Paper Citation Record · LEDGER

Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2102.09385.

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pith.paper-citation-record.v1
2102.09385 v3

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 2026-08-17T06:30:58.91139+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T06:04:09.670445Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T14:14:33.337047Z

Reference resolution

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8d618d5b-6aa8-4343-adb5-401b92de9813 · inbound

Convergence rates for gradient descent in the training of overparameterized artificial neural networks with piecewise affine activation cites this paper.

Convergence rates for gradient descent in the training of overparameterized artificial neural networks with piecewise affine activation Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-24T14:14:33.339870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bef085db-fb93-41a7-b4fa-f05a95bcb251 · inbound

Accelerated Gradient Methods for Nonconvex Optimization: Escape Trajectories From Strict Saddle Points and Convergence to Local Minima cites this paper.

Accelerated Gradient Methods for Nonconvex Optimization: Escape Trajectories From Strict Saddle Points and Convergence to Local Minima Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-05-24T07:54:09.026983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d5e0ef47-aea6-4890-bead-f1c9003d0e2b · inbound

Momentum-based minimization of the Ginzburg-Landau functional on Euclidean spaces and graphs cites this paper.

Momentum-based minimization of the Ginzburg-Landau functional on Euclidean spaces and graphs Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-10T23:02:46.844668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4232203-5f03-46fa-aed2-d4310676e10d · inbound

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

Mathematical analysis of the gradients in deep learning Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.140914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc0e604e-8ec4-4439-9be1-c37486970d8c · inbound

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

Sharp higher order convergence rates for the Adam optimizer Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T06:04:09.670445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0a274df-462d-4836-8d71-506777ca8122 · 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 of stochastic gradient descent schemes for Lojasiewicz-landscapes

Reference 22

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.