Paper Citation Record · LEDGER

Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2402.05155.

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

pith.paper-citation-record.v1
2402.05155 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T06:04:09.733450Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T19:47:57.960720Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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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 4eedb1c9-c93b-4322-95cf-0880eaeeb427 · inbound

Mathematical analysis of the gradients in deep learning cites this paper.

Mathematical analysis of the gradients in deep learning Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.203554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19228ced-58a2-4c41-8298-742441154085 · inbound

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

Sharp higher order convergence rates for the Adam optimizer Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T06:04:09.733450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c5073c3-093f-4d64-8f5c-fd5fc7375bde · inbound

Constructive Universal Approximation and Sure Convergence for Multi-Layer Neural Networks cites this paper.

Constructive Universal Approximation and Sure Convergence for Multi-Layer Neural Networks Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:47:57.965492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 57254e2a-78b5-4d58-bab9-26d47c727c39 · inbound

Landscape analysis for shallow neural networks: Complete classification of critical points for cubic activation and affine target functions cites this paper.

Landscape analysis for shallow neural networks: Complete classification of critical points for cubic activation and affine target functions Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-02T00:02:20.680666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64d5786f-b143-4eab-b889-d8ef75d17e49 · inbound

Breaking the Curse with BAND: Nonparametric Distribution Estimation in High Dimensions cites this paper.

Breaking the Curse with BAND: Nonparametric Distribution Estimation in High Dimensions Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks

Reference 209

Resolution: unresolved; no resolver link, observed 2026-07-30T15:56:46.734540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a3224f1-d351-424e-8e55-7363201d016e · inbound

On MUON optimization: From non-convergence to an error analysis with Polar Express and the Newton-Schulz polynomial from implementations cites this paper.

On MUON optimization: From non-convergence to an error analysis with Polar Express and the Newton-Schulz polynomial from implementations Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T21:05:43.250760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.