Paper Citation Record · LEDGER

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks

As of 19 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2608.12597.

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

pith.paper-citation-record.v1
2608.12597 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:08:33.467330Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

24 of 24 outbound references displayed

verified exact0
verified fuzzy17
unresolved6
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 813f79ef-836b-400c-9768-9229ff52ee96 · outbound

This paper cites Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:34.763847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ea84966b-9de2-4ac3-84aa-c58110e5b0cc · outbound

This paper cites The fast Johnson–Lindenstrauss transform and approximate nearest neighbors.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks The fast Johnson–Lindenstrauss transform and approximate nearest neighbors

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:34.752462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d3d15786-d731-4ed9-8c35-8cf12f88a787 · outbound

This paper cites Living on the Edge: Phase Transitions in Convex Programs with Random Data.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks Living on the Edge: Phase Transitions in Convex Programs with Random Data

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T00:08:31.837274Z

Source-reported events for the cited work

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Observation f13ba29d-4351-446a-8763-125c751c49ba · outbound

This paper cites Exact expressions for double descent and implicit regularization via surrogate random design.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks Exact expressions for double descent and implicit regularization via surrogate random design

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:34.740759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5b5ae243-86e7-4c2d-ae3b-6152ddaa230c · outbound

This paper cites An investigation into neural net optimization via hessian eigenvalue density.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks An investigation into neural net optimization via hessian eigenvalue density

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:34.727703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b3e135b1-e5bc-4d9b-b28f-25d5cf40e728 · outbound

This paper cites On Milman’s Inequality and Random Subspaces which Escape through a Mesh in Rn.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks On Milman’s Inequality and Random Subspaces which Escape through a Mesh in Rn

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:34.616816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8e733eb3-e193-4a23-9eaa-668ad3861839 · outbound

This paper cites Improving Neural Network Training in Low Dimensional Random Bases.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks Improving Neural Network Training in Low Dimensional Random Bases

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:34.604263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4132ba17-2358-4846-b6dc-4259abcd6121 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks LoRA: Low-Rank Adaptation of Large Language Models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:34.561987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aeb3677e-58ca-490a-914d-4cb38efaaf50 · outbound

This paper cites NOLA: Compressing LoRA using Linear Combination of Random Basis.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks NOLA: Compressing LoRA using Linear Combination of Random Basis

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af22e4c3-7fc9-419c-bb6e-586a0f15a97c · outbound

This paper cites VeRA: Vector-based Random Matrix Adaptation.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks VeRA: Vector-based Random Matrix Adaptation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:34.530199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d4effd97-f469-43fe-b41a-9cf78c91fdd1 · outbound

This paper cites How Many Degrees of Freedom Do We Need to Train Deep Networks: A Loss Landscape Perspective.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks How Many Degrees of Freedom Do We Need to Train Deep Networks: A Loss Landscape Perspective

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:34.516594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0401c524-29e6-4aec-b2db-a90e0293fda2 · outbound

This paper cites Fastfood - Computing Hilbert Space Ex- pansions in Loglinear Time.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks Fastfood - Computing Hilbert Space Ex- pansions in Loglinear Time

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:34.375344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 33e07686-36e8-4138-bcc7-134087ee7ba7 · outbound

This paper cites Eigenvectors of some large sample covariance matrix ensembles.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks Eigenvectors of some large sample covariance matrix ensembles

Reference 13

Resolution: malformed identifier; raw_fallback, observed 2026-08-16T00:08:34.363564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2d2a0872-92f5-476d-ba68-776aae0fbb0a · outbound

This paper cites Measuring the intrinsic dimension of objective landscapes.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks Measuring the intrinsic dimension of objective landscapes

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:34.352908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 12674da8-e811-481f-a2eb-c5f9c38f0478 · outbound

This paper cites D´ ecomposition orthogonale d’un espace hilbertien selon deux cˆ ones mutuellement polaires.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks D´ ecomposition orthogonale d’un espace hilbertien selon deux cˆ ones mutuellement polaires

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:34.232131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4868493b-ab0d-4977-8230-e15e16d7e13a · outbound

This paper cites PRANC: Pseudo RAndom Networks for Compacting deep models.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks PRANC: Pseudo RAndom Networks for Compacting deep models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T00:08:32.655162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6457d4f-6717-4bc8-af8f-4b43d7207107 · outbound

This paper cites Traces of class/cross-class structure pervade deep learning spectra.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks Traces of class/cross-class structure pervade deep learning spectra

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:34.058613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 18d61a6d-9ada-422d-a768-a669a7b50cdb · outbound

This paper cites Spectral convergence for a general class of random matrices.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks Spectral convergence for a general class of random matrices

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:34.046679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 68dfad4a-8a60-4011-a539-acb70c8c9d82 · outbound

This paper cites Empirical Analysis of the Hessian of Over-Parametrized Neural Networks.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks Empirical Analysis of the Hessian of Over-Parametrized Neural Networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T00:08:33.244736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbbbff45-f2f8-493c-b994-e465408b485b · outbound

This paper cites Mapping Networks.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks Mapping Networks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:33.909289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4ad33e4e-c446-47b2-9edc-91a0d0573ae5 · outbound

This paper cites Improved analysis of the subsampled randomized Hadamard transform.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks Improved analysis of the subsampled randomized Hadamard transform

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:33.732141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 04efdb92-04aa-4c13-84f5-148056c982e8 · outbound

This paper cites Cambridge Series in Statistical and Probabilistic Mathematics.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks Cambridge Series in Statistical and Probabilistic Mathematics

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T00:08:33.382517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9401236-9a44-4856-84cb-3d8a18289cd5 · outbound

This paper cites Theorem2 (Random affine-slice intersection: conic phase transition).Let S⊂R P be nonempty, compact, and convex, let θ0 /∈S, and let C be the closed convex cone of Lemma 1.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks Theorem2 (Random affine-slice intersection: conic phase transition).Let S⊂R P be nonempty, compact, and convex, let θ0 /∈S, and let C be the closed convex cone of Lemma 1

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:08:33.716870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0d86c5c1-2936-48e9-87b0-c603b0047f14 · outbound

This paper cites an unresolved cited work.

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:08:33.651930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.