"2109.12948","last_updated":"2021-09-27T10:57:18Z","snapshot_observed_at":"2026-08-16T17:53:49.508131Z","submitted_at":"2021-09-27T10:57:18Z","title":"Understanding and Overcoming the Challenges of Efficient Transformer Quantization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.12948","snapshot_observed_at":"2026-08-06T17:55:14.109364Z","title":"Understanding and overcoming the challenges of efficient transformer quantization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.109364Z"},"links":{"cited_paper":"/paper/2109.12948","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:043921ad05d529b7e026c08df25345f21db6561479001457b185c0b0273d14f3","observation_id":"46096209-e9cd-482a-8745-83417ccf525d","resolution":{"observed_at":"2026-08-06T17:55:14.109364Z","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":"2026-08-06T17:55:15.570281Z","title":"Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, march 2023","venue":null,"work_id":"dd80954b-9332-4d0a-a52c-ab5f45b7a680","year":2023},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.116231Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:292f3b033e743edc1abe1685383e593831ff7dc822b25c3c6f3647789a83c556","observation_id":"0255ca02-8f77-4275-b247-daf081271c35","resolution":{"observed_at":"2026-08-06T17:55:15.577146Z","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":"2026-08-06T17:55:15.547383Z","title":"A comparative survey of instance selection methods applied to non-neural and transformer-based text classification","venue":null,"work_id":"9a8be9e2-d804-4f3c-8eab-d4501c48910d","year":2023},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.122426Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:62cd5d79d1dc4e06cc149c45c4ed1152e2b7cc607f2d360215838cc7d0c42454","observation_id":"6e8fbcc4-ee55-4852-9ccd-3f435a5cd2a4","resolution":{"observed_at":"2026-08-06T17:55:15.553991Z","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":"1910.00382","last_updated":"2019-10-01T13:45:56Z","snapshot_observed_at":"2026-08-09T21:59:00.116141Z","submitted_at":"2019-10-01T13:45:56Z","title":"Latent-Variable Generative Models for Data-Efficient Text Classification","version":1},"cited_work":{"arxiv_id":"1910.00382","doi":null,"metadata_source":"pith","pith_arxiv_id":"1910.00382","snapshot_observed_at":"2026-08-06T17:55:15.081383Z","title":"Latent-Variable Generative Models for Data-Efficient Text Classification","venue":"cs.CL","work_id":"20bcf4f6-4168-409d-b791-854935b21d05","year":2019},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.128158Z"},"links":{"cited_paper":"/paper/1910.00382","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:d257a9fe0db9984ca899311a4c40258bd8dbff7dd8ead405c2bc6a6bfee42689","observation_id":"bcfe193e-0b5d-4b9d-81e7-5ce229546182","resolution":{"observed_at":"2026-08-06T17:55:15.087253Z","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":"2311.05052","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:55:15.039977Z","title":"Matrix completion via memoryless scalar quantization","venue":null,"work_id":"a131cf00-74af-4043-a8e8-ee5642a7e326","year":2023},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.133825Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:f877970f20eaf724c2d4a05c15bdec2d6064ace44b973d6be03748ee3ef295e1","observation_id":"388b0c98-b6e7-46b0-8b5a-7f8ea005b5f4","resolution":{"observed_at":"2026-08-06T17:55:15.052598Z","resolver_source":"raw_fallback","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":{"arxiv_id":"1902.08153","last_updated":"2020-05-07T03:30:49Z","snapshot_observed_at":"2026-08-14T17:13:05.593256Z","submitted_at":"2019-02-21T17:31:32Z","title":"Learned Step Size Quantization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.08153","snapshot_observed_at":"2026-08-06T17:55:14.139337Z","title":"Learned step size quantization","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.139337Z"},"links":{"cited_paper":"/paper/1902.08153","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:685afb13d5a04b49385d432d8c6deea97dd93b57b36f09d041947225156ab5b8","observation_id":"8b6f505e-be6e-4521-be51-583d6161aa85","resolution":{"observed_at":"2026-08-06T17:55:14.139337Z","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":"2026-08-06T17:55:14.145089Z","title":"Xilinx/brevitas","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.145089Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:0cf48aa7ee8214d7203b9a1d1cf5a3813d9d976c22619287d3862773ec1cf48e","observation_id":"625a0bd6-d97e-48ee-ab1c-f513a4570235","resolution":{"observed_at":"2026-08-06T17:55:14.145089Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-06T17:55:15.524590Z","title":"The elements of statistical learning: data mining, inference, and prediction","venue":null,"work_id":"4655d5ad-e262-40a2-8b9b-f4fb28cac191","year":2009},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.150005Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:81346bc714032df18fa0edcc412b1f177a5770d7c2642491e7196a9893f65caa","observation_id":"a291fdc2-735c-4b5b-b9ba-09f0e437f211","resolution":{"observed_at":"2026-08-06T17:55:15.531437Z","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":"2026-08-06T17:55:15.505011Z","title":"Long short-term memory","venue":null,"work_id":"e5244945-b2ef-4843-aca6-42d1593c8e0b","year":1997},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.155842Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:fd815c591a20a92d4b4253dd0c8c12ebb4a86e8cb70cd5706b755f0ec9535d4a","observation_id":"38eab526-e852-40c8-a084-27db2344c68a","resolution":{"observed_at":"2026-08-06T17:55:15.511047Z","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":"2026-08-06T17:55:15.483712Z","title":"spacy: Industrial-strength natural language processing in python","venue":null,"work_id":"776f39b2-9768-4e75-a67e-3fbfab1e6fb7","year":2020},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.161121Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:9de54801c695d7afc079e6e54136942cb175452da9a10f080a155d1d3b81b239","observation_id":"bb0750e7-3af5-47f0-aed8-1504af057dce","resolution":{"observed_at":"2026-08-06T17:55:15.489890Z","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":"2026-08-06T17:55:15.464723Z","title":"Fastai: A layered api for deep learning","venue":null,"work_id":"d90171df-d9bd-4e4e-9f42-32b420814b41","year":2020},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.166940Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:86ff6804673267da26ffdb02cf37ae5908a4644a65998e2b5e6c889ee94246f3","observation_id":"b9b26f11-88d9-43df-b9dd-b93422b560d2","resolution":{"observed_at":"2026-08-06T17:55:15.470431Z","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":"2210.16621","last_updated":"2022-10-29T14:51:41Z","snapshot_observed_at":"2026-08-16T16:20:06.913608Z","submitted_at":"2022-10-29T14:51:41Z","title":"Empirical Evaluation of Post-Training Quantization Methods for Language Tasks","version":1},"cited_work":{"arxiv_id":"2210.16621","doi":null,"metadata_source":"pith","pith_arxiv_id":"2210.16621","snapshot_observed_at":"2026-08-06T17:55:14.939151Z","title":"Empirical Evaluation of Post-Training Quantization Methods for Language Tasks","venue":"cs.CL","work_id":"8ee54b34-70fd-492a-8b51-d5946f53fcc0","year":2022},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.172635Z"},"links":{"cited_paper":"/paper/2210.16621","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:83adbff2553654ec234faa7bae9c696064e886e51a3e55ec1faf19af6f661f27","observation_id":"944ed20b-e9b6-467c-9992-3e43d4af3857","resolution":{"observed_at":"2026-08-06T17:55:14.945323Z","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":"2026-08-06T17:55:15.440480Z","title":"Accurate post training quantization with small calibration sets, in: International Conference on Machine Learning, PMLR","venue":null,"work_id":"2830d75b-8d12-40c8-af32-adc68dcac824","year":2021},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.177587Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:42592236c2bdddcd55fb25c9c4ee7928eebf8481aef6649a359225dad839c05b","observation_id":"ce41274b-dc62-48e1-bffc-21015ffebe91","resolution":{"observed_at":"2026-08-06T17:55:15.449235Z","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":"2026-08-06T17:55:15.421349Z","title":"Speech and Language Processing","venue":null,"work_id":"5db6d6e1-150f-4046-bda8-fce6d6f6b748","year":2023},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.182938Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:92903f27a57f02bb119c48ac2eb7565f38040c84df6a937e5db6ece902887218","observation_id":"fa0b2d82-b89c-42b6-83e7-93970c07896e","resolution":{"observed_at":"2026-08-06T17:55:15.427205Z","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":"2026-08-06T17:55:15.405752Z","title":"Sulla determinazione empirica di una legge di distribuzione","venue":null,"work_id":"d25e318a-4e74-4af6-bcb6-c67be8cd409d","year":1933},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.187594Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:d7141eb6174c67d675c1c61cafb32795c7ad4a6a94708197e0bf3a9b33e1f84e","observation_id":"eaebde46-d846-4bff-8456-6d011fbed0dd","resolution":{"observed_at":"2026-08-06T17:55:15.410584Z","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":"2026-08-06T17:55:15.390987Z","title":"Generative models improve fairness of medical classifiers under distribution shifts","venue":null,"work_id":"0f7e5b49-03ae-41a9-be38-6775a0d5a29d","year":2024},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.192133Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:907004339398ee18af368af4c8e3c4ba230862bf70280125f8be19ac001b683b","observation_id":"6410144a-5d72-4ea3-ab49-482e6011dcdd","resolution":{"observed_at":"2026-08-06T17:55:15.395699Z","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":"2026-08-06T17:55:15.373558Z","title":"Precision and recall metrics for generative models, in: Advances in Neural Information Processing Systems","venue":null,"work_id":"577d3caa-3619-4dca-8a10-b38888d4754c","year":2019},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.196753Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:ead3eb402d4316a0259d1a6014bbcb629c1606ff85468ecdcad8acdc49113d16","observation_id":"ca045df6-9332-4a61-aca0-78a1c7cd720b","resolution":{"observed_at":"2026-08-06T17:55:15.379315Z","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":"2026-08-06T17:55:15.357285Z","title":"Robust inference via generative classifiers for handling noisy labels, in: Proceedings of the 36th International Conference on Machine Learning (ICML)","venue":null,"work_id":"b3dc8a04-3823-41c5-a214-a0527ecbc85e","year":2019},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.201164Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:1d1920ad86c82acb3197330a3b93bfd8dcf6b99810715e8d2f1af61ffa47c6f6","observation_id":"7a354b85-64b5-4654-a7a1-477b9bacf587","resolution":{"observed_at":"2026-08-06T17:55:15.362780Z","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":"2312.05725","last_updated":"2023-12-12T05:21:40Z","snapshot_observed_at":"2026-08-16T14:36:13.559740Z","submitted_at":"2023-12-10T02:14:34Z","title":"FP8-BERT: Post-Training Quantization for Transformer","version":2},"cited_work":{"arxiv_id":"2312.05725","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.05725","snapshot_observed_at":"2026-08-06T17:55:14.911580Z","title":"FP8-BERT: Post-Training Quantization for Transformer","venue":"cs.AI","work_id":"e637e467-c748-4e8a-b630-58576e77b9a5","year":2023},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.205775Z"},"links":{"cited_paper":"/paper/2312.05725","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:2015a4b6546491d9e39ae5a093a9ccd0c32bc6475d3827ec4e267dc7e7e3b4fd","observation_id":"2ffe9e50-70a2-4887-8de8-2489562af9c1","resolution":{"observed_at":"2026-08-06T17:55:14.919915Z","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":{"arxiv_id":"2311.09755","last_updated":"2024-08-12T17:57:00Z","snapshot_observed_at":"2026-08-16T14:42:44.681101Z","submitted_at":"2023-11-16T10:30:00Z","title":"On the Impact of Calibration Data in Post-training Quantization and Pruning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.09755","snapshot_observed_at":"2026-08-06T17:55:14.215638Z","title":"On the impact of calibration data in post-training quantization and pruning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.215638Z"},"links":{"cited_paper":"/paper/2311.09755","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:51f306244a07ad7a44e5a826d5d58196dcfe018f82a9f2d7322cb9fbd41c01d7","observation_id":"331c618e-2ccc-4c6e-bb5e-71621d0386a4","resolution":{"observed_at":"2026-08-06T17:55:14.215638Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.06552","last_updated":"2019-05-27T10:54:08Z","snapshot_observed_at":"2026-08-14T19:44:39.006193Z","submitted_at":"2018-02-19T08:58:00Z","title":"Are Generative Classifiers More Robust to Adversarial Attacks?","version":3},"cited_work":{"arxiv_id":"1802.06552","doi":null,"metadata_source":"pith","pith_arxiv_id":"1802.06552","snapshot_observed_at":"2026-08-06T17:55:14.866459Z","title":"Are Generative Classifiers More Robust to Adversarial Attacks?","venue":"cs.LG","work_id":"2eef59df-f9ac-4c69-9e85-f68237a77f1f","year":2018},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.222271Z"},"links":{"cited_paper":"/paper/1802.06552","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:19800ff94f3cf56096e8c60291c2684c9f31f31734c014787e353e205f010610","observation_id":"d38901f7-4e0e-42c7-80da-6369142d105e","resolution":{"observed_at":"2026-08-06T17:55:14.873141Z","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":{"arxiv_id":"2405.16406","last_updated":"2025-02-20T06:07:00Z","snapshot_observed_at":"2026-08-16T17:18:05.876361Z","submitted_at":"2024-05-26T02:15:49Z","title":"SpinQuant: LLM quantization with learned rotations","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16406","snapshot_observed_at":"2026-08-06T17:55:14.228612Z","title":"Spinquant: Llm quantization with learned rotations","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.228612Z"},"links":{"cited_paper":"/paper/2405.16406","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:41ddb051e9d7190ab3c07d861912d8f5a6cbbbf73236e7779049517c2f25f61a","observation_id":"b6ce0028-eb07-478c-a59e-f2fe7fb8fdfb","resolution":{"observed_at":"2026-08-06T17:55:14.228612Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.15979","last_updated":"2021-08-15T04:42:45Z","snapshot_observed_at":"2026-08-16T19:09:26.485784Z","submitted_at":"2020-10-29T22:53:10Z","title":"A Greedy Algorithm for Quantizing Neural Networks","version":2},"cited_work":{"arxiv_id":"2010.15979","doi":null,"metadata_source":"pith","pith_arxiv_id":"2010.15979","snapshot_observed_at":"2026-08-06T17:55:14.815516Z","title":"A Greedy Algorithm for Quantizing Neural Networks","venue":"cs.LG","work_id":"9c30cbd9-5fa3-4457-9f05-1fcc1ed947af","year":2020},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.234118Z"},"links":{"cited_paper":"/paper/2010.15979","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:2f168a53776ae9d1fdf22efe7b77346043f6c880abc55a00b0917fb7ac28c420","observation_id":"58f2f187-b49a-4e4f-9033-08cb895cbc1a","resolution":{"observed_at":"2026-08-06T17:55:14.821188Z","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":{"arxiv_id":"1301.3781","last_updated":"2013-09-07T00:30:40Z","snapshot_observed_at":"2026-07-06T03:04:11.148340Z","submitted_at":"2013-01-16T18:24:43Z","title":"Efficient Estimation of Word Representations in Vector Space","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1301.3781","snapshot_observed_at":"2026-08-06T17:55:14.241613Z","title":"Efficient estimation of word representations in vector space","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.241613Z"},"links":{"cited_paper":"/paper/1301.3781","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:4f94231d02db23270cece52a9ff0531be18f184a64f4d53c9ea85c7a781ce9ad","observation_id":"888c3692-9d08-4969-941d-4033b743e314","resolution":{"observed_at":"2026-08-06T17:55:14.241613Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.10568","last_updated":"2020-06-30T09:51:23Z","snapshot_observed_at":"2026-08-12T19:59:54.746291Z","submitted_at":"2020-04-22T13:44:28Z","title":"Up or Down? Adaptive Rounding for Post-Training Quantization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.10568","snapshot_observed_at":"2026-08-06T17:55:14.246829Z","title":"Up or down? adaptive round- ing for post-training quantization","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.246829Z"},"links":{"cited_paper":"/paper/2004.10568","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:ef198b682020c3c0acbac75ec05311b6b362d1cff78ace1ff48ccbe00a19ee7a","observation_id":"e7f21b11-fa44-4318-a874-3ac1e9ad4786","resolution":{"observed_at":"2026-08-06T17:55:14.246829Z","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":"2026-08-06T17:55:15.339506Z","title":"Data-free quantization through weight equalization and bias correction, in: Proceedings of the IEEE/CVF international conference on computer vision, pp","venue":null,"work_id":"c50111fd-1b68-4915-9c06-668837236908","year":2019},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.252008Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:71416e2479488fa96f574eaec429b95c93512358bbb0d4a0f475a39c9ab12e53","observation_id":"51bec0e9-5646-46f7-ae5e-8a4ce9a923ee","resolution":{"observed_at":"2026-08-06T17:55:15.344805Z","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":"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":"2026-08-06T17:55:14.257594Z","title":"A white paper on neural network quantization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.257594Z"},"links":{"cited_paper":"/paper/2106.08295","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:63c73dc808103d8b4f194f699945dc6adc357a4e83768ec784899a0eaa66d5f7","observation_id":"1df8a0b4-125a-40a9-8872-53a59e06fd3f","resolution":{"observed_at":"2026-08-06T17:55:14.257594Z","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":"2026-08-06T17:55:15.323158Z","title":"On discriminative vs","venue":null,"work_id":"35c38de3-0f10-46f5-ab5d-e5b96420e529","year":2001},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.262687Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:26f7222d8c7d8991130d86a7450707ba65821b80f8c8efe779564aaf8a7517a3","observation_id":"53311228-94fd-4d30-8973-7ffa0c13a32d","resolution":{"observed_at":"2026-08-06T17:55:15.328139Z","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":"2026-08-06T17:55:15.304789Z","title":"Deep neural networks are easily fooled: High confidence predictions for unrecognizable images, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp","venue":null,"work_id":"c61abd86-2996-452d-b826-fe9d0696435c","year":2015},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.267542Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:80f0259dd654ca7b434130084aa0dcf497a75a8f20bc57b8363d102d74d8049a","observation_id":"5ac9ae95-12ee-44e8-a135-85d2d06355b8","resolution":{"observed_at":"2026-08-06T17:55:15.311068Z","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":"2026-08-06T17:55:15.286927Z","title":"On estimation of a probability density function and mode","venue":null,"work_id":"78e85a21-97a8-4ea1-b20f-be56b3c4fa56","year":1962},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.272412Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:ebd11b7aa025479421fffd28b7d4383f6bd9301e2e3452e79caedc86ff0284f1","observation_id":"1bd34eb8-51bb-479d-b7c9-c818fa0daba7","resolution":{"observed_at":"2026-08-06T17:55:15.292566Z","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":"1912.01703","last_updated":"2019-12-03T22:06:05Z","snapshot_observed_at":"2026-07-06T08:41:49.632205Z","submitted_at":"2019-12-03T22:06:05Z","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.01703","snapshot_observed_at":"2026-08-06T17:55:14.277841Z","title":"Pytorch: An imperative style, high-performance deep learning library","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.277841Z"},"links":{"cited_paper":"/paper/1912.01703","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:f1ad640a29a02753e897db3f504b4c74d647a50bdb1d68278e34feb6e2152c56","observation_id":"3eba2354-6d5f-4372-bb35-fe3a4b70b722","resolution":{"observed_at":"2026-08-06T17:55:14.277841Z","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":"2026-08-06T17:55:15.268857Z","title":"GloVe: Global vectors for word representation, in: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp","venue":null,"work_id":"fc6661c6-1509-4e84-bd4b-d9dd6b06ffa0","year":2014},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.286107Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:2e7c36b6d4da847072c258f0e6514b72b87cd2bd64dc373646685f4c2c250a76","observation_id":"fb1dfa66-6d12-456a-8cb1-281e3a527ff2","resolution":{"observed_at":"2026-08-06T17:55:15.275005Z","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":"2026-08-06T17:55:15.249743Z","title":"Remarks on some nonparametric estimates of a density function","venue":null,"work_id":"c171cd05-e855-4989-b23d-1e2fa89c0d9f","year":1956},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.291118Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:32c45feef8a569c59e16d35794a2ab2cb1993318e980eb53c4a1fc216ecf94c2","observation_id":"4012d018-999b-4872-ad4e-fe2417d4f999","resolution":{"observed_at":"2026-08-06T17:55:15.256611Z","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":"2102.00534","last_updated":"2021-01-31T20:57:33Z","snapshot_observed_at":"2026-08-16T18:48:43.811592Z","submitted_at":"2021-01-31T20:57:33Z","title":"Generative and Discriminative Deep Belief Network Classifiers: Comparisons Under an Approximate Computing Framework","version":1},"cited_work":{"arxiv_id":"2102.00534","doi":null,"metadata_source":"pith","pith_arxiv_id":"2102.00534","snapshot_observed_at":"2026-08-06T17:55:14.701479Z","title":"Generative and Discriminative Deep Belief Network Classifiers: Comparisons Under an Approximate Computing Framework","venue":"cs.LG","work_id":"974d5118-8940-492a-ade2-adf5ff248d47","year":2021},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.296245Z"},"links":{"cited_paper":"/paper/2102.00534","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:4d4b1c0b6bbb816af99c7c6931ca04a8fc55e8218b2243400dc14941670e25fe","observation_id":"aab68413-fef3-4c73-96f5-56883b051a8a","resolution":{"observed_at":"2026-08-06T17:55:14.709095Z","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":"2026-08-06T17:55:15.229456Z","title":"Table for estimating the goodness of fit of empirical distributions","venue":null,"work_id":"d14cf293-d5f0-4f6a-a4fe-5f44d21bf456","year":1948},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.301934Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:d9ab385555002c55a87d4503aaaca6d60ccce3752cb74ec3a8fb7bc1725c9c94","observation_id":"26ff210c-0eb0-4f2e-83dc-f86f1e631e8e","resolution":{"observed_at":"2026-08-06T17:55:15.235433Z","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":"2026-08-06T17:55:15.208797Z","title":"How to fine-tune bert for text classification?, in: China national conference on Chinese computational linguistics, Springer","venue":null,"work_id":"b890d81d-93f6-4f4f-ac5b-6f055b41fc68","year":2019},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.308078Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:1da36aae211cf007acd3d89d7a9b37b8a29df15c4b9b2bf9b22063cf3d991607","observation_id":"e9a0806a-a046-4045-80f8-abb9167ac1d3","resolution":{"observed_at":"2026-08-06T17:55:15.214341Z","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":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-06T17:55:14.312908Z","title":"Llama: Open and efficient foundation language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.312908Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:e059b332deeeb09e826d2c8e34cd90dd0eb741bd0036a8c4cd090b3c824e11cf","observation_id":"56c56114-15a7-42f8-b5e8-a952b638f3fa","resolution":{"observed_at":"2026-08-06T17:55:14.312908Z","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":"2026-08-06T17:55:14.319823Z","title":"Attention is all you need","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.319823Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:d0c8c2213c24b6d0cc4d85d9282109775b3dddc2c4c53eee39c5bb9ce730b683","observation_id":"d89a0034-70f8-4d1f-8497-00f5f2f960a0","resolution":{"observed_at":"2026-08-06T17:55:14.319823Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.08886","last_updated":"2019-04-06T20:35:54Z","snapshot_observed_at":"2026-08-14T17:55:27.979118Z","submitted_at":"2018-11-21T18:58:14Z","title":"HAQ: Hardware-Aware Automated Quantization with Mixed Precision","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.08886","snapshot_observed_at":"2026-08-06T17:55:14.324663Z","title":"Haq: Hardware-aware automated quantization with mixed precision","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.324663Z"},"links":{"cited_paper":"/paper/1811.08886","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:6b0bae549367692510fb66a1a4439f9776538974708077d0e7d11994c0201e1b","observation_id":"6d50a9ab-b5c7-4eec-9397-f522db0099f2","resolution":{"observed_at":"2026-08-06T17:55:14.324663Z","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":"10.1162/neco_a_01370","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:55:14.419308Z","title":"The effect of class imbalance on precision-recall curves","venue":null,"work_id":"a220cce3-d7c4-4728-ab28-cce1afde43e9","year":2021},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.331012Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:38f5d0d1c0a5a58f1816c53ed4411ea05e517c98f9970d8be8cf0dc613325c80","observation_id":"d988fac6-9c14-4c82-a9b0-116d49c1975b","resolution":{"observed_at":"2026-08-06T17:55:14.428592Z","resolver_source":"doi","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":"2026-08-06T17:55:15.180169Z","title":"Easyquant: Post-training quantization via scale optimization, in: CVPR","venue":null,"work_id":"781361b0-67e2-4b5f-8238-478cc6678def","year":2022},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.336014Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:51756396026ebef3f164520d781cdf98b3444836b10dbbbf346abda4036542ef","observation_id":"4c971d1f-ba83-4554-989a-9741f7b3e1e0","resolution":{"observed_at":"2026-08-06T17:55:15.185672Z","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":"2026-08-06T17:55:15.160987Z","title":"Zeroquant: Efficient and affordable post-training quantization for large-scale transformers","venue":null,"work_id":"666af3da-f327-4095-8972-e8818c8c75a1","year":2022},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.341072Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:9e2aeaf69b71774c8f5671c48af089236a72d2e1d56e172eb0f020f28bdd1a3f","observation_id":"7d7a5f1f-1ea8-4185-b279-34961ffdd2df","resolution":{"observed_at":"2026-08-06T17:55:15.168392Z","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":"1703.01898","last_updated":"2017-05-26T01:27:23Z","snapshot_observed_at":"2026-08-14T21:13:20.015425Z","submitted_at":"2017-03-06T14:40:09Z","title":"Generative and Discriminative Text Classification with Recurrent Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1703.01898","snapshot_observed_at":"2026-08-06T17:55:14.347124Z","title":"Generative and discriminative text classification with recurrent neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.347124Z"},"links":{"cited_paper":"/paper/1703.01898","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:4c01f7c29c89a0390aa0010bc208589b641215915490cb25d3bc8c2aa1fa9966","observation_id":"fc384a65-2736-4c6c-bff6-f9b673ffe72b","resolution":{"observed_at":"2026-08-06T17:55:14.347124Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.06188","last_updated":"2019-10-17T17:15:24Z","snapshot_observed_at":"2026-07-06T08:29:22.074079Z","submitted_at":"2019-10-14T14:55:19Z","title":"Q8BERT: Quantized 8Bit BERT","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.06188","snapshot_observed_at":"2026-08-06T17:55:14.353037Z","title":"Q8bert: Quantized 8bit bert","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.353037Z"},"links":{"cited_paper":"/paper/1910.06188","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:b1e1d5f718d38aa45af0355defc57cf86b47faadb579628b89c2c02174ba956f","observation_id":"c7421fcd-d3c3-4af6-98a6-b56cf838a902","resolution":{"observed_at":"2026-08-06T17:55:14.353037Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.01068","last_updated":"2022-06-21T17:04:40Z","snapshot_observed_at":"2026-08-06T03:13:37.403059Z","submitted_at":"2022-05-02T17:49:50Z","title":"OPT: Open Pre-trained Transformer Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.01068","snapshot_observed_at":"2026-08-06T17:55:14.358367Z","title":"Opt: Open pre-trained transformer language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.358367Z"},"links":{"cited_paper":"/paper/2205.01068","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:5ce5c2ec9a2ed1b0553707ee0927c5133e8a374abd3096dfe2484f4e55583bc6","observation_id":"4f43806d-ef24-4357-8f44-d640d3134318","resolution":{"observed_at":"2026-08-06T17:55:14.358367Z","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":"2026-08-06T17:55:14.363617Z","title":"Qronos: Correcting the past by shaping the future","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.363617Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:2a94ee8c3b407de81046ef2999500ae256bad235d1786d5cc8f13569e2b05492","observation_id":"a4b6d98c-86d9-4452-920a-f67f158dc280","resolution":{"observed_at":"2026-08-06T17:55:14.363617Z","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":"2026-08-06T17:55:15.141449Z","title":"Learning low-precision structured subnetworks using joint layerwise channel pruning and uniform quantization","venue":null,"work_id":"46877fe8-bd0a-4c13-a2ab-36e11de915de","year":null},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.368261Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:9d0308d5dd870fbee1a88d0b2581a4507a29304a3fdda1266684f3aadb01a031","observation_id":"16c77153-0086-4c44-b6f1-e7511591b5d9","resolution":{"observed_at":"2026-08-06T17:55:15.147570Z","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":"1509.01626","last_updated":"2016-04-04T02:34:30Z","snapshot_observed_at":"2026-08-17T07:53:50.742745Z","submitted_at":"2015-09-04T22:31:53Z","title":"Character-level Convolutional Networks for Text Classification","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1509.01626","snapshot_observed_at":"2026-08-06T17:55:14.373213Z","title":"Character-level convolutional networks for text classification, in: Advances in Neural Information Processing Systems","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.373213Z"},"links":{"cited_paper":"/paper/1509.01626","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:f1d7db9f54c377e568145eb0a8e6f10d5f0faa3c5073ce989cfbbed59131c7b7","observation_id":"c365e039-dd17-4a76-91d0-be22d1e99236","resolution":{"observed_at":"2026-08-06T17:55:14.373213Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1510.03820","last_updated":"2016-04-06T23:20:27Z","snapshot_observed_at":"2026-08-14T22:28:02.277468Z","submitted_at":"2015-10-13T19:00:57Z","title":"A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1510.03820","snapshot_observed_at":"2026-08-06T17:55:14.379302Z","title":"A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.379302Z"},"links":{"cited_paper":"/paper/1510.03820","citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:bd93c0ca7b3b0cde06753720baa17918463db58bce3ea81af85fda4c2ffe3c7a","observation_id":"540f0ca6-d3bf-4e4e-b506-3273cf4992de","resolution":{"observed_at":"2026-08-06T17:55:14.379302Z","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":"2026-08-06T17:55:15.121114Z","title":"Selectq: Calibration data selection for post-training quantization","venue":null,"work_id":"e68858f1-f77b-49dc-91fc-61ca4371f4dd","year":null},"citing_paper":{"arxiv_id":"2507.09687","last_updated":"2025-07-13T15:48:16Z","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T17:55:14.383990Z"},"links":{"citing_paper":"/paper/2507.09687"},"observation_digest":"sha256:c2778eca97626c58e7d8209159603cf05408d7ae802f2ba002b61daf1dbcb40c","observation_id":"79a82d83-15b4-4926-beb7-16ffa80e55a5","resolution":{"observed_at":"2026-08-06T17:55:15.126581Z","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":"2507.09687","last_updated":"2025-07-13T15:48:16Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T17:19:44.963723Z","submitted_at":"2025-07-13T15:48:16Z","title":"Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness"},"reference_resolution":{"displayed":51,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":8,"verified_fuzzy":25},"total_outbound_references":51},"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 18 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2507.09687."}