Paper Citation Record · LEDGER

Convergence rates for the Adam optimizer

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2407.21078.

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pith.paper-citation-record.v1
2407.21078 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T06:04:09.661231Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T07:29:38.395619Z

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External citation measurements

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Outbound references

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Pith citing papers

Observation 125510cf-79ac-4136-bc9f-18733c2f3a1c · inbound

Outlier-Robust Training of Machine Learning Models cites this paper.

Outlier-Robust Training of Machine Learning Models Convergence rates for the Adam optimizer

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T23:02:28.158960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0671e8b-a61a-4d9a-86e0-ae23caffabd4 · inbound

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems cites this paper.

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Convergence rates for the Adam optimizer

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T21:11:06.309189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58005b27-ec72-479c-9bf0-f06098d20999 · inbound

Sharp higher order convergence rates for the Adam optimizer cites this paper.

Sharp higher order convergence rates for the Adam optimizer Convergence rates for the Adam optimizer

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T06:04:09.661231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7561269f-221b-42ab-b3e7-386f24565866 · inbound

PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning cites this paper.

PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning Convergence rates for the Adam optimizer

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T13:20:45.494765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20c2675d-46b9-4d78-857e-23e58d6f10d4 · inbound

Adam-SHANG: A Convergent Adam-Type Method for Stochastic Smooth Convex Optimization cites this paper.

Adam-SHANG: A Convergent Adam-Type Method for Stochastic Smooth Convex Optimization Convergence rates for the Adam optimizer

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-14T18:47:36.593770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8253dc68-0c89-4955-849a-615ab6ab6c6c · inbound

Central limit theorem for the averaged Adam optimizer cites this paper.

Central limit theorem for the averaged Adam optimizer Convergence rates for the Adam optimizer

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-04T07:29:38.397461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c7dece8a-b8f7-405f-80bf-1cd65feb3371 · inbound

Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks cites this paper.

Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks Convergence rates for the Adam optimizer

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-11T20:46:05.467029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.