{"as_of":"2026-08-21T22:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3be62015d337d4b038a74b4ab6b044d5321a135eaf5bcb46bda885bf9f8bb1f4","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T23:25:30.152432Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-12T08:51:24.367342Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1905.11452","last_updated":"2020-05-22T17:02:41Z","snapshot_observed_at":"2026-08-15T23:55:48.282700Z","submitted_at":"2019-05-27T19:03:40Z","title":"Mixed Precision DNNs: All you need is a good parametrization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.11452","snapshot_observed_at":"2026-08-12T19:13:10.513888Z","title":"Differentiable quantization of deep neural networks","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2411.10948","last_updated":"2024-11-17T03:32:36Z","snapshot_observed_at":"2026-08-19T04:32:05.478286Z","submitted_at":"2024-11-17T03:32:36Z","title":"Towards Accurate and Efficient Sub-8-Bit Integer Training","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T19:13:10.513888Z"},"links":{"cited_paper":"/paper/1905.11452","citing_paper":"/paper/2411.10948"},"observation_digest":"sha256:848ed3d5e7b191a46bb15b42ae6c976e918cce08aa1b995542c97aafa6b1d92d","observation_id":"187412a1-228c-488c-a793-53b23c93f8d3","resolution":{"observed_at":"2026-08-12T19:13:10.513888Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11452","last_updated":"2020-05-22T17:02:41Z","snapshot_observed_at":"2026-08-15T23:55:48.282700Z","submitted_at":"2019-05-27T19:03:40Z","title":"Mixed Precision DNNs: All you need is a good parametrization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.11452","snapshot_observed_at":"2026-08-10T21:23:49.838976Z","title":"Mixed Precision DNNs: All you need is a good parametrization,","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2501.05097","last_updated":"2025-01-09T09:25:22Z","snapshot_observed_at":"2026-08-19T10:10:34.155612Z","submitted_at":"2025-01-09T09:25:22Z","title":"A 1Mb mixed-precision quantized encoder for image classification and patch-based compression","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T21:23:49.838976Z"},"links":{"cited_paper":"/paper/1905.11452","citing_paper":"/paper/2501.05097"},"observation_digest":"sha256:56dab66393e0b7717b0a758c7190b5110a83094c93b41769bb7828962e4aba77","observation_id":"59935be3-58d0-4937-a913-35dbd41dccee","resolution":{"observed_at":"2026-08-10T21:23:49.838976Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11452","last_updated":"2020-05-22T17:02:41Z","snapshot_observed_at":"2026-08-15T23:55:48.282700Z","submitted_at":"2019-05-27T19:03:40Z","title":"Mixed Precision DNNs: All you need is a good parametrization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.11452","snapshot_observed_at":"2026-08-15T23:25:30.152432Z","title":"A., Tiedemann, S., Kemp, T., and Nakamura, A","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2505.04877","last_updated":"2025-05-08T01:20:24Z","snapshot_observed_at":"2026-08-18T09:18:47.334330Z","submitted_at":"2025-05-08T01:20:24Z","title":"Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T23:25:30.152432Z"},"links":{"cited_paper":"/paper/1905.11452","citing_paper":"/paper/2505.04877"},"observation_digest":"sha256:e33d98c66d9a472b66455f365b7fa5512e8b041ad0d08d07b05c6f2936cbd787","observation_id":"9ad7c615-c3c3-463e-bcc5-0877934bf831","resolution":{"observed_at":"2026-08-15T23:25:30.152432Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11452","last_updated":"2020-05-22T17:02:41Z","snapshot_observed_at":"2026-08-15T23:55:48.282700Z","submitted_at":"2019-05-27T19:03:40Z","title":"Mixed Precision DNNs: All you need is a good parametrization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.11452","snapshot_observed_at":"2026-08-05T21:16:26.312095Z","title":"Mixed precision dnns: All you need is a good parametrization","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2508.09262","last_updated":"2025-08-12T18:05:33Z","snapshot_observed_at":"2026-08-18T09:08:48.055902Z","submitted_at":"2025-08-12T18:05:33Z","title":"Harnessing Input-Adaptive Inference for Efficient VLN","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-05T21:16:26.312095Z"},"links":{"cited_paper":"/paper/1905.11452","citing_paper":"/paper/2508.09262"},"observation_digest":"sha256:ad43cee39307b8baa7695a57c7be56f11bdb4dd047e1f1e32188856239300d40","observation_id":"d9856914-0fc4-41e5-9e00-f83f46834d5d","resolution":{"observed_at":"2026-08-05T21:16:26.312095Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11452","last_updated":"2020-05-22T17:02:41Z","snapshot_observed_at":"2026-08-15T23:55:48.282700Z","submitted_at":"2019-05-27T19:03:40Z","title":"Mixed Precision DNNs: All you need is a good parametrization","version":3},"cited_work":{"arxiv_id":"1905.11452","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1905.11452","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, and Song Han","venue":null,"work_id":"fc395164-0234-4cd0-a4fb-b02f24093bf1","year":1905},"citing_paper":{"arxiv_id":"2604.26378","last_updated":"2026-04-29T07:41:31Z","snapshot_observed_at":"2026-08-13T02:03:31.266492Z","submitted_at":"2026-04-29T07:41:31Z","title":"CoQuant: Joint Weight-Activation Subspace Projection for Mixed-Precision LLMs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-07T13:39:31.422613Z"},"links":{"cited_paper":"/paper/1905.11452","citing_paper":"/paper/2604.26378"},"observation_digest":"sha256:381c42b368188895102afedb506145453eea0a5a7398d9fc67a01c2fd24300fd","observation_id":"f9270761-28f7-4899-8882-b49d59a80082","resolution":{"observed_at":"2026-05-12T08:51:24.369595Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11452","last_updated":"2020-05-22T17:02:41Z","snapshot_observed_at":"2026-08-15T23:55:48.282700Z","submitted_at":"2019-05-27T19:03:40Z","title":"Mixed Precision DNNs: All you need is a good parametrization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.11452","snapshot_observed_at":"2026-08-08T12:00:53.853311Z","title":"Mixed precision dnns: All you need is a good parametrization.arXiv preprint arXiv:1905.11452, 2019","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2608.05499","last_updated":"2026-08-06T01:09:50Z","snapshot_observed_at":"2026-08-20T16:31:26.703939Z","submitted_at":"2026-08-06T01:09:50Z","title":"APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-08T12:00:53.853311Z"},"links":{"cited_paper":"/paper/1905.11452","citing_paper":"/paper/2608.05499"},"observation_digest":"sha256:29c8ba831ac26494deb0c3208d550cd2498129940f6c0af5289e1929ead89f22","observation_id":"3817aef2-eb59-4834-bc59-b033c4cbb895","resolution":{"observed_at":"2026-08-08T12:00:53.853311Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.11452","last_updated":"2020-05-22T17:02:41Z","snapshot_observed_at":"2026-08-15T23:55:48.282700Z","submitted_at":"2019-05-27T19:03:40Z","title":"Mixed Precision DNNs: All you need is a good parametrization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.11452","snapshot_observed_at":"2026-08-12T19:02:03.952100Z","title":"arXiv preprint arXiv:1905.11452 , volume=","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2608.10709","last_updated":"2026-08-11T09:29:30Z","snapshot_observed_at":"2026-08-19T22:32:56.373060Z","submitted_at":"2026-08-11T09:29:30Z","title":"SQuaT: Self-Supervised Knowledge Distillation via Student-Aware Quantized Teacher Features","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-12T19:02:03.952100Z"},"links":{"cited_paper":"/paper/1905.11452","citing_paper":"/paper/2608.10709"},"observation_digest":"sha256:b491966c8fc8ae888fa45ae1986bd6720fd876a15a67677631928da71bf57823","observation_id":"edb3abc0-cfcd-4a32-84af-41b9b5e7bfe1","resolution":{"observed_at":"2026-08-12T19:02:03.952100Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1905.11452/citation-record","integrity":"/paper/1905.11452/integrity","json":"/paper/1905.11452/citation-record.json","paper":"/paper/1905.11452"},"outbound":[],"paper":{"arxiv_id":"1905.11452","last_updated":"2020-05-22T17:02:41Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T23:55:48.282700Z","submitted_at":"2019-05-27T19:03:40Z","title":"Mixed Precision DNNs: All you need is a good parametrization"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1905.11452."}