Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Non-convergence of Adam and other adaptive stochastic gradient descent optimization methods for non-vanishing learning rates
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2407.08100.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T14:10:55.137201Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T07:29:38.389610Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation f9aaa7f2-7500-4db6-b94c-683116f86386 · inbound
Mathematical analysis of the gradients in deep learning cites this paper.
Mathematical analysis of the gradients in deep learning Non-convergence of Adam and other adaptive stochastic gradient descent optimization methods for non-vanishing learning rates
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:10:55.137201Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6866259e-82f7-440b-84eb-964097cfc13f · inbound
On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning cites this paper.
On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning Non-convergence of Adam and other adaptive stochastic gradient descent optimization methods for non-vanishing learning rates
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T14:47:40.486358Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a0a3a940-70f2-4110-b110-8045033939c7 · inbound
Central limit theorem for the averaged Adam optimizer cites this paper.
Central limit theorem for the averaged Adam optimizer Non-convergence of Adam and other adaptive stochastic gradient descent optimization methods for non-vanishing learning rates
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T07:29:38.391227Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation c30f6b7f-31bf-4dd2-a114-2f580a7bea7d · inbound
On the Convergence of Adam, Revisited cites this paper.
On the Convergence of Adam, Revisited Non-convergence of Adam and other adaptive stochastic gradient descent optimization methods for non-vanishing learning rates
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T01:55:46.406234Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c96cd27d-6069-469a-b059-ca4bd82c14a5 · 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 Non-convergence of Adam and other adaptive stochastic gradient descent optimization methods for non-vanishing learning rates
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T20:46:05.467029Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.