{"as_of":"2026-08-13T13:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:91f848cafbf60002f9ed3e4f0cfb19c1979a2a60a51e853bfd40b14821f2ee35","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":"2026-08-13T06:32:02.005865+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":"2026-08-12T11:56:28.259918Z","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":"2026-08-09T11:32:42.090960Z","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":"sha256:7901c885ab5713ead8d653e8a6d0dd719df40d3c96676ba80ff89194889ef5a7","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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":"sha256:9b6ff9bf24c218105d860bd7365786a4f59dcbe0e8886b27ab1ee9364d5ba4b6","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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":"sha256:0a0d2b1ec0de4e170f8015489aa2fc0019a32511c2135f4a067655d35f736034","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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:b5d9ed55782c34ce5ad0c9f180180e3e1c73c14dde7b3ad4888e0f4fd1325926","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.08587","last_updated":"2022-02-17T11:07:55Z","snapshot_observed_at":"2026-08-10T02:29:01.141729Z","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":"2026-08-12T11:56:28.259918Z","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:e57f57784ca2ec9f68453e7b737bdee533e6ea5a02e9132163233fba19ea8942","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":"2026-08-12T11:56:28.259918Z","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":"sha256:77aea76048323615bf772ed92c7c40544501f872162501b5a8e1227530d8a5e6","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":"2026-08-12T11:56:28.259918Z","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":"sha256:0b8b4ca5a9ca1015d6c42d244de11ca9e585ba897c33fad67918f18fe4873aa8","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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":"sha256:99d1203cf021a903ca8849e29269c3332b82b41aee6fa3b935d23675f14eb5d2","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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:d66290708f9a6db52cc594f4d96cc5913e2b62a9ba28175c25e5838c8e8ecb04","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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:d8041fd50bd347ebb3fed3ab39e432746eed2d1b18269f88cfdcf18e01e5928b","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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":"sha256:27fb15856ef6106ca3a2a27f00955030672f558ef955c7a871adbab8e090e0b3","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":"2026-08-12T11:56:28.259918Z","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":"sha256:9a39b3d3ab7530acc2cd31dd4aa2bc7fa9ba73fe184e8abc2e709c57c0d1f685","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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":"sha256:32aee41d937492f7a664ffd364a0e894229777bcb44ec9fc78453fa67db7ce2d","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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":"sha256:4516a25b80572bedb956fd04f987543eed229c90ed0d37fbe615711c4945f9a0","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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:a76d592cdc1a5448ee992a9595c7f83c615e7ea9739e5fd9cc5d774e1e1aca0e","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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":"sha256:3d3c2c9ca284ad57ccf147e0611c761c1912d38469f2caedf4d39873fcc52030","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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":"sha256:605696b9a8eae95e7a6deaa2d013edf44fd58c66c112e9016154c20855ad3433","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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:aaacfc025f4e9afadb4103ac28bb87c9a3068558fb71d56e69ad930229b6b535","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01581","last_updated":"2024-12-22T09:59:09Z","snapshot_observed_at":"2026-08-12T23:51:16.424588Z","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":"2026-08-12T11:56:28.259918Z","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":"sha256:2695acf3a176a37c3ba649e34bcfc05b138f9c4f920dafd3a2a0df35cccb863c","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":"2026-08-13T08:09:30.978749Z","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":"2026-08-12T11:56:28.259918Z","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:fb2d1b557341a627714a19737a22a228ebfe9653cc9384f691951b5a98361b5a","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":"2026-08-12T11:56:28.259918Z","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":"sha256:76d44effa1dc11e99cd97db68c0352a1c88498aef0a805aa464567e92bfd5423","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":"2026-08-12T11:56:28.259918Z","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:af69b3b34efbc26bfd8d5498b7cca5dbef56c7756b8a8aad59169f7aad6ac548","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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:e9adce4161b73f5185d4c9ce5f15c1da18344abfc8655a2b37c9d8feb9fe8f55","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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":"sha256:8f9dc1b505531af75c6a67b765fc626edbf879b49a72b86d184c672a45d3ed6a","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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:d1c1aba065c0ca4f7aa6f5eaa067a2ebbdc55977ce371a6574a6e6f876bb5830","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":"2026-08-12T11:56:28.259918Z","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":"sha256:83380b2761630253eda233dde728fac26c8b1aadcda95a9acb42a9ba4b6889bd","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.11777","last_updated":"2023-03-23T13:28:58Z","snapshot_observed_at":"2026-08-13T12:56:26.534515Z","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":"2026-08-12T11:56:28.259918Z","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":"sha256:22e9833423c7e94ba109f762b742604211d2a1cb4c52ccbcdfedd739d8a3e04c","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.03483","last_updated":"2023-09-26T19:00:32Z","snapshot_observed_at":"2026-08-13T10:04:25.371745Z","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":"2026-08-12T11:56:28.259918Z","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:f7dd3fb02f6c28f6cd5d4a2742c20ff872855d4deba4abe6eca584e7634c4c31","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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:d7b946ffa19fa5076f6615e88dce24a50d1b94940eea9ab651ab0d1f307881ae","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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":"sha256:5f904eafcd001396384e9c9576164b9f1a666a9c4e2650774d19967417c5c6cc","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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:ee3cdb01b6d470e9d61831370778440b3179063897b79eb961a40c93688b0c85","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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":"sha256:90327b54c007574fb702331b1915fe946472da32d306e4a506df1a23e6ab8786","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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":"sha256:167c9874575091c046b4cf6f80441240cdc9ba8e9908e4f23d3825651b471b96","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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":"sha256:67c5067f1cadfbfcbc9386b16e0b24849e40bd8b61222ce81ddad1cff4984810","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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":"sha256:7d1439ef796943cf46fa5abe2206c4ff9245c4a1647187650f1202515ab5d869","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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:b58159dca3513a1fe185f9935dfdc225f340614cdd7909ca7868d05cb5b78278","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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":"sha256:791f19eb37cebb7f8cf52263b69d93f989aa5dbb9292398a535f90f760bbf6eb","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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:a27809e3cddd9f90283dace797935b950094ea9925640a34d9b3b5cf4284d1f5","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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":"sha256:83259b84ea8042301c0aee32d6b57f375a9911e588c8cc6b69720c1dd1e56b75","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 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T11:56:28.259918Z","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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2411.17567."}