{"as_of":"2026-08-21T16:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:71775e6db1c51549bc15f6ef88a28d75354a0856e20f507a44ba86f795f5ce50","coverage":[{"denominator":15,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T11:08:34.765272Z","state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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/1908.09756/citation-record","integrity":"/paper/1908.09756/integrity","json":"/paper/1908.09756/citation-record.json","paper":"/paper/1908.09756"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1611.01144","last_updated":"2017-08-05T22:45:19Z","snapshot_observed_at":"2026-08-15T03:05:41.956181Z","submitted_at":"2016-11-03T19:48:08Z","title":"Categorical Reparameterization with Gumbel-Softmax","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.01144","snapshot_observed_at":"2026-08-14T11:08:34.193331Z","title":"Categorical repa- rameterization with gumbel-softmax","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.09756","last_updated":"2020-06-25T23:36:28Z","snapshot_observed_at":"2026-08-21T08:53:37.868700Z","submitted_at":"2019-08-26T15:56:10Z","title":"Differentiable Product Quantization for End-to-End Embedding Compression","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T11:08:34.193331Z"},"links":{"cited_paper":"/paper/1611.01144","citing_paper":"/paper/1908.09756"},"observation_digest":"sha256:2dbc5b89f3ec70eb75048dc31132f0d63b2a247679e138006b0937261c3cc563","observation_id":"54ea8013-e559-49b2-849d-13499a9919dd","resolution":{"observed_at":"2026-08-14T11:08:34.193331Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.03382","last_updated":"2018-06-07T21:48:19Z","snapshot_observed_at":"2026-08-16T17:42:56.918261Z","submitted_at":"2018-03-09T04:39:35Z","title":"Fast Decoding in Sequence Models using Discrete Latent Variables","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.03382","snapshot_observed_at":"2026-08-14T11:08:34.207485Z","title":"Fast decoding in sequence models using discrete latent variables","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.09756","last_updated":"2020-06-25T23:36:28Z","snapshot_observed_at":"2026-08-21T08:53:37.868700Z","submitted_at":"2019-08-26T15:56:10Z","title":"Differentiable Product Quantization for End-to-End Embedding Compression","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T11:08:34.207485Z"},"links":{"cited_paper":"/paper/1803.03382","citing_paper":"/paper/1908.09756"},"observation_digest":"sha256:3ec9c890455ec42eb88ee0c738e5901ce8335ede8d7159ca4af3a228ad5ffa41","observation_id":"bf6c516b-fe88-4dac-9ac3-0efd9550c977","resolution":{"observed_at":"2026-08-14T11:08:34.207485Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1301.3781","last_updated":"2013-09-07T00:30:40Z","snapshot_observed_at":"2026-07-06T03:04:11.148340Z","submitted_at":"2013-01-16T18:24:43Z","title":"Efficient Estimation of Word Representations in Vector Space","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1301.3781","snapshot_observed_at":"2026-08-14T11:08:34.503579Z","title":"Efficient estimation of word representations in vector space","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.09756","last_updated":"2020-06-25T23:36:28Z","snapshot_observed_at":"2026-08-21T08:53:37.868700Z","submitted_at":"2019-08-26T15:56:10Z","title":"Differentiable Product Quantization for End-to-End Embedding Compression","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T11:08:34.503579Z"},"links":{"cited_paper":"/paper/1301.3781","citing_paper":"/paper/1908.09756"},"observation_digest":"sha256:8ded03e7ad9bca0d8cd0fea4f312500f069996f95849afaaf10dc91896b664b6","observation_id":"c0eb7d56-e806-4828-a461-5530003ad612","resolution":{"observed_at":"2026-08-14T11:08:34.503579Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1508.07909","last_updated":"2016-06-10T14:45:08Z","snapshot_observed_at":"2026-08-17T07:28:37.146698Z","submitted_at":"2015-08-31T16:37:31Z","title":"Neural Machine Translation of Rare Words with Subword Units","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1508.07909","snapshot_observed_at":"2026-08-14T11:08:34.515088Z","title":"Neural machine translation of rare words with subword units","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.09756","last_updated":"2020-06-25T23:36:28Z","snapshot_observed_at":"2026-08-21T08:53:37.868700Z","submitted_at":"2019-08-26T15:56:10Z","title":"Differentiable Product Quantization for End-to-End Embedding Compression","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T11:08:34.515088Z"},"links":{"cited_paper":"/paper/1508.07909","citing_paper":"/paper/1908.09756"},"observation_digest":"sha256:d08d28bc00d2802a6841b3e8b9b2a2f87f52e6ccbd94ef3283a26b7f4e4d01b1","observation_id":"220327cc-8abb-49df-9ae0-08d5bc59fc54","resolution":{"observed_at":"2026-08-14T11:08:34.515088Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.01068","last_updated":"2017-11-17T15:31:45Z","snapshot_observed_at":"2026-08-19T20:14:59.885275Z","submitted_at":"2017-11-03T09:05:44Z","title":"Compressing Word Embeddings via Deep Compositional Code Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.01068","snapshot_observed_at":"2026-08-14T11:08:34.566133Z","title":"and Nakayama, H","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.09756","last_updated":"2020-06-25T23:36:28Z","snapshot_observed_at":"2026-08-21T08:53:37.868700Z","submitted_at":"2019-08-26T15:56:10Z","title":"Differentiable Product Quantization for End-to-End Embedding Compression","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T11:08:34.566133Z"},"links":{"cited_paper":"/paper/1711.01068","citing_paper":"/paper/1908.09756"},"observation_digest":"sha256:950e4d3cc466df0b9065d2bbcb039b4da90238f535fcfa2ec9faa8b102b89107","observation_id":"ed807279-edf4-4917-b42a-0936a7df2c85","resolution":{"observed_at":"2026-08-14T11:08:34.566133Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1409.2329","last_updated":"2015-02-19T14:46:00Z","snapshot_observed_at":"2026-08-21T00:41:57.850170Z","submitted_at":"2014-09-08T13:08:00Z","title":"Recurrent Neural Network Regularization","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.2329","snapshot_observed_at":"2026-08-14T11:08:34.710694Z","title":"Recurrent neural network regularization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.09756","last_updated":"2020-06-25T23:36:28Z","snapshot_observed_at":"2026-08-21T08:53:37.868700Z","submitted_at":"2019-08-26T15:56:10Z","title":"Differentiable Product Quantization for End-to-End Embedding Compression","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T11:08:34.710694Z"},"links":{"cited_paper":"/paper/1409.2329","citing_paper":"/paper/1908.09756"},"observation_digest":"sha256:d48ba5d030dc8a9653226e0a0811a6d5cc505dbd73fa0be31407e8dc6bb84b0c","observation_id":"97918a2c-1d5a-4f18-b36f-2e3465992d3c","resolution":{"observed_at":"2026-08-14T11:08:34.710694Z","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":"2026-08-14T11:08:36.097656Z","title":"Proof of Proposition 1 Proof","venue":null,"work_id":"b8fc6094-cbfe-44ef-a610-2f482697afb2","year":2018},"citing_paper":{"arxiv_id":"1908.09756","last_updated":"2020-06-25T23:36:28Z","snapshot_observed_at":"2026-08-21T08:53:37.868700Z","submitted_at":"2019-08-26T15:56:10Z","title":"Differentiable Product Quantization for End-to-End Embedding Compression","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T11:08:34.744893Z"},"links":{"citing_paper":"/paper/1908.09756"},"observation_digest":"sha256:d96020301dfad4313b47fe85214c2e8da7038dbbdb0cd6cfc1ea699a7f624dec","observation_id":"a4853b54-5c69-415f-820d-4051927823e4","resolution":{"observed_at":"2026-08-14T11:08:36.109059Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:08:35.990457Z","title":"All models were trained with a batch size of 2048 sentences for 250k steps, and with the SM3 optimizer (Anil et al.,","venue":null,"work_id":"28d5009b-c6a8-4a4b-9585-0ff67cda538d","year":null},"citing_paper":{"arxiv_id":"1908.09756","last_updated":"2020-06-25T23:36:28Z","snapshot_observed_at":"2026-08-21T08:53:37.868700Z","submitted_at":"2019-08-26T15:56:10Z","title":"Differentiable Product Quantization for End-to-End Embedding Compression","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T11:08:34.755324Z"},"links":{"citing_paper":"/paper/1908.09756"},"observation_digest":"sha256:4b9749c92679b6e772a455841a9c4316d4fc5a19bf0e28b4651b6a7a92d123f0","observation_id":"dbe79b0b-acba-4453-adea-b2f0f37b89a2","resolution":{"observed_at":"2026-08-14T11:08:36.005981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:08:35.831621Z","title":"For the DPQ experiments, we used DPQ-SX with no subspace-sharing, D = 128 andK = 32, and exactly the same configurations and hyperparameters as in our baseline","venue":null,"work_id":"cec7d27a-cd6e-4540-a046-f1037698fd13","year":2018},"citing_paper":{"arxiv_id":"1908.09756","last_updated":"2020-06-25T23:36:28Z","snapshot_observed_at":"2026-08-21T08:53:37.868700Z","submitted_at":"2019-08-26T15:56:10Z","title":"Differentiable Product Quantization for End-to-End Embedding Compression","version":3},"reference_index":1024,"source":"pdf_text","source_observed_at":"2026-08-14T11:08:34.765272Z"},"links":{"citing_paper":"/paper/1908.09756"},"observation_digest":"sha256:cc56143b124dfe3c4370ccbac2d55d3f4692d93f223261b0482bb19656e37221","observation_id":"9ed559cf-d109-4d12-a6db-0c6f8af0fbf8","resolution":{"observed_at":"2026-08-14T11:08:35.907523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:08:36.144806Z","title":"and Richardson, J","venue":null,"work_id":"2feb2c13-d7b1-4edd-8c11-30b82e41084c","year":2018},"citing_paper":{"arxiv_id":"1908.09756","last_updated":"2020-06-25T23:36:28Z","snapshot_observed_at":"2026-08-21T08:53:37.868700Z","submitted_at":"2019-08-26T15:56:10Z","title":"Differentiable Product Quantization for End-to-End Embedding Compression","version":3},"reference_index":2009,"source":"pdf_text","source_observed_at":"2026-08-14T11:08:34.357956Z"},"links":{"citing_paper":"/paper/1908.09756"},"observation_digest":"sha256:c4bc1097215bd864036948d1d3a77f7a1f95e298a92fb5740cf3b78be8c2a78a","observation_id":"8001d799-e38c-4336-b4f0-8834deb1f2ed","resolution":{"observed_at":"2026-08-14T11:08:36.185858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-08-14T18:16:28.847993Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-14T11:08:33.893625Z","title":"Adaptive mixture of low-rank factorizations for compact neural modeling","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.09756","last_updated":"2020-06-25T23:36:28Z","snapshot_observed_at":"2026-08-21T08:53:37.868700Z","submitted_at":"2019-08-26T15:56:10Z","title":"Differentiable Product Quantization for End-to-End Embedding Compression","version":3},"reference_index":2013,"source":"pdf_text","source_observed_at":"2026-08-14T11:08:33.893625Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/1908.09756"},"observation_digest":"sha256:e4c41924702f572ce356ab760345105258f231a9d0ff180eeb7dcf79039541f5","observation_id":"c5dde2c2-1d9f-49d6-8cb2-2d88ffc31335","resolution":{"observed_at":"2026-08-14T11:08:33.893625Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.04861","last_updated":"2017-04-17T03:57:34Z","snapshot_observed_at":"2026-08-20T09:32:02.065929Z","submitted_at":"2017-04-17T03:57:34Z","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.04861","snapshot_observed_at":"2026-08-14T11:08:33.941084Z","title":"G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.09756","last_updated":"2020-06-25T23:36:28Z","snapshot_observed_at":"2026-08-21T08:53:37.868700Z","submitted_at":"2019-08-26T15:56:10Z","title":"Differentiable Product Quantization for End-to-End Embedding Compression","version":3},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-14T11:08:33.941084Z"},"links":{"cited_paper":"/paper/1704.04861","citing_paper":"/paper/1908.09756"},"observation_digest":"sha256:49e57c14ed9f4141a5929e6a8df64d903de91bfd09994e6180bda2ddc6ed2026","observation_id":"da5a8060-ad3c-49df-a670-5a8975f8a237","resolution":{"observed_at":"2026-08-14T11:08:33.941084Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1502.03167","last_updated":"2015-03-02T20:44:12Z","snapshot_observed_at":"2026-08-12T21:01:12.961874Z","submitted_at":"2015-02-11T01:44:18Z","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1502.03167","snapshot_observed_at":"2026-08-14T11:08:34.051090Z","title":"and Szegedy, C","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.09756","last_updated":"2020-06-25T23:36:28Z","snapshot_observed_at":"2026-08-21T08:53:37.868700Z","submitted_at":"2019-08-26T15:56:10Z","title":"Differentiable Product Quantization for End-to-End Embedding Compression","version":3},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-14T11:08:34.051090Z"},"links":{"cited_paper":"/paper/1502.03167","citing_paper":"/paper/1908.09756"},"observation_digest":"sha256:e8cd791860fbe0ddb9fb61327a43610d025888acff3f57559b8a65cac4520f10","observation_id":"bf501902-2316-4d36-b471-3e65c4bdb1fa","resolution":{"observed_at":"2026-08-14T11:08:34.051090Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1510.00149","last_updated":"2016-02-15T06:25:40Z","snapshot_observed_at":"2026-08-04T16:59:47.843960Z","submitted_at":"2015-10-01T09:03:44Z","title":"Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1510.00149","snapshot_observed_at":"2026-08-14T11:08:33.927497Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.09756","last_updated":"2020-06-25T23:36:28Z","snapshot_observed_at":"2026-08-21T08:53:37.868700Z","submitted_at":"2019-08-26T15:56:10Z","title":"Differentiable Product Quantization for End-to-End Embedding Compression","version":3},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-14T11:08:33.927497Z"},"links":{"cited_paper":"/paper/1510.00149","citing_paper":"/paper/1908.09756"},"observation_digest":"sha256:9db7e31cdc65495645f1195c855748d66cd503ecb24a0e94984212c223e1b16a","observation_id":"1a6e2705-c439-4de7-a604-11991bfbd59f","resolution":{"observed_at":"2026-08-14T11:08:33.927497Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1308.3432","last_updated":"2013-08-15T15:19:34Z","snapshot_observed_at":"2026-08-14T04:51:04.817737Z","submitted_at":"2013-08-15T15:19:34Z","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1308.3432","snapshot_observed_at":"2026-08-14T11:08:33.848309Z","title":"Estimating or propagating gradients through stochastic neurons for con- ditional computation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.09756","last_updated":"2020-06-25T23:36:28Z","snapshot_observed_at":"2026-08-21T08:53:37.868700Z","submitted_at":"2019-08-26T15:56:10Z","title":"Differentiable Product Quantization for End-to-End Embedding Compression","version":3},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-14T11:08:33.848309Z"},"links":{"cited_paper":"/paper/1308.3432","citing_paper":"/paper/1908.09756"},"observation_digest":"sha256:5fd482953949ca38f85e2979125d1268c47d0ff4d2d720d45aadd4ea6633973e","observation_id":"db9ec727-b479-4ab3-b132-019c68e2accd","resolution":{"observed_at":"2026-08-14T11:08:33.848309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"1908.09756","last_updated":"2020-06-25T23:36:28Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-21T08:53:37.868700Z","submitted_at":"2019-08-26T15:56:10Z","title":"Differentiable Product Quantization for End-to-End Embedding Compression"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":0,"verified_fuzzy":4},"total_outbound_references":15},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:1908.09756."}