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Paper Citation Record · LEDGER

Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2102.09385.

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

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 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:02:46.844668Z

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

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
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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 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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-24T14:10:07.099631Z digest=sha256:50deb787f741d429a72b6f667faaa9a1bd20932e20087765c2bfb246d6a98c0b

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-24T07:51:59.724176Z digest=sha256:ff259b8506519982aeb7e980fb8a00c27262f0412bb24038bd256f4d15f2f253

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.

source=pdf_text observed=2026-08-10T23:02:46.844668Z digest=sha256:635627fd7971b471220312601fcb3365c7dc24dc976d65addce66f81a1c240f0

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.

source=pdf_text observed=2026-08-10T14:10:55.140914Z digest=sha256:bede3ea0f2c9d312bcf7755e4ebe02695e8ddec0217d4948379b4ff9aafcb2e4

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.

source=arxiv_source observed=2026-07-11T20:46:05.467029Z digest=sha256:845b1b0183cf61b7e82e23fb4d91ef09aceb6e0720a30161388ed7103764b78b