Paper Citation Record · LEDGER

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming

As of 5 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2604.06350.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2604.06350 v3

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T18:38:58.883757Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

verified exact18
verified fuzzy5
unresolved6
parse uncertain0
malformed identifier0
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External citation measurements

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Outbound references

Observation c17a36ba-0ff8-43d3-9ec0-488f3eaf066f · outbound

This paper cites Absil, R.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Absil, R

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T16:58:07.837012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ec8d9300-8384-4944-adda-39dffaf158e1 · outbound

This paper cites Adachi S.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Adachi S

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T16:58:07.841975Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ced4e664-e2b0-4034-b921-e46327b4cba1 · outbound

This paper cites an unresolved cited work.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-05-16T16:58:07.839665Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d281d6d9-3d46-4d6e-a63a-2a9a499ce1e1 · outbound

This paper cites an unresolved cited work.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Unresolved cited work

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-10T18:40:43.821598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9d7d14fe-e6cf-4bf2-a8b4-9b94cb074909 · outbound

This paper cites Bertsekas, J.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Bertsekas, J

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T16:58:07.844503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation badf3219-f967-45d0-ae66-c3c401e962ce · outbound

This paper cites Bonnabel,Stochastic Gradient Descent on Riemannian Manifolds,IEEE Transactions on Automatic Control, Volume 58, Issue 9, September 2013.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Bonnabel,Stochastic Gradient Descent on Riemannian Manifolds,IEEE Transactions on Automatic Control, Volume 58, Issue 9, September 2013

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T16:58:07.828809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 062eb9c2-13ae-469c-98ec-75964ef023ed · outbound

This paper cites an unresolved cited work.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-05-16T16:58:07.831462Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 65e49422-3b42-42bd-831b-102e3c8d95ea · outbound

This paper cites Bernstein, J., Wang, Y .-X., Azizzadenesheli, K., and Anand- kumar, A.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Bernstein, J., Wang, Y .-X., Azizzadenesheli, K., and Anand- kumar, A

Reference 8

Resolution: verified exact; doi, observed 2026-05-10T18:40:43.816220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 115e3556-9121-4664-a1c2-2ce1293bb16a · outbound

This paper cites an unresolved cited work.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-05-16T16:58:07.834005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a17bbc2f-40f1-44cb-b6a5-e20ca9f12fcd · outbound

This paper cites AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:15:50.879275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cf19c4b9-52c3-4938-8d72-a93a9ee5c38f · outbound

This paper cites and Iiduka, H.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming and Iiduka, H

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:10:53.403947Z

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No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 96875aba-eeba-4ebc-9ce4-fe3662b29a43 · outbound

This paper cites an unresolved cited work.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-05-16T16:58:07.822736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3ef300f3-0353-4936-a83f-cac4e59f93c4 · outbound

This paper cites Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:10:53.387410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 06b94e82-6df9-4af7-a69c-075f8e1dd027 · outbound

This paper cites Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-04T21:55:55.135246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b10f2784-44c7-4ff1-b591-18a24a4966c4 · outbound

This paper cites Accelerating Stochastic Gradient Descent Using Antithetic Sampling.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Accelerating Stochastic Gradient Descent Using Antithetic Sampling

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:15:50.960937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 809ec902-af93-4c32-9e7f-dfc5ea59a49c · outbound

This paper cites Mairal,Stochastic majorization-minimization algorithms for large-scale optimization,Advances in Neural Information Processing Systems, pages 2283–2291.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Mairal,Stochastic majorization-minimization algorithms for large-scale optimization,Advances in Neural Information Processing Systems, pages 2283–2291

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T16:58:07.825796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c3f2e6f4-c73e-48f3-b8a7-829ebc71f193 · outbound

This paper cites Ostroukhov, A.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Ostroukhov, A

Reference 17

Resolution: verified exact; doi, observed 2026-05-10T18:40:43.818920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d45888a4-73f4-4d42-a5cb-e3f4f00a07f1 · outbound

This paper cites Accelerating Minibatch Stochastic Gradient Descent using Typicality Sampling.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Accelerating Minibatch Stochastic Gradient Descent using Typicality Sampling

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-04T23:27:31.511868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 36dfd9ed-26c4-40ee-acdc-d62cd581e1bd · outbound

This paper cites Optimal Mini-Batch Size Selection for Fast Gradient Descent.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Optimal Mini-Batch Size Selection for Fast Gradient Descent

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:15:50.927292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7da9b7b7-83d7-4f58-b98a-ef11a6fe6168 · outbound

This paper cites The Impact of the Mini-batch Size on the Variance of Gradients in Stochastic Gradient Descent.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming The Impact of the Mini-batch Size on the Variance of Gradients in Stochastic Gradient Descent

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:15:50.888856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0c901f71-d70c-4a69-9c42-ff28102ce384 · outbound

This paper cites Improving the convergence of SGD through adaptive batch sizes.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Improving the convergence of SGD through adaptive batch sizes

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:10:53.378599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 817f82f5-7fb0-434d-88f9-8a911bc80502 · outbound

This paper cites Increasing Both Batch Size and Learning Rate Accelerates Stochastic Gradient Descent.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Increasing Both Batch Size and Learning Rate Accelerates Stochastic Gradient Descent

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:15:50.943130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation db677db4-9898-4773-ab3d-627082eb869d · outbound

This paper cites Adaptive Batch Size and Learning Rate Scheduler for Stochastic Gradient Descent Based on Minimization of Stochastic First-order Oracle Complexity.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Adaptive Batch Size and Learning Rate Scheduler for Stochastic Gradient Descent Based on Minimization of Stochastic First-order Oracle Complexity

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:15:50.861012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7046a45f-8bf9-495c-90d4-37dbda174324 · outbound

This paper cites Weighted Low-rank Approximation via Stochastic Gradient Descent on Manifolds.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Weighted Low-rank Approximation via Stochastic Gradient Descent on Manifolds

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:15:50.917182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5ecc8adf-b1e7-4890-9dea-5b6e56640418 · outbound

This paper cites an unresolved cited work.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-05-16T16:58:07.815053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 52784257-75e6-49e9-a8d8-935a6b9d4150 · outbound

This paper cites an unresolved cited work.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-05-16T16:58:07.819954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1e812dfd-7760-4c47-b994-78750ffad88f · outbound

This paper cites Adaptive Stochastic Gradient Descents on Manifolds with an Application on Weighted Low-Rank Approximation.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Adaptive Stochastic Gradient Descents on Manifolds with an Application on Weighted Low-Rank Approximation

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:10:53.399797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ea20203f-f894-43d7-9630-d5e3487120a8 · outbound

This paper cites Determinantal Point Processes for Mini-Batch Diversification.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Determinantal Point Processes for Mini-Batch Diversification

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:10:53.393872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 19c8a13b-8b9f-4696-9962-23d0c8aeb64d · outbound

This paper cites Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling.

Convergence of Riemannian Stochastic Gradient Descents: Varying Batch Sizes And Nonstandard Batch Forming Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:10:53.382341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

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