Paper Citation Record · LEDGER

Random Weight Factorization Improves the Training of Continuous Neural Representations

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2210.01274.

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

pith.paper-citation-record.v1
2210.01274 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:13:42.528080Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:20:07.140766Z

Reference resolution

0 of 0 outbound references displayed

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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 164601d9-6336-4e1d-8dca-a4c8712395e0 · inbound

Physics-informed neural networks for solving moving interface flow problems using the level set approach cites this paper.

Physics-informed neural networks for solving moving interface flow problems using the level set approach Random Weight Factorization Improves the Training of Continuous Neural Representations

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T12:13:42.528080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca96eb09-9d47-4891-86c5-5ef5f53d1d52 · inbound

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries cites this paper.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Random Weight Factorization Improves the Training of Continuous Neural Representations

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-09T12:04:05.112761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6de6bbe-fdfb-4434-9922-fea2dafaa3e7 · inbound

Physics-informed machine learning surrogate for scalable simulation of thermal histories during wire-arc directed energy deposition cites this paper.

Physics-informed machine learning surrogate for scalable simulation of thermal histories during wire-arc directed energy deposition Random Weight Factorization Improves the Training of Continuous Neural Representations

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T17:57:32.672028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11a1cea3-0f07-4ee6-bc68-26aa7ab75616 · inbound

Fragment size density estimator for shrinkage-induced fracture based on a physics-informed neural network cites this paper.

Fragment size density estimator for shrinkage-induced fracture based on a physics-informed neural network Random Weight Factorization Improves the Training of Continuous Neural Representations

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:36.215692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b15b8182-9c4f-494a-98d1-3aee51ccad1b · inbound

Learning Deformable Body Interactions With Adaptive Spatial Tokenization cites this paper.

Learning Deformable Body Interactions With Adaptive Spatial Tokenization Random Weight Factorization Improves the Training of Continuous Neural Representations

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T16:23:47.919127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7709e621-f773-43fd-8fd4-6f46bc462400 · inbound

PINNACLE: An Open-Source Computational Framework for Classical and Quantum PINNs cites this paper.

PINNACLE: An Open-Source Computational Framework for Classical and Quantum PINNs Random Weight Factorization Improves the Training of Continuous Neural Representations

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:48:01.458583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b08eae4b-a1b9-4c08-b7c1-5d0ce146724f · inbound

Oscillatory State-Space Models as Inductive Biases for Physics-Informed Neural PDE Solvers cites this paper.

Oscillatory State-Space Models as Inductive Biases for Physics-Informed Neural PDE Solvers Random Weight Factorization Improves the Training of Continuous Neural Representations

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T20:12:37.660072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation df7c9956-8f2a-47c9-aebf-03223ebf2233 · inbound

RepNN: Tackling spectral bias in deep neural networks via parameter reparameterization cites this paper.

RepNN: Tackling spectral bias in deep neural networks via parameter reparameterization Random Weight Factorization Improves the Training of Continuous Neural Representations

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:18:43.189932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bb54c943-399b-4138-92b2-c97609999d1a · inbound

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers cites this paper.

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers Random Weight Factorization Improves the Training of Continuous Neural Representations

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-04T20:20:07.143797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.