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Paper Citation Record · LEDGER

Stochastic Training of Residual Networks: a Differential Equation Viewpoint

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1812.00174.

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

pith.paper-citation-record.v1
1812.00174 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:16:37.214522Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T12:58:10.227604Z

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 cf2f9d5c-7b87-4457-aea2-9dd46795daf1 · inbound

Bi-Residual Neural Network based Synchronous Motor Electrical Faults Diagnosis: Intra-link Layer Design for High-frequency Features cites this paper.

Bi-Residual Neural Network based Synchronous Motor Electrical Faults Diagnosis: Intra-link Layer Design for High-frequency Features Stochastic Training of Residual Networks: a Differential Equation Viewpoint

Reference 16

Resolution
verified exact
local_arxiv, observed 2026-08-07T12:58:10.288927Z

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=2026-08-07T12:58:08.650692Z digest=sha256:c5c4875fabc46ebdef6bd9fb6d61429ba19d2a6ae38927b339fc3b0fe928acaa

Observation 0eef15b2-10df-40d8-bd02-b69e75ab569b · inbound

Deep Neural Networks Inspired by Differential Equations cites this paper.

Deep Neural Networks Inspired by Differential Equations Stochastic Training of Residual Networks: a Differential Equation Viewpoint

Reference 234

Resolution
unresolved
no resolver link, observed 2026-08-04T10:54:40.242648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T10:54:40.242648Z digest=sha256:a53fdca40311399e607e28f1121f59a8f5e0d1b8a9c13d13d996b9a27509245f

Observation 443a5676-2fc7-454d-890a-77e1c260c6d8 · inbound

EulerLoRA: Rank-Driven Jump Dynamics for Calibrated Parameter-Efficient Fine-Tuning cites this paper.

EulerLoRA: Rank-Driven Jump Dynamics for Calibrated Parameter-Efficient Fine-Tuning Stochastic Training of Residual Networks: a Differential Equation Viewpoint

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-15T15:16:37.214522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T15:16:37.214522Z digest=sha256:3de25a0c9047ba1007cb1f4acd255fb99bf772958f46f8bad72305e1ce51c3e5