{"as_of":"2026-08-20T02:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0310aa55d0b9813b7b85a6b96062b9dfe100ebb01db95a0a43d1f930a373df1a","coverage":[{"denominator":14,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-25T02:05:45.602929Z","state":"measured"},{"denominator":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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/1907.02649/citation-record","integrity":"/paper/1907.02649/integrity","json":"/paper/1907.02649/citation-record.json","paper":"/paper/1907.02649"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1409.0473","last_updated":"2016-05-19T21:53:22Z","snapshot_observed_at":"2026-08-18T12:17:42.484599Z","submitted_at":"2014-09-01T16:33:02Z","title":"Neural Machine Translation by Jointly Learning to Align and Translate","version":7},"cited_work":{"arxiv_id":"1409.0473","doi":"10.48550/arxiv.1409.0473","metadata_source":"pith","pith_arxiv_id":"1409.0473","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Neural Machine Translation by Jointly Learning to Align and Translate","venue":"cs.CL","work_id":"d831e763-d530-4029-a65c-ac595d82cb2a","year":2014},"citing_paper":{"arxiv_id":"1907.02649","last_updated":"2019-07-05T01:49:45Z","snapshot_observed_at":"2026-08-15T03:44:33.763745Z","submitted_at":"2019-07-05T01:49:45Z","title":"A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-25T02:05:45.602929Z"},"links":{"cited_paper":"/paper/1409.0473","citing_paper":"/paper/1907.02649"},"observation_digest":"sha256:e29648e9ecf525e567b53a8147e2278a1344230e88a3233e517efb4069e6972c","observation_id":"f4226af6-4aa4-4f2f-9d56-e9697f1d9c4c","resolution":{"observed_at":"2026-05-25T02:06:32.686537Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-07-16T23:20:48.784513+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-16T23:20:48.784513+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-19T06:32:39.956319+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":"End-to-end attention-based large vocabulary speech recognition","venue":null,"work_id":"6db79706-6485-453b-b503-0bbc2c1a538c","year":2016},"citing_paper":{"arxiv_id":"1907.02649","last_updated":"2019-07-05T01:49:45Z","snapshot_observed_at":"2026-08-15T03:44:33.763745Z","submitted_at":"2019-07-05T01:49:45Z","title":"A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-25T02:05:45.602929Z"},"links":{"citing_paper":"/paper/1907.02649"},"observation_digest":"sha256:64413d5c6cc1f658e2a500827e99b8be18f79316ae8e92003284deb54f8c0072","observation_id":"fd74598d-4ad3-4df4-bee2-796a4a2d91ca","resolution":{"observed_at":"2026-05-25T02:06:32.734954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.03993","last_updated":"2019-05-17T08:17:05Z","snapshot_observed_at":"2026-08-18T22:19:46.507711Z","submitted_at":"2019-02-11T16:51:12Z","title":"Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning","version":2},"cited_work":{"arxiv_id":"1902.03993","doi":null,"metadata_source":"pith","pith_arxiv_id":"1902.03993","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning","venue":"cs.LG","work_id":"5c68f1f4-a57a-4f19-a257-f2ea0790bf23","year":2019},"citing_paper":{"arxiv_id":"1907.02649","last_updated":"2019-07-05T01:49:45Z","snapshot_observed_at":"2026-08-15T03:44:33.763745Z","submitted_at":"2019-07-05T01:49:45Z","title":"A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-25T02:05:45.602929Z"},"links":{"cited_paper":"/paper/1902.03993","citing_paper":"/paper/1907.02649"},"observation_digest":"sha256:03c745c03c1cfadf335e0921d1a5e16185438f941123bdf4e2688cf28db58356","observation_id":"c3504014-b8f5-4d18-b60e-0d732af19515","resolution":{"observed_at":"2026-05-25T02:06:32.706191Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1406.1078","last_updated":"2014-09-03T00:25:02Z","snapshot_observed_at":"2026-08-15T13:36:27.756066Z","submitted_at":"2014-06-03T17:47:08Z","title":"Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation","version":3},"cited_work":{"arxiv_id":"1406.1078","doi":"10.48550/arxiv.1406.1078","metadata_source":"pith","pith_arxiv_id":"1406.1078","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation","venue":"cs.CL","work_id":"af085a26-bb72-4b1a-8e38-107776555081","year":2014},"citing_paper":{"arxiv_id":"1907.02649","last_updated":"2019-07-05T01:49:45Z","snapshot_observed_at":"2026-08-15T03:44:33.763745Z","submitted_at":"2019-07-05T01:49:45Z","title":"A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-25T02:05:45.602929Z"},"links":{"cited_paper":"/paper/1406.1078","citing_paper":"/paper/1907.02649"},"observation_digest":"sha256:4bed8ed22d1512089fae598ca7458843b61a203c06353f59e4c9e7278cbf494e","observation_id":"b51a8edd-2e02-461f-86f2-608a1d31f076","resolution":{"observed_at":"2026-05-25T02:06:32.696469Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-07-16T20:21:34.843836+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-16T20:21:34.843836+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.02405","last_updated":"2019-02-06T21:46:34Z","snapshot_observed_at":"2026-08-19T09:06:19.037061Z","submitted_at":"2019-02-06T21:46:34Z","title":"On the Variance of Unbiased Online Recurrent Optimization","version":1},"cited_work":{"arxiv_id":"1902.02405","doi":null,"metadata_source":"pith","pith_arxiv_id":"1902.02405","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"On the Variance of Unbiased Online Recurrent Optimization","venue":"cs.LG","work_id":"c0b2a408-b9cc-4bc0-9090-c0fafbd44774","year":2019},"citing_paper":{"arxiv_id":"1907.02649","last_updated":"2019-07-05T01:49:45Z","snapshot_observed_at":"2026-08-15T03:44:33.763745Z","submitted_at":"2019-07-05T01:49:45Z","title":"A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-25T02:05:45.602929Z"},"links":{"cited_paper":"/paper/1902.02405","citing_paper":"/paper/1907.02649"},"observation_digest":"sha256:918e974328a9947b6ec972aaea3378319ed96526bd2dc8d88eaa11c6a113bdd7","observation_id":"c056bed8-36fc-4042-ac4c-4b5f9bc66c59","resolution":{"observed_at":"2026-05-25T02:06:32.692712Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1308.0850","last_updated":"2014-06-05T16:04:02Z","snapshot_observed_at":"2026-08-15T00:07:53.077276Z","submitted_at":"2013-08-04T21:04:36Z","title":"Generating Sequences With Recurrent Neural Networks","version":5},"cited_work":{"arxiv_id":"1308.0850","doi":"10.48550/arxiv.1308.0850","metadata_source":"pith","pith_arxiv_id":"1308.0850","snapshot_observed_at":"2026-07-10T20:07:33.400866Z","title":"Generating Sequences With Recurrent Neural Networks","venue":"cs.NE","work_id":"3da63ce3-c701-48e0-a32e-756f2712b58c","year":2013},"citing_paper":{"arxiv_id":"1907.02649","last_updated":"2019-07-05T01:49:45Z","snapshot_observed_at":"2026-08-15T03:44:33.763745Z","submitted_at":"2019-07-05T01:49:45Z","title":"A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-25T02:05:45.602929Z"},"links":{"cited_paper":"/paper/1308.0850","citing_paper":"/paper/1907.02649"},"observation_digest":"sha256:ca39a6d9e5c5b320ed3e6432defd24a9a64931a5776ec2353123c24233d19037","observation_id":"74d07050-9fd2-4159-9793-96537fd3399c","resolution":{"observed_at":"2026-05-25T02:06:32.714776Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-17T19:26:44.032537Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":"1412.6980","doi":"10.1002/mrm.28086","metadata_source":"pith","pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Adam: A Method for Stochastic Optimization","venue":"cs.LG","work_id":"1910796d-9b52-4683-bf5c-de9632c1028b","year":2014},"citing_paper":{"arxiv_id":"1907.02649","last_updated":"2019-07-05T01:49:45Z","snapshot_observed_at":"2026-08-15T03:44:33.763745Z","submitted_at":"2019-07-05T01:49:45Z","title":"A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-25T02:05:45.602929Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/1907.02649"},"observation_digest":"sha256:ea60113d95b6dcc72690b70c670d80626f7943918e26a7842263fd786cbcf631","observation_id":"d175fcbe-88e5-49e0-9e83-fbcb6e49faf9","resolution":{"observed_at":"2026-05-25T02:06:32.697233Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.conb.2019.01.011","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"doi: https://doi.org/ 10.1016/j.conb.2019.01.011","venue":"Current Opinion in Neurobiology","work_id":"c7ca1efe-6f5c-4e8b-a0fe-064b4de35999","year":2019},"citing_paper":{"arxiv_id":"1907.02649","last_updated":"2019-07-05T01:49:45Z","snapshot_observed_at":"2026-08-15T03:44:33.763745Z","submitted_at":"2019-07-05T01:49:45Z","title":"A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-25T02:05:45.602929Z"},"links":{"citing_paper":"/paper/1907.02649"},"observation_digest":"sha256:73c6a87f05950937320074251f48f8874f6265ba770a2c7921b27aa5cc21f0c3","observation_id":"95544b46-ad4d-4d82-acbf-bd4a9743ac23","resolution":{"observed_at":"2026-05-25T02:06:32.415910Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.07411","last_updated":"2019-08-11T00:41:14Z","snapshot_observed_at":"2026-08-17T22:14:16.022489Z","submitted_at":"2018-10-17T07:36:47Z","title":"Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations","version":4},"cited_work":{"arxiv_id":"1810.07411","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1810.07411","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Online learning of recur- rent neural architectures by locally aligning distributed representations","venue":null,"work_id":"592e70d9-0c80-4a9d-aea2-728dd7dd1e2c","year":null},"citing_paper":{"arxiv_id":"1907.02649","last_updated":"2019-07-05T01:49:45Z","snapshot_observed_at":"2026-08-15T03:44:33.763745Z","submitted_at":"2019-07-05T01:49:45Z","title":"A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-25T02:05:45.602929Z"},"links":{"cited_paper":"/paper/1810.07411","citing_paper":"/paper/1907.02649"},"observation_digest":"sha256:db4cd89d4e3f0e6de261f2686b3ddf21c351225efb987fd0cb3099d1e7f214de","observation_id":"24126657-0330-4d60-b796-8147eb5ec5b4","resolution":{"observed_at":"2026-05-25T02:06:32.719891Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.11542","last_updated":"2017-11-30T18:03:47Z","snapshot_observed_at":"2026-08-19T03:14:49.907705Z","submitted_at":"2017-11-30T18:03:47Z","title":"Learning to Adapt by Minimizing Discrepancy","version":1},"cited_work":{"arxiv_id":"1711.11542","doi":null,"metadata_source":"pith","pith_arxiv_id":"1711.11542","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Learning to Adapt by Minimizing Discrepancy","venue":"cs.LG","work_id":"e69a7af4-e29b-4f85-bc29-100afdebe174","year":2017},"citing_paper":{"arxiv_id":"1907.02649","last_updated":"2019-07-05T01:49:45Z","snapshot_observed_at":"2026-08-15T03:44:33.763745Z","submitted_at":"2019-07-05T01:49:45Z","title":"A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-25T02:05:45.602929Z"},"links":{"cited_paper":"/paper/1711.11542","citing_paper":"/paper/1907.02649"},"observation_digest":"sha256:aafc5dc416383bf20fbebaa89231a798f28df7d3851c29457b67565e4f3016a4","observation_id":"cb2a3ab0-7267-4b07-b5d7-7780ba411b49","resolution":{"observed_at":"2026-05-25T02:06:32.701498Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Christopher Roth, Ingmar Kanitscheider, and Ila Fiete","venue":null,"work_id":"fae72587-bcb9-4043-bae7-2db781b62e19","year":2018},"citing_paper":{"arxiv_id":"1907.02649","last_updated":"2019-07-05T01:49:45Z","snapshot_observed_at":"2026-08-15T03:44:33.763745Z","submitted_at":"2019-07-05T01:49:45Z","title":"A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-25T02:05:45.602929Z"},"links":{"citing_paper":"/paper/1907.02649"},"observation_digest":"sha256:6af85a1a5f96078402d3f155274a1eacfca098461dc047bc4343621acea3434b","observation_id":"a6c9924e-6bc4-45a1-bb5c-5127eae8fc39","resolution":{"observed_at":"2026-05-25T02:06:32.723394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1702.05043","last_updated":"2017-05-23T11:42:03Z","snapshot_observed_at":"2026-08-15T10:35:56.722584Z","submitted_at":"2017-02-16T16:38:08Z","title":"Unbiased Online Recurrent Optimization","version":3},"cited_work":{"arxiv_id":"1702.05043","doi":null,"metadata_source":"pith","pith_arxiv_id":"1702.05043","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Unbiased Online Recurrent Optimization","venue":"cs.NE","work_id":"06f3e30b-5005-4ba7-b43c-ab0d711f15c9","year":2017},"citing_paper":{"arxiv_id":"1907.02649","last_updated":"2019-07-05T01:49:45Z","snapshot_observed_at":"2026-08-15T03:44:33.763745Z","submitted_at":"2019-07-05T01:49:45Z","title":"A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-25T02:05:45.602929Z"},"links":{"cited_paper":"/paper/1702.05043","citing_paper":"/paper/1907.02649"},"observation_digest":"sha256:44a92e4b2771f96bd8fb0498618a3a328cd4723d9a6e0dae16480b2fded42c47","observation_id":"5a68f9e6-382b-479e-b2fd-ea97f1b73d2e","resolution":{"observed_at":"2026-05-25T02:06:32.710394Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"Lemma for generating rank-1 unbiased estimates For completeness, we state the Lemma from Tallec and Ollivier (2017) in components notation","venue":null,"work_id":"f4873e30-7287-4590-a4fd-c517b75d99dd","year":2017},"citing_paper":{"arxiv_id":"1907.02649","last_updated":"2019-07-05T01:49:45Z","snapshot_observed_at":"2026-08-15T03:44:33.763745Z","submitted_at":"2019-07-05T01:49:45Z","title":"A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-25T02:05:45.602929Z"},"links":{"citing_paper":"/paper/1907.02649"},"observation_digest":"sha256:64430462d533f79f29dfe1a5c29d6309cd8bbfc76a791ecbee0f7d1a699573c0","observation_id":"2f5cb300-7b7b-45b1-aea4-7644761510b9","resolution":{"observed_at":"2026-05-25T02:06:32.729708Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"(2019) use (1 − exp(−γi)) rather than αi as a temporal filter for B(t) ij","venue":null,"work_id":"76951af4-91eb-457d-af5d-bf4055d7618b","year":2015},"citing_paper":{"arxiv_id":"1907.02649","last_updated":"2019-07-05T01:49:45Z","snapshot_observed_at":"2026-08-15T03:44:33.763745Z","submitted_at":"2019-07-05T01:49:45Z","title":"A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-25T02:05:45.602929Z"},"links":{"citing_paper":"/paper/1907.02649"},"observation_digest":"sha256:ef74ae188af523086988810aae48dc8592ad4e11e1d4e9160ca7cc2957aeed6e","observation_id":"9f31b854-7c06-46a8-b4fa-368773734941","resolution":{"observed_at":"2026-05-25T02:06:32.726611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1907.02649","last_updated":"2019-07-05T01:49:45Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T03:44:33.763745Z","submitted_at":"2019-07-05T01:49:45Z","title":"A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks"},"reference_resolution":{"displayed":14,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":10,"verified_fuzzy":4},"total_outbound_references":14},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:1907.02649."}