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Paper Citation Record · LEDGER

Statistical mechanics of extensive-width Bayesian neural networks near interpolation

As of 10 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2505.24849.

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

pith.paper-citation-record.v1
2505.24849 v2

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:20:28.261300Z

measured 32 of 32 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:46:58.455179Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T14:46:58.823686Z

Reference resolution

31 of 31 outbound references displayed

  • verified exact1
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  • unresolved12
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 715bb315-01e6-4bad-8913-d6cfb60b9dd6 · outbound

This paper cites an unresolved cited work.

Statistical mechanics of extensive-width Bayesian neural networks near interpolation Unresolved cited work

Reference 2

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Observation 2f8caef1-7bd2-4169-92ff-9fa41dbf9739 · outbound

This paper cites universal.

Statistical mechanics of extensive-width Bayesian neural networks near interpolation universal

Reference 5

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This paper cites (57) Recall V is the support of Pv (assumed discrete for the moment).

Statistical mechanics of extensive-width Bayesian neural networks near interpolation (57) Recall V is the support of Pv (assumed discrete for the moment)

Reference 8

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Observation ed0d6e15-1a3a-4c68-ade7-530b787ae493 · outbound

This paper cites (58) The measure is coupled only through the latter δ’s.

Statistical mechanics of extensive-width Bayesian neural networks near interpolation (58) The measure is coupled only through the latter δ’s

Reference 9

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This paper cites (61) We have used the fact that⟨ · ⟩ˆB(v) is symmetric if the prior PW is, thus forcing us to match j with p if i ̸= l.

Statistical mechanics of extensive-width Bayesian neural networks near interpolation (61) We have used the fact that⟨ · ⟩ˆB(v) is symmetric if the prior PW is, thus forcing us to match j with p if i ̸= l

Reference 10

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This paper cites (63) Notice that the effective law ˜P ((Sa.

Statistical mechanics of extensive-width Bayesian neural networks near interpolation (63) Notice that the effective law ˜P ((Sa

Reference 11

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This paper cites an unresolved cited work.

Statistical mechanics of extensive-width Bayesian neural networks near interpolation Unresolved cited work

Reference 12

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This paper cites (65) The factor V kd W (QW ) was already treated in the previous section.

Statistical mechanics of extensive-width Bayesian neural networks near interpolation (65) The factor V kd W (QW ) was already treated in the previous section

Reference 13

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This paper cites an unresolved cited work.

Statistical mechanics of extensive-width Bayesian neural networks near interpolation Unresolved cited work

Reference 14

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This paper cites an unresolved cited work.

Statistical mechanics of extensive-width Bayesian neural networks near interpolation Unresolved cited work

Reference 15

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This paper cites As usual, the Nishimori identities impose Qaa 2 = r2 = 1 + γ¯v2 without the need of any Fourier conjugate.

Statistical mechanics of extensive-width Bayesian neural networks near interpolation As usual, the Nishimori identities impose Qaa 2 = r2 = 1 + γ¯v2 without the need of any Fourier conjugate

Reference 16

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This paper cites Y (or S0, ξ).

Statistical mechanics of extensive-width Bayesian neural networks near interpolation Y (or S0, ξ)

Reference 17

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Statistical mechanics of extensive-width Bayesian neural networks near interpolation Unresolved cited work

Reference 18

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Statistical mechanics of extensive-width Bayesian neural networks near interpolation Unresolved cited work

Reference 19

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This paper cites an unresolved cited work.

Statistical mechanics of extensive-width Bayesian neural networks near interpolation Unresolved cited work

Reference 20

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This paper cites an unresolved cited work.

Statistical mechanics of extensive-width Bayesian neural networks near interpolation Unresolved cited work

Reference 21

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This paper cites In this case, inspired by (Sakata & Kabashima, 2013; Kabashima et al., 2016), in order to relax (14) we can propose the Gaussian ansatz d ¯P ((Sa.

Statistical mechanics of extensive-width Bayesian neural networks near interpolation In this case, inspired by (Sakata & Kabashima, 2013; Kabashima et al., 2016), in order to relax (14) we can propose the Gaussian ansatz d ¯P ((Sa

Reference 22

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Statistical mechanics of extensive-width Bayesian neural networks near interpolation Unresolved cited work

Reference 23

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Statistical mechanics of extensive-width Bayesian neural networks near interpolation sp” and “uni

Reference 25

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Statistical mechanics of extensive-width Bayesian neural networks near interpolation components

Reference 26

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This paper cites the true distribution P ((Sa.

Statistical mechanics of extensive-width Bayesian neural networks near interpolation the true distribution P ((Sa

Reference 27

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Statistical mechanics of extensive-width Bayesian neural networks near interpolation Unresolved cited work

Reference 28

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Statistical mechanics of extensive-width Bayesian neural networks near interpolation standard Gaussian entries

Reference 29

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Statistical mechanics of extensive-width Bayesian neural networks near interpolation This entails dropping entirely the dependencies among matrix entries, induced by their Wishart-like form (92), for each Sa 2(v)

Reference 30

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Statistical mechanics of extensive-width Bayesian neural networks near interpolation universal

Reference 31

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Statistical mechanics of extensive-width Bayesian neural networks near interpolation Feature learning in finite-width Bayesian deep linear networks with multiple outputs and convolutional layers

Reference 916

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This paper cites URL https: //doi.org/10.1080/00018732.2016.1211393.

Statistical mechanics of extensive-width Bayesian neural networks near interpolation URL https: //doi.org/10.1080/00018732.2016.1211393

Reference 2016

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Statistical mechanics of extensive-width Bayesian neural networks near interpolation Adam: A Method for Stochastic Optimization

Reference 2017

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Statistical mechanics of extensive-width Bayesian neural networks near interpolation doi: 10.1007/s00440-018-0879-0

Reference 2019

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Statistical mechanics of extensive-width Bayesian neural networks near interpolation Guionnet, A

Reference 2022

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Observation 630f28d6-0b51-4143-bb5e-8771cff0bf2e · outbound

This paper cites Fundamental limits of overparametrized shallow neural networks for supervised learning.

Statistical mechanics of extensive-width Bayesian neural networks near interpolation Fundamental limits of overparametrized shallow neural networks for supervised learning

Reference 2023

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Pith citing papers

Observation 3d7ea875-7d57-41df-b093-b0440d5354f5 · inbound

Microscopic and collective signatures of feature learning in neural networks cites this paper.

Microscopic and collective signatures of feature learning in neural networks Statistical mechanics of extensive-width Bayesian neural networks near interpolation

Reference 53

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