Paper Citation Record · LEDGER

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods

As of 18 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2504.12601.

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

pith.paper-citation-record.v1
2504.12601 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:37:36.765372Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

21 of 21 outbound references displayed

verified exact0
verified fuzzy11
unresolved10
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ad16737a-cded-4dfa-a108-7969deca011b · outbound

This paper cites Toward a precise smoothness hypothesis in sard’s theorem.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods Toward a precise smoothness hypothesis in sard’s theorem

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:37.060743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a3c84275-9805-4fb6-a377-40e8437ace07 · outbound

This paper cites Dynamics of stochastic approximation algorithms.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods Dynamics of stochastic approximation algorithms

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T12:37:36.686294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0e12b06-60b1-4c05-9ee9-ff2df867ec9e · outbound

This paper cites Adaptive algorithms and stochastic approximations, volume 22.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods Adaptive algorithms and stochastic approximations, volume 22

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:37.032434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8ad73e9d-1faf-4805-a1f0-a5e2cff48bd4 · outbound

This paper cites Stochastic approximation: a dynamical systems viewpoint, volume 9.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods Stochastic approximation: a dynamical systems viewpoint, volume 9

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:37.016164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 11e81e78-91ae-4474-8095-497837fd4097 · outbound

This paper cites Large-scale machine learning with stochastic gradient descent.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods Large-scale machine learning with stochastic gradient descent

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T12:37:36.699940Z

Source-reported events for the cited work

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Observation fca6afb9-02df-460c-97ae-c0e1489d6f8e · outbound

This paper cites Optimization methods for large-scale machine learning.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods Optimization methods for large-scale machine learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T12:37:36.704089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 002d177c-764d-40ed-9ed0-3e1f89377a83 · outbound

This paper cites Stochastic first-and zeroth-order methods for nonconvex stochastic programming.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods Stochastic first-and zeroth-order methods for nonconvex stochastic programming

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T12:37:36.708429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d513c6a4-7326-4e7e-a660-5475d0811232 · outbound

This paper cites Understanding the role of momentum in stochastic gradient methods.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods Understanding the role of momentum in stochastic gradient methods

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:36.972062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c6283f2f-2bec-416e-bc75-b3c0d95d26a7 · outbound

This paper cites A practical guide to training restricted boltzmann machines.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods A practical guide to training restricted boltzmann machines

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:36.957461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d328f59f-7448-4f8a-b2d9-dfe8e45dda1c · outbound

This paper cites Revisit last-iterate convergence of msgd under milder requirement on step size.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods Revisit last-iterate convergence of msgd under milder requirement on step size

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:36.940505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ffe37b04-a244-4cc4-9f85-0c0137c06fbb · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods Adam: A Method for Stochastic Optimization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T12:37:36.723282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c0be079-5477-4c21-8773-463515ecb030 · outbound

This paper cites Stochastic approximation algorithms and applications, vol.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods Stochastic approximation algorithms and applications, vol

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:36.925690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6801d40d-3544-4b8b-ad7e-180ce3aed144 · outbound

This paper cites Efficient backprop.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods Efficient backprop

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T12:37:36.731738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e37a25b5-ae56-4d27-b78a-9675600675ad · outbound

This paper cites Analysis of recursive stochastic algorithms.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods Analysis of recursive stochastic algorithms

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:36.902301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2af8d2bf-16a6-4f7c-8c7f-fc89415b4905 · outbound

This paper cites Theory for the user.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods Theory for the user

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T12:37:36.739942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00d02dbf-34da-4c26-9f20-722dd2d0047f · outbound

This paper cites On the almost sure convergence of stochastic gradient descent in non-convex problems.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods On the almost sure convergence of stochastic gradient descent in non-convex problems

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T12:37:36.744307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddd4fb53-eb3d-401a-8665-6cae6c948be8 · outbound

This paper cites Sgd and hogwild! convergence without the bounded gradients assumption.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods Sgd and hogwild! convergence without the bounded gradients assumption

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:36.869337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a3717365-5f8b-4440-9f82-b1e436342ac0 · outbound

This paper cites A stochastic approximation method.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods A stochastic approximation method

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:36.854640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fd54b708-b542-408c-b768-f6b058eb735d · outbound

This paper cites An overview of gradient descent optimization algorithms.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods An overview of gradient descent optimization algorithms

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T12:37:36.756445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 445941a5-35eb-4f51-ac1f-dd8cd7f3e7a9 · outbound

This paper cites The measure of the critical values of differentiable maps.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods The measure of the critical values of differentiable maps

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:37:36.839647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 57d7a1c1-0859-4513-8ef1-d053fd785028 · outbound

This paper cites Towards theoretically understanding why sgd generalizes better than adam in deep learning.

Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods Towards theoretically understanding why sgd generalizes better than adam in deep learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T12:37:36.765372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.