{"as_of":"2026-08-05T13:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c22df900bea6f1abc1b63371682302eccf81fb4776d15264fc82cbd35b19db20","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-24T15:15:42.863578Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+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/1907.11840/citation-record","integrity":"/paper/1907.11840/integrity","json":"/paper/1907.11840/citation-record.json","paper":"/paper/1907.11840"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dynamic capacity networks","venue":null,"work_id":"932b9653-a2f3-4ab7-96a9-57fb2c736279","year":2016},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:01315b0c49cda127c18f60428fdd2718420fbc40ad6225168e2126382399aa30","observation_id":"9c8235f3-cf0f-4aaf-8d14-86a56c740697","resolution":{"observed_at":"2026-05-24T15:16:14.628718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Adaptive neural networks for efficient inference","venue":null,"work_id":"2d583635-89ce-4a6b-bf2b-9526c2ec110f","year":2017},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:f0c9861aa1924909603b1acab6bfc1b86ffd9f0cc93836988f4cfc58b9bf4643","observation_id":"93c4964b-5be1-4520-a246-65dbde470e09","resolution":{"observed_at":"2026-05-24T15:16:14.558303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.00363","last_updated":"2016-04-18T13:11:45Z","snapshot_observed_at":"2026-07-06T04:35:01.085808Z","submitted_at":"2015-11-02T02:50:05Z","title":"BinaryConnect: Training Deep Neural Networks with binary weights during propagations","version":3},"cited_work":{"arxiv_id":"1511.00363","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.00363","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"BinaryConnect: Training Deep Neural Networks with binary weights during propagations","venue":"cs.LG","work_id":"18bdc5c5-5c31-4064-a1d0-68014a389e90","year":2015},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"cited_paper":"/paper/1511.00363","citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:7b0059a40aff8790d533a25ece0f9295abf54666087beabfd236d6d4395b6fe1","observation_id":"f8ff096c-9014-417d-8162-38e9123e68fa","resolution":{"observed_at":"2026-05-24T15:16:14.179308Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":"e52c388b-6539-447c-93d9-2ef99ed0d7c1","year":2009},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:c133ed6b29a1d00f69b9774e37175e03655cda50c929f72cd4efb2f2ccdcceca","observation_id":"06202f35-671e-4083-9012-3a24d3afaae0","resolution":{"observed_at":"2026-05-24T15:16:14.560791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Exploiting linear structure within convolutional networks for efficient evaluation","venue":null,"work_id":"343243fd-e49d-45d1-b61d-725c68fa918f","year":2014},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:58ac3de422da7df0741d199837d8a7dfc280bf8354a5a7078769b36af85c9fcd","observation_id":"d544166f-a3ff-42c0-abb3-041c875b4a5b","resolution":{"observed_at":"2026-05-24T15:16:14.599420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"More is less: A more complicated network with less inference complexity","venue":null,"work_id":"ba17e82f-da4b-436a-af2d-bae6e9ae1177","year":2017},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:5b84576a84af577a79fa58ac4016c0e735b2943f4e78df7253d1ce6e3129aadb","observation_id":"1cd00b5b-c941-406a-814d-118e6d5b0c61","resolution":{"observed_at":"2026-05-24T15:16:14.566348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry Vetrov, and Ruslan Salakhutdinov","venue":null,"work_id":"2195a58b-9a38-4213-83d9-4b7f775c54ca","year":2017},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:42b30ac82173f0022dd13511f3aac0a524a1ca34916c1104a3347ed8ed9200a1","observation_id":"8dcc1a0b-3099-4aa6-b41d-9a2636cd155e","resolution":{"observed_at":"2026-05-24T15:16:14.593505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.05331","last_updated":"2019-01-28T05:25:48Z","snapshot_observed_at":"2026-07-06T07:07:37.439864Z","submitted_at":"2018-10-12T03:00:59Z","title":"Dynamic Channel Pruning: Feature Boosting and Suppression","version":2},"cited_work":{"arxiv_id":"1810.05331","doi":null,"metadata_source":"pith","pith_arxiv_id":"1810.05331","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dynamic Channel Pruning: Feature Boosting and Suppression","venue":"cs.CV","work_id":"fd244cbe-9caf-4e6b-af3b-3d4fb93b696a","year":2018},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"cited_paper":"/paper/1810.05331","citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:c26945478f9ae18c60041a8e85e4c129d67e644e568cb795ff04881457e66609","observation_id":"7f2a30de-a6fd-4d06-9a7a-4eb6d15008a9","resolution":{"observed_at":"2026-05-24T15:16:14.182938Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep compression: Compressing deep neural net- works with pruning, trained quantization and huffman cod- ing","venue":null,"work_id":"d2ea6dc0-2a47-440f-8935-f27c08bf8e34","year":2016},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:ff5bf8a8b2d4be40c3084da9880d75d01b4d53c55dabe11e6defe442af12b7f2","observation_id":"754aa005-d34f-40b1-b37e-0fc934425624","resolution":{"observed_at":"2026-05-24T15:16:14.610596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep residual learning for image recog- nition","venue":null,"work_id":"f7615cfd-a5b0-40c1-82b0-ed470b368755","year":2016},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:521ad6a7e8bb10827d12188b2f490be0db27189a73bbaed651606cded5f3e55a","observation_id":"505d8486-dbb3-4315-ad7e-50f2b0e4dbab","resolution":{"observed_at":"2026-05-24T15:16:14.617106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.12549","last_updated":"2019-10-28T23:53:50Z","snapshot_observed_at":"2026-08-01T22:06:34.676319Z","submitted_at":"2018-05-29T20:11:56Z","title":"Channel Gating Neural Networks","version":2},"cited_work":{"arxiv_id":"1805.12549","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1805.12549","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Edward Suh","venue":null,"work_id":"8bd69414-86ba-4fa8-9169-189ad93b6472","year":2018},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"cited_paper":"/paper/1805.12549","citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:01d6bc9ccad65a92cd2e11e33a79900fa3941ee876bb350abb736fa83754e89a","observation_id":"82b0dee0-a0e9-4ba3-9c30-d841cd8392de","resolution":{"observed_at":"2026-05-24T15:16:14.188135Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Accurate image super-resolution using very deep convolutional networks","venue":null,"work_id":"8d00e92f-00a3-46f2-9cfd-8e8b932b52b8","year":2016},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:f5be361c7bcd43d2a47ed2118f9de64d985fe0401f085fe2df577b72b8606ec0","observation_id":"faf3a786-4bed-4d97-90bf-bdc5961a5979","resolution":{"observed_at":"2026-05-24T15:16:14.595190Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Imagenet classification with deep convolutional neural networks","venue":null,"work_id":"c468df9c-8b8a-4445-8224-cee22adc88c1","year":2012},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:deee4d3b743146615e36c371afd666da030f6a853a3b02f845fdacd48886d42d","observation_id":"04b623ed-32b5-4ed7-b7b8-6d2ac7917767","resolution":{"observed_at":"2026-05-24T15:16:14.587875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning multiple lay- ers of features from tiny images","venue":null,"work_id":"33346044-cf9e-46df-8f73-70c88ab4afd7","year":2009},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:a1f7e835c190ef9fa51934536d659412740f755c3e3beecc5871f04a8a31776b","observation_id":"caca05c5-7a18-4def-9111-fed46aeceacb","resolution":{"observed_at":"2026-05-24T15:16:14.623436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Runtime neural pruning","venue":null,"work_id":"7915aad0-e7d2-49c4-8f95-46541db89687","year":2017},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:683bb32382de4a0ff351d0027e0f200f9b10aa511e71a2ae51089b545dcf5e84","observation_id":"e4ac94c9-5d5f-46d4-9918-d34b01fee047","resolution":{"observed_at":"2026-05-24T15:16:14.613746Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dynamic deep neural networks: Optimizing accuracy-efficiency trade-offs by selective execution","venue":null,"work_id":"bae12048-5fd3-40f8-bc0d-0f429eb4d15a","year":2018},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:50bab9cf2da1e4031df230aff0a85d27c6e39c2ee5b4915483f86f39783478c1","observation_id":"faefa2ac-4b25-4a97-8ccb-61f9b5baed00","resolution":{"observed_at":"2026-05-24T15:16:14.620210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning efficient convolutional networks through net- work slimming","venue":null,"work_id":"e0c36f94-9697-4cd9-8266-8789a62e85ad","year":2017},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:0537546b0f7bb4663c21791c2c3b6e04cbf3353aad3a94b93b17cb09a92e7116","observation_id":"999450e1-9512-4f67-9867-2b1a8292b37d","resolution":{"observed_at":"2026-05-24T15:16:14.634240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Thinet: A filter level pruning method for deep neural network compression","venue":null,"work_id":"42ff7177-2a24-4770-8489-8e50d240a954","year":2017},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:2aab44c146e140ebd1dd63c87e09a88ea0ff01af3444301cabc10664378e6bf4","observation_id":"09ef61c3-bf25-40eb-a4e9-85f38d9c0e4c","resolution":{"observed_at":"2026-05-24T15:16:14.604833Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deciding how to decide: Dynamic routing in artificial neural networks","venue":null,"work_id":"590b0a97-abd7-49f7-8887-954e8ac0b578","year":2017},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:8f61ad83882ac7fa4274e7d7558be321e07a546732582e72fa664d407622be12","observation_id":"ec8f881e-976e-4a7c-96c6-32cc28048a43","resolution":{"observed_at":"2026-05-24T15:16:14.568984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Xnor-net: Ima- genet classification using binary convolutional neural net- works","venue":null,"work_id":"726c7d87-5639-4d53-b2c0-867f5f6b1297","year":2016},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:8ab9d33bca638e322c407776d81fc230d0a6136e9f5f919e72073b3184915693","observation_id":"ebf599d4-5794-41ad-abf3-96bbb9298173","resolution":{"observed_at":"2026-05-24T15:16:14.606898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Faster r-cnn: Towards real-time object detection with region proposal networks","venue":null,"work_id":"d80e21ce-d984-4beb-b8ab-74b0e823fc58","year":2015},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:ff7c6e538b71bbaff10ed0f55960f4a437ac5e72932a392e6a9181895ab0584f","observation_id":"d55a089a-1dd4-4ad7-a52b-e81fc98572eb","resolution":{"observed_at":"2026-05-24T15:16:14.599977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Sbnet: Sparse blocks network for fast inference","venue":null,"work_id":"fbf883c0-24dc-43b2-8ce8-276c072f2111","year":2018},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:0b0ac3fcba710009e6be8a718a77acd1e19309ebe79bb73b917ff0d48fe8a4be","observation_id":"99276bab-3801-4fc9-a0ad-9073920a6f04","resolution":{"observed_at":"2026-05-24T15:16:14.602013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mobilenetv2: Inverted residuals and linear bottlenecks","venue":null,"work_id":"ba822273-92ff-4550-bb05-25fe1e57f521","year":2018},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:f37c4bf350990d5f80038d0cfbf4f632a8df80731e32d07f518c5d0213fed023","observation_id":"4f25189c-11cf-4e95-829a-1d047cff1d1d","resolution":{"observed_at":"2026-05-24T15:16:14.631366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Very deep convolutional networks for large-scale image recognition","venue":null,"work_id":"0cfcfcab-c997-4cd1-adb9-7702aeef122f","year":2015},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:47448104f74933ff7d51b45f8c52795148bde9b06f9523beafdb231e37fb3d9f","observation_id":"887aa0ea-abd6-435a-97fb-fccd18dca069","resolution":{"observed_at":"2026-05-24T15:16:14.626273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"0051b1a2-f0da-4fec-8211-3e17bfa8c358","year":2016},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:eabaaf7406ecacda1e9a3e25bf43fc0dbc97af9dac55e964c37a706de9cee306","observation_id":"114c9591-2cff-4b3e-bca6-e91f2bd3a691","resolution":{"observed_at":"2026-05-24T15:16:14.563291Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mark, Noam Shazeer, and Kayvon Fata- halian","venue":null,"work_id":"7dc10376-717f-42d4-88b8-02c9a09b8f12","year":2018},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:d1312771a7a7044d559a428e7426fb3aa16e249a536100c443b61c3fff81508b","observation_id":"88ea4485-df8f-4807-a8bf-21dead423a98","resolution":{"observed_at":"2026-05-24T15:16:14.596441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Improving the speed of neural networks on cpus","venue":null,"work_id":"70245fb7-d7f7-43d9-894b-a74b04737705","year":2011},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:0a92da52169261784426e43f140134a823f713e9db7aeadf5360e151dea9f476","observation_id":"eb73b08d-e132-4191-8e1e-753041aa56c3","resolution":{"observed_at":"2026-05-24T15:16:14.556534Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Convolutional networks with adaptive inference graphs","venue":null,"work_id":"4e6a24eb-a6a4-4087-bd69-283110cbc8b2","year":2018},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:a71fd12268bbfd353e0355465bd8c0aec3b4d6d1b32132999fd5b7dc396b6378","observation_id":"6375daba-7e0e-4cb0-b950-16e5ef88d925","resolution":{"observed_at":"2026-05-24T15:16:14.584480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Cnnpack: Packing convolu- tional neural networks in the frequency domain","venue":null,"work_id":"e2e4a096-0289-4a34-af56-0ab40aec9740","year":2016},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:ed86a5e2b7a5e720da6d72f04f29bf91f07b4709b6aec8ad6c625c47907f3e88","observation_id":"bec1eba0-c292-4117-b381-fa4ba7dc12fb","resolution":{"observed_at":"2026-05-24T15:16:14.580367Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Gonzalez","venue":null,"work_id":"ed3d938a-4f87-4ff4-8747-1f562e722752","year":2018},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:3d6497b7a2c5afd2ad1c7000786f3cd3e301d868f9478e1428546eeab60ef14e","observation_id":"950032fd-9600-4946-994a-69a911c1c075","resolution":{"observed_at":"2026-05-24T15:16:14.636662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning structured sparsity in deep neural networks","venue":null,"work_id":"503d61ab-07cb-4349-8a23-188abcec30a3","year":2016},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:1e48d624803a799c5ee6fb7424af6a85f3baea7f1bbb922f8d99abf8aee1aa26","observation_id":"62a610f6-ccbd-43ff-996b-0838cde4a44b","resolution":{"observed_at":"2026-05-24T15:16:14.610421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"l2, 1-norm regularized dis- criminative feature selection for unsupervised learning","venue":null,"work_id":"7402ad45-5227-4178-acd5-cddbd4f14b9b","year":2011},"citing_paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-24T15:15:42.863578Z"},"links":{"citing_paper":"/paper/1907.11840"},"observation_digest":"sha256:e61fa7b1c4e412fdfbd1004f211c139d81e697ebdae0c39fc971e5dc9a9f3cfd","observation_id":"dd159fc1-f5a4-4369-beae-74bb8ae20285","resolution":{"observed_at":"2026-05-24T15:16:14.613331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1907.11840","last_updated":"2019-07-27T02:59:38Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-31T23:59:39.083538Z","submitted_at":"2019-07-27T02:59:38Z","title":"Learning Instance-wise Sparsity for Accelerating Deep Models"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":3,"verified_fuzzy":28},"total_outbound_references":32},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:1907.11840."}