Paper Citation Record · LEDGER

On the Power-Law Hessian Spectrums in Deep Learning

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2201.13011.

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

pith.paper-citation-record.v1
2201.13011 v2

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 2026-08-04T06:34:03.388597+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T21:12:51.213934Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation da3bf9bd-86e8-4011-9d01-6e968bd9c813 · inbound

Sketched Gaussian Mechanism for Private Federated Learning cites this paper.

Sketched Gaussian Mechanism for Private Federated Learning On the Power-Law Hessian Spectrums in Deep Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T21:12:51.213934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f834eb7a-92e9-4f59-bb55-fa826ccc9063 · inbound

Wolkowicz-Styan Upper Bound on the Hessian Eigenspectrum for Cross-Entropy Loss in Nonlinear Smooth Neural Networks cites this paper.

Wolkowicz-Styan Upper Bound on the Hessian Eigenspectrum for Cross-Entropy Loss in Nonlinear Smooth Neural Networks On the Power-Law Hessian Spectrums in Deep Learning

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-10T16:15:34.060474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 84fb903c-5762-4ea2-831d-40881bf2f164 · inbound

Fast Gauss-Newton for Multiclass Cross-Entropy cites this paper.

Fast Gauss-Newton for Multiclass Cross-Entropy On the Power-Law Hessian Spectrums in Deep Learning

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:46:09.643905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 45d59deb-1816-47fb-a754-2bb19e8dfb14 · inbound

Closed-Form Steepest Descent Direction toward Flat Minima: Reducing Upper Bounds on the Loss Hessian Eigenspectrum in Neural Networks cites this paper.

Closed-Form Steepest Descent Direction toward Flat Minima: Reducing Upper Bounds on the Loss Hessian Eigenspectrum in Neural Networks On the Power-Law Hessian Spectrums in Deep Learning

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-06-30T09:44:36.954038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e624a3d6-6efa-489d-93d1-42b76ea93da8 · inbound

Why can genetic algorithms work in high-dimensional search spaces? cites this paper.

Why can genetic algorithms work in high-dimensional search spaces? On the Power-Law Hessian Spectrums in Deep Learning

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-06-30T03:14:14.816995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.