{"as_of":"2026-08-21T02:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:462bd3a7285fc4e7328aa53de0eccb950a0db422a4435a0b1c0cd6ec13bc0a1e","coverage":[{"denominator":52,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":52,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T10:19:04.356659Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2509.04244/citation-record","integrity":"/paper/2509.04244/integrity","json":"/paper/2509.04244/citation-record.json","paper":"/paper/2509.04244"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:19:05.252219Z","title":null,"venue":null,"work_id":"ce291c5e-a23a-4d4b-b719-b972b9b7fac5","year":2025},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.092369Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:28746803feb4d5fae5d8da3d87249fcde59c356bb7f3eb48f2a35ed6c4bcca1f","observation_id":"cdfa23fb-9d4a-450a-8271-78a26a176b30","resolution":{"observed_at":"2026-08-05T10:19:05.257355Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:05.235319Z","title":"Adam: Adaptive ap proximate multiplier for fault tolerance in dnn accelerators,","venue":null,"work_id":"bb36dab8-161b-4932-97e4-806d2208fa68","year":2025},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.098017Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:7188cc72bf0f106352015bc59700fc817e4a4aa4c4f9c9984febd5cf52829b79","observation_id":"466cc3e8-aa21-4a65-ad43-10dfe37b2d92","resolution":{"observed_at":"2026-08-05T10:19:05.240654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:05.218991Z","title":"T ransaxx: Efﬁcient transformers with approximate computing,","venue":null,"work_id":"278a23c2-adfb-461d-ad64-513ed9c2acf7","year":2025},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.103528Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:57330cd43c9ff37b81ccaace33e9bff597f8e378ab0376a0deea77211ad4f6b7","observation_id":"712e158c-b356-465b-9685-5808b54e9a8a","resolution":{"observed_at":"2026-08-05T10:19:05.224717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.13144","last_updated":"2020-02-02T14:15:07Z","snapshot_observed_at":"2026-08-19T02:03:55.357515Z","submitted_at":"2019-09-28T20:14:11Z","title":"Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.13144","snapshot_observed_at":"2026-08-05T10:19:04.109703Z","title":"Additive powers-of-two quan tization: An efﬁcient non-uniform discretization for neural networks,","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.109703Z"},"links":{"cited_paper":"/paper/1909.13144","citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:60de3c6a45059ff6d0058153d3f1b7fe8367e82cdbbbb544ff11f39ecd292795","observation_id":"6befa921-d68b-48f0-9897-5ed1112ed01a","resolution":{"observed_at":"2026-08-05T10:19:04.109703Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.05600","last_updated":"2023-08-10T14:19:58Z","snapshot_observed_at":"2026-08-19T13:01:36.669108Z","submitted_at":"2023-08-10T14:19:58Z","title":"NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search","version":1},"cited_work":{"arxiv_id":"2308.05600","doi":null,"metadata_source":"pith","pith_arxiv_id":"2308.05600","snapshot_observed_at":"2026-08-05T10:19:04.629069Z","title":"NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search","venue":"cs.LG","work_id":"77c20828-3762-4189-9e22-29cc86f52161","year":2023},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.114878Z"},"links":{"cited_paper":"/paper/2308.05600","citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:085f07b73247ccc64723fe322b98e5e76be814584edfbb109df75eabd3ceeed8","observation_id":"bcf8e7db-ca96-4ccc-8834-c8f1b3a8d333","resolution":{"observed_at":"2026-08-05T10:19:04.634009Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13365","last_updated":"2024-12-14T09:43:24Z","snapshot_observed_at":"2026-08-16T13:50:44.853887Z","submitted_at":"2024-05-22T05:48:25Z","title":"Communication-Efficient Federated Learning via Clipped Uniform Quantization","version":2},"cited_work":{"arxiv_id":"2405.13365","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.13365","snapshot_observed_at":"2026-08-05T10:19:04.605989Z","title":"Communication-Efficient Federated Learning via Clipped Uniform Quantization","venue":"cs.LG","work_id":"4ce87abd-6948-41db-8964-c811a48afe66","year":2024},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.120016Z"},"links":{"cited_paper":"/paper/2405.13365","citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:bdea48dc8aea2025c104337890c36aed796f7f17535df1392662fc733fe52397","observation_id":"6c43922e-5881-4975-81d6-c07af8b81995","resolution":{"observed_at":"2026-08-05T10:19:04.611049Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:05.203185Z","title":"Quantizat ion without tears,","venue":null,"work_id":"da44d60b-a00d-4c94-9883-23b08319998f","year":2025},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.125250Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:1a81713e6ee6da825a45b80645bb7feae0ae3b5398d5a2959c7fe394c5839c9f","observation_id":"b2377270-f541-4645-b5fc-0ea75f128ae2","resolution":{"observed_at":"2026-08-05T10:19:05.208106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.02340","last_updated":"2019-02-23T07:45:29Z","snapshot_observed_at":"2026-08-16T06:51:50.505762Z","submitted_at":"2018-10-04T17:39:58Z","title":"SNIP: Single-shot Network Pruning based on Connection Sensitivity","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.02340","snapshot_observed_at":"2026-08-05T10:19:04.130623Z","title":"Snip: Single-shot ne twork pruning based on connection sensitivity,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.130623Z"},"links":{"cited_paper":"/paper/1810.02340","citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:26892c9e662900764aaab283d34c59d12ec2ee17d9cbad1302b2502275e6d79e","observation_id":"44e1d29a-65a3-4d78-bc20-7dcdbd5df26d","resolution":{"observed_at":"2026-08-05T10:19:04.130623Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.02319","last_updated":"2023-04-05T09:19:19Z","snapshot_observed_at":"2026-08-19T03:25:39.292102Z","submitted_at":"2023-04-05T09:19:19Z","title":"Efficient CNNs via Passive Filter Pruning","version":1},"cited_work":{"arxiv_id":"2304.02319","doi":null,"metadata_source":"pith","pith_arxiv_id":"2304.02319","snapshot_observed_at":"2026-08-05T10:19:04.566240Z","title":"Efficient CNNs via Passive Filter Pruning","venue":"cs.LG","work_id":"8b34c09f-cb1c-4e75-b8ae-8fb52f5cf929","year":2023},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.135501Z"},"links":{"cited_paper":"/paper/2304.02319","citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:58cab56c9dcc067ca1a9bf38c22adc97147b069dee4c2be156c60d5454f40ccd","observation_id":"3d0efa0a-7b79-4678-a080-e95eb2e3e27a","resolution":{"observed_at":"2026-08-05T10:19:04.571306Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:05.187211Z","title":"Consecutive layer collaborati ve ﬁlter similarity for differentiable neural network pruning,","venue":null,"work_id":"3a68a773-6d5f-4fa6-b939-7e5ba9b62fba","year":2023},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.140496Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:e93f7b4868d7d305bd02d8f892fb5e0e1c875d62796f895165acba0cdbc466bc","observation_id":"f9ec4482-522b-4fb9-9225-60b63769b52d","resolution":{"observed_at":"2026-08-05T10:19:05.192155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:05.171453Z","title":"Pruning convolution neural n etworks using ﬁlter clustering based on normalized cross-correlat ion similarity,","venue":null,"work_id":"22fc22fc-8453-4b63-a0de-63508547e882","year":2025},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.145640Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:4798c5da4818809f739851163584a112d76ace3c6cbc92ab4604fec7fe1b353e","observation_id":"09fdead4-19fe-4da4-ad88-96d208ddd846","resolution":{"observed_at":"2026-08-05T10:19:05.176164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:05.155689Z","title":"Losparse: Structured compression of large language model s based on low-rank and sparse approximation,","venue":null,"work_id":"4a6014da-d49f-48c3-87dc-492a2800d9b1","year":2023},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.150383Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:face7525ea893873bb2ca4a255984c0d75acbff597d5ad7fecf79c942bb1b7f4","observation_id":"9e7be6f4-fc02-45c2-8192-b945e9ad2d3b","resolution":{"observed_at":"2026-08-05T10:19:05.161362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.20078","last_updated":"2025-05-11T04:52:45Z","snapshot_observed_at":"2026-08-19T10:13:23.422786Z","submitted_at":"2025-04-25T06:04:01Z","title":"Low-Rank Matrix Approximation for Neural Network Compression","version":2},"cited_work":{"arxiv_id":"2504.20078","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.20078","snapshot_observed_at":"2026-08-05T10:19:04.543991Z","title":"Low-Rank Matrix Approximation for Neural Network Compression","venue":"cs.LG","work_id":"738535f4-85e3-4399-badc-416540e99fa6","year":2025},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.155109Z"},"links":{"cited_paper":"/paper/2504.20078","citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:b41800c14d1d3f897876760ae37b757111454082ae35426c335b53c47b32a77f","observation_id":"694650fb-6663-4b3c-8e83-712beda24b57","resolution":{"observed_at":"2026-08-05T10:19:04.549131Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:05.139602Z","title":"Uncertai nty-based knowledge distillation for bayesian deep neural network co mpression,","venue":null,"work_id":"2f2adede-a969-4e1f-b34e-a988f26ff851","year":2024},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.161019Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:a84faa14277e14f08ad7e6a3c8b33fb6d93d864823bf8c786e9bbcd765672c0c","observation_id":"16a5ce26-c017-4d1c-bd94-eb32707a0242","resolution":{"observed_at":"2026-08-05T10:19:05.144775Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:05.124926Z","title":"Counterclockwise block-by-block knowledge distillatio n for neural network compression,","venue":null,"work_id":"44d576e4-7097-497e-812f-8273916a6fd2","year":2025},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.166594Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:0f4101233c70d66468bace6c6cc17756c4613f0b58055ef21d765a8e5cd08898","observation_id":"45cbe6ca-0309-47d1-aebd-73226d1901b4","resolution":{"observed_at":"2026-08-05T10:19:05.129612Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:05.109769Z","title":"A compre hensive survey on model quantization for deep neural networks in ima ge classiﬁcation,","venue":null,"work_id":"773f8924-6667-4114-a730-d6833ebbefea","year":2023},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.171364Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:5bf9fd88b3203b1ff4d39eab9ea050a3fd9d9b62303e1ce89ae01616808bcecc","observation_id":"8ea23d92-38d7-467b-8cf1-8b6469083e2c","resolution":{"observed_at":"2026-08-05T10:19:05.114619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:05.094343Z","title":"Hfpq: deep neural network com pression by hardware-friendly pruning-quantization,","venue":null,"work_id":"3a52e3a3-6548-48d8-b156-f45e08021f1e","year":2021},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.176392Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:c8d301770a592a11c99301e2c9c86e6d9b00fd149af53e5abe55265d84336066","observation_id":"8b22f11d-cb0a-4b0a-95cb-c20be3c1bf7d","resolution":{"observed_at":"2026-08-05T10:19:05.099367Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:05.078593Z","title":"Hardware-aware dnn compression via diverse prun- ing and mixed-precision quantization,","venue":null,"work_id":"ab1f64ea-d53c-4fca-94d8-add8b1ceb931","year":2024},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.181180Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:29a8166d55e85ff09a14538652d2a9972018535d014c77d1610ae220abe52506","observation_id":"a5f7fbb0-156b-418d-884b-341d5d8cda14","resolution":{"observed_at":"2026-08-05T10:19:05.084011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:05.062398Z","title":"Optimized convolutional ne ural network at the iot edge for image detection using pruning and quantiz ation,","venue":null,"work_id":"1a41f66e-6ee5-4483-a721-e1da0cfb7bad","year":2025},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.186121Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:b062df216d7ef4b5b0555c546566647915182a83f7d55a712876be3f9e52fd73","observation_id":"52ed80f7-e1e2-4e60-897f-c52d4082bbe3","resolution":{"observed_at":"2026-08-05T10:19:05.067307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:05.046890Z","title":"Filter pruning via geometric median for deep convolutional neural networks ac celeration,","venue":null,"work_id":"c44f3213-5c16-42f6-9920-69dc778efe0a","year":2019},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.191074Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:e2f9fa1f8932172c5255c71a30694cc18212d2a78167be753135300fa0466d4c","observation_id":"6816ecd2-a475-4fff-a265-894469a3fd7d","resolution":{"observed_at":"2026-08-05T10:19:05.051779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:05.031059Z","title":"Differentiable joi nt pruning and quantization for hardware efﬁciency,","venue":null,"work_id":"2009b790-3e3b-4074-af12-8d724c294dae","year":2020},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.195969Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:d972acfcf1f664c77233bf7975bd330d3b6c152caf319e37be0d3cc71e66cabe","observation_id":"3f9650cf-99fe-4dca-95b2-f8e5ae633d43","resolution":{"observed_at":"2026-08-05T10:19:05.036533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:05.014440Z","title":"Learning both wei ghts and con- nections for efﬁcient neural network,","venue":null,"work_id":"50eeca09-f012-45f1-916c-7b2d305d9273","year":2015},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.201017Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:ab075ec0156a46ee976507e1e19f637b5973757f4e3fca20911e832f70252005","observation_id":"b0d3d3a0-c500-4f28-a53c-9ea323c9409b","resolution":{"observed_at":"2026-08-05T10:19:05.019484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:04.998614Z","title":"Sparse optimizatio n guided pruning for neural networks,","venue":null,"work_id":"9ba01dab-7cbe-491d-a36d-8c8bb5b3ae35","year":2024},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.206051Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:783e786a6a0f87878f3dcf19b8f522b04df5b37b15d6182d2ba205cdf8b2d958","observation_id":"9e31f64e-3b3b-40ed-b546-65aa1d67d597","resolution":{"observed_at":"2026-08-05T10:19:05.003337Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:04.982539Z","title":"Thinet: A ﬁlter level pruni ng method for deep neural network compression,","venue":null,"work_id":"5445fbe7-58f5-49c0-8ff9-e058fa3a5b8e","year":2017},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.210985Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:8023e2c9e0d93df23ed0937001269c9ca8dd6a6d428e2bf375632c74fafc7efa","observation_id":"810c681f-680f-498a-abd3-b14881e941a9","resolution":{"observed_at":"2026-08-05T10:19:04.987606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1808.06866","last_updated":"2018-08-21T12:22:38Z","snapshot_observed_at":"2026-08-14T18:38:54.194750Z","submitted_at":"2018-08-21T12:22:38Z","title":"Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks","version":1},"cited_work":{"arxiv_id":"1808.06866","doi":null,"metadata_source":"pith","pith_arxiv_id":"1808.06866","snapshot_observed_at":"2026-08-05T10:19:04.519943Z","title":"Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks","venue":"cs.CV","work_id":"ff0a8ccd-0c36-45fe-ab2c-a1c169e618f4","year":2018},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.216527Z"},"links":{"cited_paper":"/paper/1808.06866","citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:e83b893f0cd123fa55a9d3d4608fd560f037daa5a3fb89f07cb7c947e0830bfb","observation_id":"f6f46192-6141-459c-bcdb-f3b851daddb7","resolution":{"observed_at":"2026-08-05T10:19:04.526135Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:04.966179Z","title":"A novel and efﬁcient model pruning method for deep convolutional ne ural networks by evaluating the direct and indirect effects of ﬁl ters,","venue":null,"work_id":"19ee8f32-6f00-47de-9cb6-e19f2a6ecf2c","year":2024},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.221893Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:2f69786faeeb5abc3e7849b687f7f2e4223eb4d82b335b3ac546fc88cc20fbd1","observation_id":"a76dded1-e77d-4a00-bc1c-0404c0628cb7","resolution":{"observed_at":"2026-08-05T10:19:04.972262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:04.950472Z","title":"Daar: Dual attention coope rative adaptive pruning rate by data-driven for ﬁlter pruning: S. lian et al","venue":null,"work_id":"19e9f57f-c080-48f0-bcbb-43dfc4aa4e9a","year":2025},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.227772Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:3db061e4932382baa0a09a4964ba6ae7a96227abc34d59a2036e46932d2ba2ed","observation_id":"743a3fdf-2e31-4019-b835-9193c6ec8568","resolution":{"observed_at":"2026-08-05T10:19:04.955499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:04.934263Z","title":"Eacp: An effec tive automatic channel pruning for neural networks,","venue":null,"work_id":"9076e89f-b90e-4cb3-bd9e-aeea7b1c1a6a","year":2023},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.232991Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:46b81dddcec13968778974570a58c70bb4484a8ae7affb88ff30c6daae921b81","observation_id":"cc18c9a3-23f5-4bf2-b143-5bef14bb54e4","resolution":{"observed_at":"2026-08-05T10:19:04.939455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:04.916356Z","title":"Pro gressive local ﬁlter pruning for image retrieval acceleration,","venue":null,"work_id":"03e7bc62-924c-4027-91ec-e450af87b2f4","year":2023},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.238079Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:3fe4c913ad0647fd447d07393030b8606fa0dd4e7f12b9c27cb1c9674f70c8ec","observation_id":"920ab912-b5ed-44eb-a977-9aef93ee5670","resolution":{"observed_at":"2026-08-05T10:19:04.922463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:04.898815Z","title":"Sfp: Similarity-based ﬁlter pruning for deep neural networks,","venue":null,"work_id":"1f99f9c4-bb6f-409f-8823-faf9907abaad","year":2025},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.242886Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:f84e6e2fcb6c1083be56451f3d8964c05c15dc438bf1e629e8a5f2dd682dd78a","observation_id":"1d7c632d-5834-4f84-ab27-47ed5c98b96f","resolution":{"observed_at":"2026-08-05T10:19:04.903900Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1603.01025","last_updated":"2016-03-17T03:32:30Z","snapshot_observed_at":"2026-08-19T16:36:22.833023Z","submitted_at":"2016-03-03T08:51:52Z","title":"Convolutional Neural Networks using Logarithmic Data Representation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1603.01025","snapshot_observed_at":"2026-08-05T10:19:04.247796Z","title":"Convo- lutional neural networks using logarithmic data represent a- tion,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.247796Z"},"links":{"cited_paper":"/paper/1603.01025","citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:8ad82267321485d174453e0d28eff76e6ab676c1c920f84829358b99b2d682f1","observation_id":"d888a9a5-c6be-4c54-b2f3-546caebf7976","resolution":{"observed_at":"2026-08-05T10:19:04.247796Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1702.03044","last_updated":"2017-08-25T13:21:18Z","snapshot_observed_at":"2026-08-20T07:34:09.180609Z","submitted_at":"2017-02-10T02:30:22Z","title":"Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1702.03044","snapshot_observed_at":"2026-08-05T10:19:04.252977Z","title":"Incremental n etwork quantization: Towards lossless cnns with low-precision we ights,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.252977Z"},"links":{"cited_paper":"/paper/1702.03044","citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:3ad9bf9e8f521719a5a034d2e0ad2ba3bb1a4b16cc6fbea4b2a7f5b42b247dcd","observation_id":"744ddcbb-6068-4acb-88da-bd1a6e815959","resolution":{"observed_at":"2026-08-05T10:19:04.252977Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.03254","last_updated":"2025-08-06T10:03:03Z","snapshot_observed_at":"2026-08-17T12:06:20.621575Z","submitted_at":"2025-05-06T07:32:24Z","title":"PROM: Prioritize Reduction of Multiplications Over Lower Bit-Widths for Efficient CNNs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.03254","snapshot_observed_at":"2026-08-05T10:19:04.260880Z","title":"Prom: Prio ritize reduc- tion of multiplications over lower bit-widths for efﬁcient cnns,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.260880Z"},"links":{"cited_paper":"/paper/2505.03254","citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:fa44991da2dd6913edc815f8772e89aeb078222196a041499700a90cad0eafe7","observation_id":"66f10ec3-392c-4034-9dad-1ed1150365b6","resolution":{"observed_at":"2026-08-05T10:19:04.260880Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08041","last_updated":"2024-04-06T10:22:57Z","snapshot_observed_at":"2026-08-16T14:52:50.059474Z","submitted_at":"2023-10-12T05:25:49Z","title":"QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.08041","snapshot_observed_at":"2026-08-05T10:19:04.266249Z","title":"Ql lm: Accurate and efﬁcient low-bitwidth quantization for large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.266249Z"},"links":{"cited_paper":"/paper/2310.08041","citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:bf959062bd7a312ccf6d81eafbb472be564eb3fae65fd637aacc3be3fe75e443","observation_id":"e42ecdb5-05ce-44e9-b1cf-79526c7ee07b","resolution":{"observed_at":"2026-08-05T10:19:04.266249Z","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":"2026-08-05T10:19:04.882025Z","title":"Block and subword-s caling ﬂoating-point (BSFP) : An efﬁcient non-uniform quantizati on for low precision inference,","venue":null,"work_id":"320be43a-fe6b-49ca-8656-6d8b3dffecf8","year":2023},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.271482Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:5bbd1948af1fbd07ad103f42d24125d4c8114e82e1b66ce50ee5253d33de773b","observation_id":"9bdd1306-cd85-44a9-85df-9278c2594f03","resolution":{"observed_at":"2026-08-05T10:19:04.887120Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.14681","last_updated":"2021-06-25T07:24:53Z","snapshot_observed_at":"2026-08-18T02:09:00.445107Z","submitted_at":"2021-06-25T07:24:53Z","title":"PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.14681","snapshot_observed_at":"2026-08-05T10:19:04.276568Z","title":"Pqk: model compression vi a pruning, quantization, and knowledge distillation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.276568Z"},"links":{"cited_paper":"/paper/2106.14681","citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:12d4fb7a7f5229e857723c113547a7f5c7711b6ea7d1db0f44a886d09698cdcd","observation_id":"73d23a4b-3fb3-4684-8614-bca3fcb8c9fe","resolution":{"observed_at":"2026-08-05T10:19:04.276568Z","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":"2026-08-05T10:19:04.865647Z","title":"Compressed neural ar- chitecture utilizing dimensionality reduction and quanti zation,","venue":null,"work_id":"2efb1bad-1e0e-45e0-804d-2ff5fd873cb1","year":2023},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.282346Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:3f56acbaa186a66dd80be00a96d9332a02cb40e687ca02be93c8896ff6318177","observation_id":"5f436c79-202f-4c38-918c-5074f6663d9a","resolution":{"observed_at":"2026-08-05T10:19:04.870652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:04.849041Z","title":"Quantization-aware trainin g with dynamic and static pruning,","venue":null,"work_id":"7f73d17b-d5af-4fc6-b6b9-8bde71e0d51a","year":2025},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.287086Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:2cf90c3a3a02a6dfebeafa4418685c49a8faaf29a9d0b3fb17178c16b6afbede","observation_id":"48d4ebd4-a930-4629-b199-aec48e4e3e2d","resolution":{"observed_at":"2026-08-05T10:19:04.854784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:04.832397Z","title":"Non-structured dnn weight pruning—is it beneﬁcial in any platform?","venue":null,"work_id":"62a1c9e3-30cd-4c15-941d-7db6799901b8","year":2021},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.292022Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:d20632e0f9cb34404a5c22ca5b04eeca57568ca22b6f9561561fa84264870a8d","observation_id":"ce6d90f8-1c46-4d98-bee5-850a443123d7","resolution":{"observed_at":"2026-08-05T10:19:04.837463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:04.297621Z","title":"Deep residual learni ng for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.297621Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:b58fe5aea502f78103c57e41502178cdc8d0a8cf55028ed26da7edba0ae38849","observation_id":"788494c9-ee61-4663-acd8-7697f3087660","resolution":{"observed_at":"2026-08-05T10:19:04.297621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-17T19:17:06.411141Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-05T10:19:04.302916Z","title":"V ery deep convolutional networks for large-scale image recognition,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.302916Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:a54e20c335904db777d4e3c913c2eba7f7cf863c8b681b28e7af8790ddac4b4c","observation_id":"cd483498-f590-40b8-ba4b-f8714fe2327d","resolution":{"observed_at":"2026-08-05T10:19:04.302916Z","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":"2026-08-05T10:19:04.308360Z","title":"Learning multiple layers of features from tiny images,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.308360Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:7bb733810e6b362aa2b7078b424560af5c7e48bb38f534f541273c77571d81eb","observation_id":"ab275cc1-82fb-4e40-a53c-1618c8b2f812","resolution":{"observed_at":"2026-08-05T10:19:04.308360Z","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":"2026-08-05T10:19:04.795724Z","title":"Blen ded coarse gradient descent for full quantization of deep neural netwo rks,","venue":null,"work_id":"82fb663a-85fc-413c-8fbd-51c3b4e0b110","year":2019},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.312836Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:0a52436897002131cbae8c81c78943fa4233e9988934447f0092b894bca7c200","observation_id":"f2648ac9-4d32-42e0-9cfa-f90af9d274cd","resolution":{"observed_at":"2026-08-05T10:19:04.801093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:04.779890Z","title":"Ro bustness- aware 2-bit quantization with real-time performance for ne ural network,","venue":null,"work_id":"3c6fe2fd-38e6-4244-a734-4e1f886232c7","year":2021},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.317873Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:5e50d51d83e7a638f37436613d02fb15048896c3b52288ed46c37977563adc28","observation_id":"e2b426f8-ecba-455c-9885-86d0bc28ef5d","resolution":{"observed_at":"2026-08-05T10:19:04.784938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:04.762996Z","title":"Se arching for low-bit weights in quantized neural networks,","venue":null,"work_id":"d093c957-b6a2-40f5-83ae-031e6024e307","year":2020},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.322306Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:b90ff57a309a772fc72af90483b61124f5bbc7486fd3bdaf0e467d61ca92bcf5","observation_id":"ff5bf210-c361-41f0-a1c6-df47a259b1b0","resolution":{"observed_at":"2026-08-05T10:19:04.768220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:04.745831Z","title":"Search w hat you want: Barrier panelty nas for mixed precision quantization ,","venue":null,"work_id":"4d16759b-e180-402b-9fb7-27575cf83347","year":2020},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.327832Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:832d04626c533711bb1fd2a1b9248d1b7f00974fa5ad52c65638f9a076309674","observation_id":"daec8255-8787-40f7-9ac0-718e53407332","resolution":{"observed_at":"2026-08-05T10:19:04.751192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:04.727773Z","title":"Dynamical channel pruning by conditional accuracy change for deep neural netw orks,","venue":null,"work_id":"45acdda3-83fc-499a-9690-3b42e5cde125","year":2020},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.332783Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:681e66b4888fcf31589f7e54b0647fe9f6bdedfa38eb9a7801eea6d84a213c2b","observation_id":"2a2ffe9c-3565-474b-be8f-b7d75ce04d2c","resolution":{"observed_at":"2026-08-05T10:19:04.733894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:04.711211Z","title":"Iterative clus tering pruning for convolutional neural networks,","venue":null,"work_id":"7e833223-78e3-4095-a6bc-3d1f4b2e7ac4","year":2023},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.337141Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:231c61e501105c07ac2a1d06a566262e06aa00c1f05b1b7f51411f85b6b22ceb","observation_id":"a2e778de-6a48-4dac-b630-f7e3e3bd1457","resolution":{"observed_at":"2026-08-05T10:19:04.716618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:04.694670Z","title":"Hessian-aware pruning and optimal neural impl ant,","venue":null,"work_id":"e69bd618-a3f8-4736-b5a1-4149072fb814","year":2022},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.341733Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:01ff0256bad2d3b2a90ea383a0aadc3e45b6b41660467249fd9318558cb173c0","observation_id":"eb9e6d3a-4333-4a46-92b5-adeb1781fcd5","resolution":{"observed_at":"2026-08-05T10:19:04.699741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04549","last_updated":"2024-06-06T23:19:57Z","snapshot_observed_at":"2026-08-16T13:45:20.019844Z","submitted_at":"2024-06-06T23:19:57Z","title":"Concurrent Training and Layer Pruning of Deep Neural Networks","version":1},"cited_work":{"arxiv_id":"2406.04549","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.04549","snapshot_observed_at":"2026-08-05T10:19:04.394412Z","title":"Concurrent Training and Layer Pruning of Deep Neural Networks","venue":"cs.LG","work_id":"f1c1ed9f-020e-4c8e-b8b9-5192f8f22165","year":2024},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.346795Z"},"links":{"cited_paper":"/paper/2406.04549","citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:7250cb8181500dab6b0e473d16c9a6065305dbf7509da4f269a4cf7f6e57ed9c","observation_id":"c51b1efe-0a1b-4d28-8497-f82c8bd70425","resolution":{"observed_at":"2026-08-05T10:19:04.401873Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:04.677194Z","title":"Shallowing deep networks: Layer-w ise pruning based on feature representations,","venue":null,"work_id":"e5dac23e-6799-41f2-a4ad-92f7f3e674ff","year":2018},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.351939Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:b03361e2e1d1bbffdfe9f94804976520c8afe65117ab013b6267e8f196585c70","observation_id":"8abb2e95-ffdd-4d99-8f66-f7446a40b51f","resolution":{"observed_at":"2026-08-05T10:19:04.682967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-05T10:19:04.661370Z","title":"Inference- aware convolutional neural network pruning,","venue":null,"work_id":"8acb7d3c-260a-4670-a09e-092cf58adb9b","year":2022},"citing_paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-05T10:19:04.356659Z"},"links":{"citing_paper":"/paper/2509.04244"},"observation_digest":"sha256:91b38c6c19c995294cf25ecfaf9b48c8ba015b8b78cdf3db8a2c968df6f819fd","observation_id":"d2d2744c-7663-476d-b8a1-4b56f15668df","resolution":{"observed_at":"2026-08-05T10:19:04.666698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.04244","last_updated":"2025-09-04T14:17:28Z","latest_version":1,"primary_category":"cs.NE","snapshot_observed_at":"2026-08-16T06:52:35.720273Z","submitted_at":"2025-09-04T14:17:28Z","title":"Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression"},"reference_resolution":{"displayed":52,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":6,"verified_fuzzy":35},"total_outbound_references":52},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2509.04244."}