Paper Citation Record · LEDGER

Node Perturbation Can Effectively Train Multi-Layer Neural Networks

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2310.00965.

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

pith.paper-citation-record.v1
2310.00965 v8

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:47:52.851527Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T10:11:01.474655Z

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 2fb3e6f8-5a6c-405c-81fa-cbba39cba9aa · inbound

Noise-based Local Learning using Stochastic Magnetic Tunnel Junctions cites this paper.

Noise-based Local Learning using Stochastic Magnetic Tunnel Junctions Node Perturbation Can Effectively Train Multi-Layer Neural Networks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T13:47:52.851527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d100268-1d1f-4f73-a6ac-9ce38e4af91a · inbound

Scaling of hardware-compatible perturbative training algorithms cites this paper.

Scaling of hardware-compatible perturbative training algorithms Node Perturbation Can Effectively Train Multi-Layer Neural Networks

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T14:26:25.888258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae489a5c-5438-410c-9b4f-ed5b54c661e3 · inbound

Training Non-Differentiable Networks via Optimal Transport cites this paper.

Training Non-Differentiable Networks via Optimal Transport Node Perturbation Can Effectively Train Multi-Layer Neural Networks

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-06-03T02:05:39.131799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c046469e-11aa-442c-80b5-4975057df117 · inbound

Conditioned Direct Feedback Alignment via Activity and Error Geometry cites this paper.

Conditioned Direct Feedback Alignment via Activity and Error Geometry Node Perturbation Can Effectively Train Multi-Layer Neural Networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T15:04:34.491172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.