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Paper Citation Record · LEDGER

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation

As of 17 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2602.00545.

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

pith.paper-citation-record.v1
2602.00545 v3

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T06:08:38.421394Z

measured 31 of 31 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T07:49:39.417566Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T07:49:50.825137Z

Reference resolution

30 of 30 outbound references displayed

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External citation measurements

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Outbound references

Observation ea82a3df-6fa8-4f25-8f06-d06b0d2ccf86 · outbound

This paper cites Kakade, and Michael I.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Kakade, and Michael I

Reference 1

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Observation 086a0295-795e-4704-b877-896a7af886f0 · outbound

This paper cites Adahessian: An adaptive second order optimizer for machine learning.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Adahessian: An adaptive second order optimizer for machine learning

Reference 2

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Observation 7cb9d17c-2b5d-4eec-bab4-aafb3d5eabbc · outbound

This paper cites Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training

Reference 3

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Observation 02fdbfbe-1239-4b91-9b55-583c553c6fc3 · outbound

This paper cites Adam-mini: Use Fewer Learning Rates To Gain More.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Adam-mini: Use Fewer Learning Rates To Gain More

Reference 4

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source=pdf_text observed=2026-08-03T06:08:34.989677Z digest=sha256:6fe5564613d1e920541f05653ddd0d61fcc1b46aaa1f2a2fcf750bdf38a31b74

Observation 0c26e057-ae40-468b-9570-316363bdbced · outbound

This paper cites On large-batch training for deep learning: Generalization gap and sharp minima.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation On large-batch training for deep learning: Generalization gap and sharp minima

Reference 5

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Observation cce763f2-5f3c-4f68-92e9-f5d0d1bd29ce · outbound

This paper cites Visualizing the loss landscape of neural nets.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Visualizing the loss landscape of neural nets

Reference 6

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Observation 723a83f3-e465-49d9-829a-ab21dbe0cedb · outbound

This paper cites Vetrov, and Andrew Gordon Wilson.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Vetrov, and Andrew Gordon Wilson

Reference 7

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Observation 3ac2bd12-e81d-4ea3-920d-0832023e1b7a · outbound

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

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Empirical Analysis of the Hessian of Over-Parametrized Neural Networks

Reference 8

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Observation c1963cdb-9dc5-462b-a400-1451564dd894 · outbound

This paper cites Gradient Descent Happens in a Tiny Subspace.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Gradient Descent Happens in a Tiny Subspace

Reference 9

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Observation 7bfe98bc-ee1f-468c-b3ab-fdab3e6dbe40 · outbound

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

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation An investigation into neural net optimization via hessian eigenvalue density

Reference 10

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Observation 13c61a53-9285-49a7-8667-48b8650475dd · outbound

This paper cites Pyhessian: Neural networks through the lens of the hessian.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Pyhessian: Neural networks through the lens of the hessian

Reference 11

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Observation 1eb21678-95e1-4800-88ab-ec31de70aa33 · outbound

This paper cites Zico Kolter, and Ameet Talwalkar.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Zico Kolter, and Ameet Talwalkar

Reference 12

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Observation e8812bcd-ed1d-4686-acab-a3cc3aa75240 · outbound

This paper cites Does sgd really happen in tiny subspaces?, 2024.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Does sgd really happen in tiny subspaces?, 2024

Reference 13

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Observation 44f3a387-fb7b-46b0-8f4c-87d584381e4c · outbound

This paper cites an unresolved cited work.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Unresolved cited work

Reference 14

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Observation d0525bef-6f93-42a1-98d5-7288e1da05b7 · outbound

This paper cites Models of Heavy-Tailed Mechanistic Universality.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Models of Heavy-Tailed Mechanistic Universality

Reference 15

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Observation 9f95393f-96b1-4b23-90de-db564b316b76 · outbound

This paper cites Geometry of neural network loss surfaces via random matrix theory.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Geometry of neural network loss surfaces via random matrix theory

Reference 16

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Observation 6e764501-f90d-4715-83f7-973f7b594962 · outbound

This paper cites A random matrix approach to neural networks.The Annals of Applied Probability, 28(2):1190–1248, 2018.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation A random matrix approach to neural networks.The Annals of Applied Probability, 28(2):1190–1248, 2018

Reference 17

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Observation f6f7e997-b17f-4055-bfe1-799f43810546 · outbound

This paper cites The spectrum of the fisher information matrix of a single-hidden-layer neural network.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation The spectrum of the fisher information matrix of a single-hidden-layer neural network

Reference 18

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Observation ac66228c-8fd9-4b7c-838e-9235d87800c5 · outbound

This paper cites Traces of class/cross-class structure in deep network spectra.Journal of Machine Learning Research, 21(252):1–64, 2020.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Traces of class/cross-class structure in deep network spectra.Journal of Machine Learning Research, 21(252):1–64, 2020

Reference 19

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Observation 500ce38c-6d20-4996-9f8b-d041cd95cc7f · outbound

This paper cites A convergence analysis of gradient descent for deep linear neural networks.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation A convergence analysis of gradient descent for deep linear neural networks

Reference 20

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Observation 4429011e-a35a-4ac0-b493-ceb378d6426c · outbound

This paper cites Analytic insights into structure and rank of neural network hessian maps.Advances in Neural Information Processing Systems, 34:23914–23927, 2021.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Analytic insights into structure and rank of neural network hessian maps.Advances in Neural Information Processing Systems, 34:23914–23927, 2021

Reference 21

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Observation 4052fb58-2015-4457-9490-0ad61b5a1626 · outbound

This paper cites Deep linear network training dynamics from random initialization: Data, width, depth, and hyperparameter transfer.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Deep linear network training dynamics from random initialization: Data, width, depth, and hyperparameter transfer

Reference 22

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Observation 8a6805d3-59a9-4fd9-af4a-e2932bce1e40 · outbound

This paper cites Theoretical characterisation of the gauss-newton conditioning in neural networks.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Theoretical characterisation of the gauss-newton conditioning in neural networks

Reference 23

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Observation 15c883c2-22f0-4a5d-af66-20b3f386a0ee · outbound

This paper cites Depth, not data: An analysis of hessian spectral bifurcation.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Depth, not data: An analysis of hessian spectral bifurcation

Reference 24

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Observation 2f15e5f9-9647-4dd7-8007-30310d389f6a · outbound

This paper cites Since the balanced initialization chooses V such that its column space lies within the support ofΣ xx, we haveV ⊤ΣxxV=I d∗.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Since the balanced initialization chooses V such that its column space lies within the support ofΣ xx, we haveV ⊤ΣxxV=I d∗

Reference 25

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Observation 7bee1521-a540-42ad-9b2d-818837a28931 · outbound

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Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Unresolved cited work

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This paper cites Note that ifd L =d ∗ =d 0, thenU∈R dL×dL is a square orthogonal matrix.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Note that ifd L =d ∗ =d 0, thenU∈R dL×dL is a square orthogonal matrix

Reference 27

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Observation f6ba9769-0bf2-4c6a-8539-ab3c4b6b6182 · outbound

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Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Unresolved cited work

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Observation 156245e9-bcfa-4f68-86c8-3dfcb301e220 · outbound

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Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Unresolved cited work

Reference 29

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Observation bb345218-d900-41d2-9a00-f9ef788526f0 · outbound

This paper cites Furthermore, the eigenvaluesλ i,t fori= 1,.

Depth, Not Data: An Analysis of Hessian Spectral Bifurcation Furthermore, the eigenvaluesλ i,t fori= 1,

Reference 30

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Pith citing papers

Observation 7cc6ded9-5e9e-4ab0-bfb5-580f6b8e4741 · inbound

RMNP: Row-Momentum Normalized Preconditioning for Scalable Matrix-Based Optimization cites this paper.

RMNP: Row-Momentum Normalized Preconditioning for Scalable Matrix-Based Optimization Depth, Not Data: An Analysis of Hessian Spectral Bifurcation

Reference 31

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