Pith. sign in

Paper Citation Record · LEDGER

Blow up phenomena for gradient descent optimization methods in the training of artificial neural networks

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2211.15641.

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

pith.paper-citation-record.v1
2211.15641 v1

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

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T06:04:09.692953Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T07:29:38.356564Z

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 d3d9e9cc-cec9-4211-a353-d84650cddd89 · inbound

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

Mathematical analysis of the gradients in deep learning Blow up phenomena for gradient descent optimization methods in the training of artificial neural networks

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-10T14:10:55.160595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:10:55.160595Z digest=sha256:3330fc807d0df2cf4f075fc2490a5d34e637de752118d4f55379db5dfae801c2

Observation 16cf2906-5a97-40e5-b95f-01a96f2bc6ac · inbound

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

Sharp higher order convergence rates for the Adam optimizer Blow up phenomena for gradient descent optimization methods in the training of artificial neural networks

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-16T06:04:09.692953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T06:04:09.692953Z digest=sha256:649b79fabdd537037224d87f1fe3e99b4659d6e37ecfb971bd464207698274c0

Observation 3df7c9c6-1abd-410a-bfde-b0a24629b2e4 · inbound

PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning cites this paper.

PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning Blow up phenomena for gradient descent optimization methods in the training of artificial neural networks

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T13:20:46.654207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:20:46.654207Z digest=sha256:5b46ad737e69fb55100c717ae52143ea8c8474e895901ff2b458fc04328cc7ba

Observation 48689680-bf34-49ec-88ed-dbc3cbe47fba · inbound

Central limit theorem for the averaged Adam optimizer cites this paper.

Central limit theorem for the averaged Adam optimizer Blow up phenomena for gradient descent optimization methods in the training of artificial neural networks

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-07-04T07:29:38.358020Z

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.

source=pdf_text observed=2026-06-26T13:29:08.750421Z digest=sha256:8f8193eabb68c8d3d49f7bd7567bf01d91c5c251226a8c3bde63ecd6b94d3872

Observation e7b94612-2d9e-491b-95f3-7b7f410dd143 · 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 Blow up phenomena for gradient descent optimization methods in the training of artificial neural networks

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-02T00:02:20.036538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T00:02:20.036538Z digest=sha256:9fb6d8a5ca0e33a4b80115e7910234b33140973499bd23998ace69cfd8ad237a