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Paper Citation Record · LEDGER

Do Residual Neural Networks discretize Neural Ordinary Differential Equations?

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2205.14612.

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

pith.paper-citation-record.v1
2205.14612 v2

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

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T10:08:40.882193Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T12:10:09.067676Z

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 261f86d8-6360-4ec8-8e1a-0d763d9efa3d · inbound

From Layers to States: A State Space Model Perspective to Deep Neural Network Layer Dynamics cites this paper.

From Layers to States: A State Space Model Perspective to Deep Neural Network Layer Dynamics Do Residual Neural Networks discretize Neural Ordinary Differential Equations?

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-08T10:08:40.882193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T10:08:40.882193Z digest=sha256:ee18a8ec6fcfd2a275dceaef5cd3f96c6dff04673dc8dcbd6f76449a4f143ee0

Observation ce438e1c-c00f-4ea0-aedd-641905424e4e · inbound

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling cites this paper.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Do Residual Neural Networks discretize Neural Ordinary Differential Equations?

Reference 12

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T12:10:09.071585Z

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=2026-08-06T12:10:08.616770Z digest=sha256:9a6f4495319a4f14295e5c670889cad2d6f159043bd90bb808c81e94502e104e