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Paper Citation Record · LEDGER

Riemannian metrics for neural networks I: feedforward networks

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1303.0818.

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

pith.paper-citation-record.v1
1303.0818 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:34:23.624749Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T12:06:55.436438Z

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 90b0e596-3477-4e86-aa87-d4e1915e405c · inbound

Constitutive Manifold Neural Networks cites this paper.

Constitutive Manifold Neural Networks Riemannian metrics for neural networks I: feedforward networks

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T00:34:23.624749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:34:23.624749Z digest=sha256:e8a99ae2ad9d288effd4ac94a589766ce3e0a907b1572f6f0f9d49bd91e13983

Observation 2d9f2429-8f8b-424a-9a50-50015e44fad4 · inbound

Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss cites this paper.

Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss Riemannian metrics for neural networks I: feedforward networks

Reference 144

Resolution
metadata mismatch
local_arxiv, observed 2026-07-02T12:06:55.437633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-06-28T02:35:39.845487Z digest=sha256:24cbe477474aaefd83308e7e02e7bf6b9e6dc0f9e35c6261badf08f55beaa549