{"as_of":"2026-08-21T21:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b3e40d2f296d0f7f90bc3713231ace17330617d35eae51f1749f2275cfa4d4db","coverage":[{"denominator":38,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T12:08:32.250336Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-27T16:32:17.167850Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T01:27:31.159532Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"cited_work":{"arxiv_id":"2411.17567","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.17567","snapshot_observed_at":"2026-07-03T01:27:31.159532Z","title":"Dexheimer and J","venue":null,"work_id":"d82d9cd6-a06f-4d76-9341-ba57d28375a2","year":2024},"citing_paper":{"arxiv_id":"2606.09734","last_updated":"2026-06-08T16:59:58Z","snapshot_observed_at":"2026-08-15T04:58:56.829898Z","submitted_at":"2026-06-08T16:59:58Z","title":"Adaptive directional gradients for parameterised quantum circuits","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-06-27T16:32:17.167850Z"},"links":{"cited_paper":"/paper/2411.17567","citing_paper":"/paper/2606.09734"},"observation_digest":"sha256:58b58b1c61d438e1a03ec36a7532b9aa38734b8396d7e3d3a5f03415f8c2cb8c","observation_id":"69d85e0b-864b-4d75-a3f7-2c31dc94d7e5","resolution":{"observed_at":"2026-07-03T01:27:31.161043Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.17567/citation-record","integrity":"/paper/2411.17567/integrity","json":"/paper/2411.17567/citation-record.json","paper":"/paper/2411.17567"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:08:35.292017Z","title":"Abramowitz and I","venue":null,"work_id":"7f3a2a00-ed96-4a86-9f00-60748f5bfccc","year":1972},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:30.576003Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:ad8d07c0907cd7e9c4859b8eaaffadec66dfccea92d076b1d1dcf393572f07ee","observation_id":"00f2e110-a43c-4e16-adf8-9db959313509","resolution":{"observed_at":"2026-08-12T12:08:35.353431Z","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":"2026-08-12T12:08:35.271491Z","title":"Towards diffusion approximations for stochastic gradient descent without replacement","venue":null,"work_id":"32dcc284-3269-4d23-a1aa-6e29d5e42705","year":2022},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:30.631649Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:506ac789f7e6da6b0ddd8f09d8461bd234337d072807035eb8708477ee1baff0","observation_id":"e6de8100-8ec0-4e64-bb48-05c5890e0598","resolution":{"observed_at":"2026-08-12T12:08:35.276846Z","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":"2026-08-12T12:08:35.051491Z","title":null,"venue":null,"work_id":"5e1a55bb-6e1a-4ae3-a37e-d7088c424a04","year":1995},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:30.705636Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:769b3827a86f736bacc34026410dd99dc63283a87288fc52b69e1b52841fdba4","observation_id":"816b1d7b-9536-4f6c-ae13-20ce12f56c30","resolution":{"observed_at":"2026-08-12T12:08:35.174155Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2202.08587","last_updated":"2022-02-17T11:07:55Z","snapshot_observed_at":"2026-08-19T00:45:13.062786Z","submitted_at":"2022-02-17T11:07:55Z","title":"Gradients without Backpropagation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.08587","snapshot_observed_at":"2026-08-12T12:08:30.724228Z","title":"Gradients without Backprop- agation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:30.724228Z"},"links":{"cited_paper":"/paper/2202.08587","citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:b89f9280ae0639875f60d2ed01d5ee86243ae1fbb4750f5cc755c6dbe0ef7b8a","observation_id":"c20a1bee-8cce-48cf-a6ea-c3c4a4c1187f","resolution":{"observed_at":"2026-08-12T12:08:30.724228Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:08:30.754629Z","title":"Curriculumlearning","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:30.754629Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:ab2db30e7f7ffdacb68b1f3179e2d29c0fec0fe3a8ce3b51641fcb7149073f30","observation_id":"5b6e24fc-69e5-4112-9710-d53cf6ad1fed","resolution":{"observed_at":"2026-08-12T12:08:30.754629Z","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":"2026-08-12T12:08:35.035931Z","title":"Learningsingle-indexmodelswithshallow neural networks","venue":null,"work_id":"5eab09ae-3ce9-40a3-84ab-7d389ed1a195","year":2022},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:30.775597Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:332cbe7bc28571a92d96e9a3c861985abdadeb22bbf8e4fc8eec967710e8d0d8","observation_id":"25c7ca7e-9e6f-4448-a68e-e79e899c95a9","resolution":{"observed_at":"2026-08-12T12:08:35.040469Z","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":"2026-08-12T12:08:34.829211Z","title":"Convergence guarantees for forward gradient descent in the linear regression model","venue":null,"work_id":"2a7aa060-337c-4078-a893-3d1527ce250d","year":2024},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:30.869109Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:737cf0949cee20f9f58b811a04aa9d7743e4b125bcf38a1fad27fa3a7812497c","observation_id":"caa22045-2ec4-4b7d-9fd4-f3a8b467392a","resolution":{"observed_at":"2026-08-12T12:08:34.920538Z","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":"2026-08-12T12:08:34.761206Z","title":"Is Learning in Biological Neural Networks Based on StochasticGradientDescent?AnAnalysisUsingStochasticProcesses","venue":null,"work_id":"77a9e01d-b3bf-4539-908b-0e278df3c28f","year":2024},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:30.961290Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:73c5b870f4ac69243de8b30d0611a040f54be7a468737fb007f91e19a0c2946f","observation_id":"ac6adee0-968d-4ee5-b755-693cc52a23a0","resolution":{"observed_at":"2026-08-12T12:08:34.770639Z","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":"2026-08-12T12:08:34.707291Z","title":"DropoutRegularizationVersusl2-Penalization in the Linear Model","venue":null,"work_id":"9577afbf-0aeb-4f55-8a1c-a9aacd7135fa","year":2024},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.001524Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:dd43ae6123e7c72e2d90ac7f216423a5baf3c4c4a3c30fd0167fcc74997ded4e","observation_id":"006a37bd-949d-4fcc-8a3c-7397459ac67f","resolution":{"observed_at":"2026-08-12T12:08:34.735847Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:08:31.041156Z","title":null,"venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.041156Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:fc574453cfb5d6fb61c839243a2fa6a3792591ba508305cf25df779099d63256","observation_id":"e1f024f3-5089-413b-88d4-c8d7ab7f66ed","resolution":{"observed_at":"2026-08-12T12:08:31.041156Z","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":"2026-08-12T12:08:34.561691Z","title":"The recent excitement about neural networks","venue":null,"work_id":"987233b8-fb22-4330-8fe3-c5f5c280d9c5","year":1989},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.061788Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:b6decce4b94ac0b65225c74c2c69795fdd9d389b4c736274d070f8c48497a4a3","observation_id":"e9749480-3018-49c5-ad96-82efef19e8db","resolution":{"observed_at":"2026-08-12T12:08:34.633210Z","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":"2026-08-12T12:08:34.524335Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","venue":null,"work_id":"ab82cf35-2da8-459e-99bc-f49e9be04248","year":null},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.161001Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:310ce1b612b24f0468a90c2a9b8695f9cc26118c9cca8e00e93b51357d69a465","observation_id":"9eb34da9-8bac-41d3-a280-0e018c80851a","resolution":{"observed_at":"2026-08-12T12:08:34.540098Z","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":"2026-08-12T12:08:34.443794Z","title":"Optimal Rates for Zero- Order Convex Optimization: The Power of Two Function Evaluations","venue":null,"work_id":"77ce181f-147e-4f6a-b18a-fd5a53075004","year":2015},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.251283Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:7c4d49bdce3dd0129b1dcad2558366460e347ab72e81086d8ed935da814ee162","observation_id":"75c6f764-d711-408a-8104-46aa9cc02157","resolution":{"observed_at":"2026-08-12T12:08:34.506848Z","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":"2026-08-12T12:08:34.315128Z","title":"LearningSingle-IndexModelsinGaussianSpace","venue":null,"work_id":"9b668c35-977a-4387-8e65-a5d9f1ed60aa","year":1930},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.297833Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:a9615ef393add948473e97babf3536a73041c0eb507c97e99577bce84a20e41c","observation_id":"0c4def28-2b34-4135-a017-084fc33a1003","resolution":{"observed_at":"2026-08-12T12:08:34.320080Z","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":"2026-08-12T12:08:34.292329Z","title":"Beyond the Regret Minimization Barrier: Optimal Algorithms for Stochastic Strongly-Convex Optimization","venue":null,"work_id":"d6775e21-bba1-4136-b81d-9a60fc123c12","year":2014},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.313034Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:bd60ba897209172fc1b00bdefd44dd3371b9a110c639c6de17e0fa997d04a6a9","observation_id":"418231ea-8506-4e9d-85c6-af3f0eb6ea4c","resolution":{"observed_at":"2026-08-12T12:08:34.298396Z","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":"2026-08-12T12:08:34.206154Z","title":"The organization of behavior: A neuropsychological theory.NewYork:Wiley,June","venue":null,"work_id":"3a88daff-60f6-4ed5-b3e6-4e26fd074dc8","year":null},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.374716Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:c91ec5b4d4f0dadc0fa73351cf53467bd943b1cfcad85b94fbaabb1b8e4f13ee","observation_id":"3f8fd844-2e88-4a60-be8d-3191bb8cd4d9","resolution":{"observed_at":"2026-08-12T12:08:34.280724Z","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":"2026-08-12T12:08:34.185229Z","title":"Concentrationinequalitiesandmomentboundsforsam- plecovarianceoperators","venue":null,"work_id":"825df078-9ec1-4504-a46f-c6a543746778","year":2017},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.454304Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:3eea4b1587798355cdf18ac50c31ba03ed427bbfe753e218a40fe6f12fcdcba0","observation_id":"68817df9-d434-4626-a372-1c5a25cf6c74","resolution":{"observed_at":"2026-08-12T12:08:34.193489Z","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":"2406.01581","last_updated":"2024-12-22T09:59:09Z","snapshot_observed_at":"2026-08-20T05:15:28.976292Z","submitted_at":"2024-06-03T17:56:58Z","title":"Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01581","snapshot_observed_at":"2026-08-12T12:08:31.468606Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.468606Z"},"links":{"cited_paper":"/paper/2406.01581","citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:1776500273ccc89d0faff076b7738bf4f7f9d18cd9cf64d8779179102382f960","observation_id":"d5f299b4-cfd9-48f4-be49-93cf771f32a7","resolution":{"observed_at":"2026-08-12T12:08:31.468606Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.07434","last_updated":"2024-09-11T17:28:38Z","snapshot_observed_at":"2026-08-19T00:48:49.650675Z","submitted_at":"2024-09-11T17:28:38Z","title":"Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.07434","snapshot_observed_at":"2026-08-12T12:08:31.506796Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.506796Z"},"links":{"cited_paper":"/paper/2409.07434","citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:4f0e614863be8e211aa934f46d9214c875ee750e09a1c85292c6d5e400f06342","observation_id":"e8d487e4-8b77-487d-95f7-5d779e1f1a1d","resolution":{"observed_at":"2026-08-12T12:08:31.506796Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:08:31.512430Z","title":"Backpropagation and the brain","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.512430Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:4c2f90e296ab216f60110bcc4d876cd457df123805407c2b54bca5108fb28240","observation_id":"09ebcf7f-dc95-4829-beb0-7ca32b2b920b","resolution":{"observed_at":"2026-08-12T12:08:31.512430Z","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":"2026-08-12T12:08:34.041628Z","title":"A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning: Principals, Recent Advances, and Applications","venue":null,"work_id":"2242839a-12db-415f-a3fd-8a6fee2d75cd","year":2020},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.589602Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:5766098af1659f551172669fcde66eff6c55201993d9164e904f521e6bf4a620","observation_id":"c2223dfc-50a3-4eb9-9da5-bf5a5f37a252","resolution":{"observed_at":"2026-08-12T12:08:34.060295Z","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":"2026-08-12T12:08:33.875493Z","title":"Continuous-time limit of stochastic gradient descent revisited","venue":null,"work_id":"3eb561ed-6cc1-4525-ad07-2717ad8384ac","year":2015},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.676834Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:2dbba89796ac0247e99d58a97bd4d25add4d5180a6a54b9c9224b3296c3557eb","observation_id":"c8fd3079-3fc1-485e-958e-5e1ebb56b6f9","resolution":{"observed_at":"2026-08-12T12:08:33.976973Z","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":"2026-08-12T12:08:33.849325Z","title":"SGD without Replacement: Sharper Rates for General Smooth Convex Functions","venue":null,"work_id":"b3877d2f-c9ea-4e25-8b3e-4712fa4a9bcd","year":null},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.689223Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:de2b8a182d583226bd0156fbd3f94686cd2bc37a0ecab70fd9d71e812a1d7403","observation_id":"1e0ef21f-32e0-411e-bdf7-e3a0be71f98d","resolution":{"observed_at":"2026-08-12T12:08:33.854818Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T12:08:31.705049Z","title":"Random Gradient-Free Minimization of Convex Func- tions","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.705049Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:b7031f07729dd847e992a9bcfc5de6c0a040d6c82a1ec89aed02356284d9e2fa","observation_id":"ba164d68-6d08-412b-8325-42ff7b80ddb6","resolution":{"observed_at":"2026-08-12T12:08:31.705049Z","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":"2026-08-12T12:08:33.736519Z","title":"Scaling Forward Gradient With Local Losses","venue":null,"work_id":"d538f692-d89e-4ec6-a758-b74cc7438b90","year":2023},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.751664Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:a984634a603b3d4ed1d7799860135fae8836571a7d21eb29b53105dcbd8f8cd4","observation_id":"0582c209-10f4-4f95-bc31-ba78b7153730","resolution":{"observed_at":"2026-08-12T12:08:33.836576Z","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":"2301.11777","last_updated":"2023-03-23T13:28:58Z","snapshot_observed_at":"2026-08-19T00:48:51.701519Z","submitted_at":"2023-01-27T15:30:25Z","title":"Interpreting learning in biological neural networks as zero-order optimization method","version":2},"cited_work":{"arxiv_id":"2301.11777","doi":null,"metadata_source":"pith","pith_arxiv_id":"2301.11777","snapshot_observed_at":"2026-08-12T12:08:32.436884Z","title":"Interpreting learning in biological neural networks as zero-order optimization method","venue":"cs.LG","work_id":"5ff2ebd4-afa6-41ef-81a7-c49ba3936741","year":2023},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.801681Z"},"links":{"cited_paper":"/paper/2301.11777","citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:f2b7071c827d46ee46e61902e44f70f34ca4dedb8de4adba0850da77e97bd812","observation_id":"77e2ce15-d98c-4ae2-8e96-199fec3c3c47","resolution":{"observed_at":"2026-08-12T12:08:32.451945Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.03483","last_updated":"2023-09-26T19:00:32Z","snapshot_observed_at":"2026-08-19T00:48:50.558300Z","submitted_at":"2023-09-26T19:00:32Z","title":"Hebbian learning inspired estimation of the linear regression parameters from queries","version":1},"cited_work":{"arxiv_id":"2311.03483","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.03483","snapshot_observed_at":"2026-08-12T12:08:32.330183Z","title":"Hebbian learning inspired estimation of the linear regression parameters from queries","venue":"math.ST","work_id":"0d0cc0c4-4272-4990-99d8-dc479013e697","year":2023},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.877237Z"},"links":{"cited_paper":"/paper/2311.03483","citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:b5b52e7afe9ebfccc3f03bc899521709f7468c4455b1035358bcec9d9703323a","observation_id":"515dbff5-63d8-4ba9-a966-f7f5b20e9308","resolution":{"observed_at":"2026-08-12T12:08:32.394211Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"2026-08-12T12:08:33.655266Z","title":"Sgd: The role of implicit regularization, batch- sizeandmultiple-epochs","venue":null,"work_id":"50101575-0734-48ca-ab48-d668ad6fa365","year":2021},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.923151Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:7213ff9670dae33e4f2524dbd534a9de058fc6e501e5c815c8cfe8d3480d49a3","observation_id":"2d8d38b9-9842-4493-a3fc-1ceffaf82704","resolution":{"observed_at":"2026-08-12T12:08:33.661997Z","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":"2026-08-12T12:08:33.633096Z","title":"Learningrelusviagradientdescent","venue":null,"work_id":"640daf26-4b5c-46af-87af-4a5664d2ca08","year":2017},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.941412Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:c8d55c7b6f74931235f0c3cc3589aaa4a22f07839d177e155c872cf1daac40c3","observation_id":"e68bd503-0c2b-436f-8740-4cd8fc4dce89","resolution":{"observed_at":"2026-08-12T12:08:33.644587Z","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":"2026-08-12T12:08:33.479058Z","title":"Curriculum learning: A survey","venue":null,"work_id":"2defeb19-0c7d-4140-808a-93090d47fa54","year":2022},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.967940Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:b88035d35c10847cd2d2b1f2532670249bb4b2c9c0638d9733e7d30877fddbaa","observation_id":"8ba97733-2e2f-4861-8c36-f4516b4dd019","resolution":{"observed_at":"2026-08-12T12:08:33.569973Z","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":"2026-08-12T12:08:33.448730Z","title":"Deeplearn- inginspikingneuralnetworks","venue":null,"work_id":"b0656fa5-421a-415f-a94c-426c1ed9451e","year":2019},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:31.973152Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:082549fb396a6e53c5d0d60a4cfcb784bfd2b6330d159cfbe5ced6de84de85e1","observation_id":"65019ef6-76c5-4b59-92d2-b8fb42676bac","resolution":{"observed_at":"2026-08-12T12:08:33.462209Z","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":"2026-08-12T12:08:33.294754Z","title":null,"venue":null,"work_id":"17433dd1-56e8-4c7f-91df-3cba378bb8fe","year":2022},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:32.034908Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:9fce6c49186bb5b8df010ebf788c62684fca919e3cc0488164a6e58d2786bf8f","observation_id":"0a9d7877-5b61-4dbb-b4eb-834da75733b7","resolution":{"observed_at":"2026-08-12T12:08:33.309807Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-12T12:08:33.159894Z","title":"High-Dimensional Probability: An Introduction with Applications in Data Science","venue":null,"work_id":"b62e6c79-38c5-4b79-868b-d9189891bdd1","year":2018},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:32.042910Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:884d0eee4a4248be591188d332da6018970a0c587eec4cbc81bea5f2a2258094","observation_id":"02ceae06-2523-476c-9e72-14d443d7fa6c","resolution":{"observed_at":"2026-08-12T12:08:33.257371Z","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":"2026-08-12T12:08:33.131938Z","title":"Theory of Curriculum Learning, with Convex Loss Func- tions","venue":null,"work_id":"24eb1fcf-1202-41c2-9f86-9d3aa8df9cea","year":2020},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:32.071007Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:6d675530147ad6cdd0203b136a98fe0872cbc4c09a8c12c7d4bd1fa89d8cf57d","observation_id":"96778c89-4dbc-49f4-9288-97bdfd9b325e","resolution":{"observed_at":"2026-08-12T12:08:33.138189Z","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":"2026-08-12T12:08:32.998102Z","title":"CurriculumLearningbyTransferLearning:Theory and Experiments with Deep Networks","venue":null,"work_id":"f042b1b0-c3af-4615-a51a-5e1bff62e2f5","year":2018},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:32.092922Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:f2cd976ac3251f0438bdae3ffc0f919d696c2c995e4bf5360c58eb0a83caf859","observation_id":"7f3bfc5e-e51e-4c35-bc85-3017590c0a40","resolution":{"observed_at":"2026-08-12T12:08:33.022804Z","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":"2026-08-12T12:08:32.853023Z","title":"Theories of Error Back-Propagation in the Brain","venue":null,"work_id":"6c239a8b-f025-45c4-8c8c-c1f4b70bf0fc","year":2019},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:32.166035Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:f5093f968fb763ad28394a4ef2ba9bf6fe7896460f4af1b03d41d76bcd006ec0","observation_id":"69096c47-285c-4dbc-a2cc-c4eaae233f6f","resolution":{"observed_at":"2026-08-12T12:08:32.932013Z","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":"2026-08-12T12:08:32.815576Z","title":"On the statistical benefits of curriculum learning","venue":null,"work_id":"386800ec-b141-4436-af95-66b259405d97","year":2022},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:32.245209Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:d2804f9ea671a5a5533298823e23ebadc11eeb621164e91465e50144abbd95fb","observation_id":"544f2893-97d5-410b-b8ec-8cf51a6bf919","resolution":{"observed_at":"2026-08-12T12:08:32.836458Z","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":"2026-08-12T12:08:32.691172Z","title":"Optimal epoch stochastic gradient descent ascent methods for min-max optimization","venue":null,"work_id":"325ca191-3930-48e4-bd55-c5f44cca0fdf","year":2020},"citing_paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T12:08:32.250336Z"},"links":{"citing_paper":"/paper/2411.17567"},"observation_digest":"sha256:dde12823e40ea134b816a1972f7a9501ede7b794a969f04c3e6d2df741652a89","observation_id":"9bf47b03-e550-4b92-802e-46983db17945","resolution":{"observed_at":"2026-08-12T12:08:32.767976Z","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"}}],"paper":{"arxiv_id":"2411.17567","last_updated":"2024-11-26T16:28:16Z","latest_version":1,"primary_category":"math.ST","snapshot_observed_at":"2026-08-19T09:40:38.678156Z","submitted_at":"2024-11-26T16:28:16Z","title":"Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling"},"reference_resolution":{"displayed":38,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":9,"verified_exact":1,"verified_fuzzy":27},"total_outbound_references":38},"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 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2411.17567."}