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Paper Citation Record · LEDGER

Emergence of Structure in Ensembles of Random Neural Networks

As of 24 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2505.10331.

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

pith.paper-citation-record.v1
2505.10331 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:21:37.979412Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

33 of 33 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3cc1e5f8-02fd-46c6-b73a-f0d6af7fcd57 · outbound

This paper cites A review on neural networks with random weights,.

Emergence of Structure in Ensembles of Random Neural Networks A review on neural networks with random weights,

Reference 1

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Observation 5def8cea-1519-44f5-a3f9-6b425d720d8b · outbound

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Emergence of Structure in Ensembles of Random Neural Networks Unresolved cited work

Reference 2

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Observation 5570f92c-86d2-467e-a7d3-67ed3b66c42c · outbound

This paper cites Couillet and Z.

Emergence of Structure in Ensembles of Random Neural Networks Couillet and Z

Reference 3

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Observation 6c42d621-d985-4995-bb8a-399c44973664 · outbound

This paper cites Backpropagation and stochastic gradient descent method,.

Emergence of Structure in Ensembles of Random Neural Networks Backpropagation and stochastic gradient descent method,

Reference 4

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Observation 2fcee71a-ee1c-4f6a-8170-fac47fa667bf · outbound

This paper cites Engel, Statistical mechanics of learning.

Emergence of Structure in Ensembles of Random Neural Networks Engel, Statistical mechanics of learning

Reference 5

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Observation 5a5674b7-410d-4f3b-a2b3-cece5df72505 · outbound

This paper cites On weight initialization in deep neural networks.

Emergence of Structure in Ensembles of Random Neural Networks On weight initialization in deep neural networks

Reference 6

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This paper cites A review on weight initialization strategies for neural networks,.

Emergence of Structure in Ensembles of Random Neural Networks A review on weight initialization strategies for neural networks,

Reference 7

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Observation f2d99a1a-7930-4145-96b0-d76eccd2148d · outbound

This paper cites Eigenvalues of covariance matrices: Application to neural-network learning,.

Emergence of Structure in Ensembles of Random Neural Networks Eigenvalues of covariance matrices: Application to neural-network learning,

Reference 8

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Observation fed200d3-9940-4e1b-bc64-3f0c99818467 · outbound

This paper cites Attention is all you need,.

Emergence of Structure in Ensembles of Random Neural Networks Attention is all you need,

Reference 9

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Observation 89b7f1c8-b392-4bc7-a49e-958d53cac252 · outbound

This paper cites Highly accurate protein structure prediction with alphafold,.

Emergence of Structure in Ensembles of Random Neural Networks Highly accurate protein structure prediction with alphafold,

Reference 10

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Observation d2be8fb9-63bd-4b59-8216-84b6f157d0e7 · outbound

This paper cites How does weight correlation affect generalisation ability of deep neural networks?,.

Emergence of Structure in Ensembles of Random Neural Networks How does weight correlation affect generalisation ability of deep neural networks?,

Reference 11

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Observation d93d23d7-f438-4fa5-a54a-d2ff5d87a0e2 · outbound

This paper cites Heavy-tailed universality predicts trends in test accuracies for very large pre-trained deep neural networks,.

Emergence of Structure in Ensembles of Random Neural Networks Heavy-tailed universality predicts trends in test accuracies for very large pre-trained deep neural networks,

Reference 12

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Observation 01dd3298-ed11-4f7c-8d1b-f30c44cdfd4b · outbound

This paper cites Computational power of neural networks: A characterization in terms of kolmogorov complexity,.

Emergence of Structure in Ensembles of Random Neural Networks Computational power of neural networks: A characterization in terms of kolmogorov complexity,

Reference 13

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Observation ddf62955-9268-4d4c-9669-6f033d73a236 · outbound

This paper cites Spin glass theory and its new challenge: structured disorder,.

Emergence of Structure in Ensembles of Random Neural Networks Spin glass theory and its new challenge: structured disorder,

Reference 14

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Observation 6bfd1256-8d2a-4946-9d2f-3ff1f483fb00 · outbound

This paper cites More is different: Broken symmetry and the nature of the hierarchical structure of science.,.

Emergence of Structure in Ensembles of Random Neural Networks More is different: Broken symmetry and the nature of the hierarchical structure of science.,

Reference 15

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Observation 12467d9e-0b59-4348-9395-cc64ed305d4e · outbound

This paper cites The perceptron: A model for brain functioning. i,.

Emergence of Structure in Ensembles of Random Neural Networks The perceptron: A model for brain functioning. i,

Reference 16

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Observation 6afae37b-1901-40fe-aa14-c812907139ad · outbound

This paper cites Huang, Statistical mechanics.

Emergence of Structure in Ensembles of Random Neural Networks Huang, Statistical mechanics

Reference 17

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Observation 87fad8c7-87eb-4447-9c81-316cd7f7d62e · outbound

This paper cites Mnist handwritten digit database,.

Emergence of Structure in Ensembles of Random Neural Networks Mnist handwritten digit database,

Reference 18

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Observation 6efad838-b6a3-4b11-aed4-597af4ef0445 · outbound

This paper cites Emerging opportunities and challenges for the future of reservoir computing,.

Emergence of Structure in Ensembles of Random Neural Networks Emerging opportunities and challenges for the future of reservoir computing,

Reference 19

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Observation 23d9ec06-f441-4769-a1cb-c34664c897a1 · outbound

This paper cites A review on extreme learning machine,.

Emergence of Structure in Ensembles of Random Neural Networks A review on extreme learning machine,

Reference 20

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Observation 094cf167-9853-497b-abcf-ac2e8422ced5 · outbound

This paper cites Visual feature extraction by a multilayered network of analog threshold ele- ments,.

Emergence of Structure in Ensembles of Random Neural Networks Visual feature extraction by a multilayered network of analog threshold ele- ments,

Reference 21

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Observation b44fb8ba-16af-4702-aa30-d7d1a96eb7b0 · outbound

This paper cites On random weights and unsupervised feature learning.,.

Emergence of Structure in Ensembles of Random Neural Networks On random weights and unsupervised feature learning.,

Reference 22

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Observation cbbc8c99-3d69-4385-8dde-647ccf43c2be · outbound

This paper cites Beyond simple features: A large-scale feature search approach to unconstrained face recognition,.

Emergence of Structure in Ensembles of Random Neural Networks Beyond simple features: A large-scale feature search approach to unconstrained face recognition,

Reference 23

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Observation f10fd9b7-bbdc-4af0-8312-ea981f9eae3a · outbound

This paper cites Deep neural networks with random gaussian weights: A universal classification strategy?,.

Emergence of Structure in Ensembles of Random Neural Networks Deep neural networks with random gaussian weights: A universal classification strategy?,

Reference 24

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Emergence of Structure in Ensembles of Random Neural Networks Deep randomized neural networks,

Reference 25

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Observation 27437e67-45ca-4763-8edf-5e4dff24f2c1 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Emergence of Structure in Ensembles of Random Neural Networks Distilling the Knowledge in a Neural Network

Reference 26

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Observation 1dc4b78b-d178-4f03-a981-1ebd89d73033 · outbound

This paper cites On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning.

Emergence of Structure in Ensembles of Random Neural Networks On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Reference 27

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Observation a5f826d3-4559-4685-8f48-7eb286d20360 · outbound

This paper cites A phase transition in diffusion models reveals the hierarchical nature of data,.

Emergence of Structure in Ensembles of Random Neural Networks A phase transition in diffusion models reveals the hierarchical nature of data,

Reference 28

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Source-reported events for the cited work

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Observation 2d2402eb-edd6-46eb-9f9e-1f6862bc0eca · outbound

This paper cites Why and when can deep-but not shallow-networks avoid the curse of dimensionality: a review,.

Emergence of Structure in Ensembles of Random Neural Networks Why and when can deep-but not shallow-networks avoid the curse of dimensionality: a review,

Reference 29

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Source-reported events for the cited work

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Observation f735d672-cf42-4549-b17b-a61676084974 · outbound

This paper cites Intrinsic dimensionality estimation of submanifolds in rd,.

Emergence of Structure in Ensembles of Random Neural Networks Intrinsic dimensionality estimation of submanifolds in rd,

Reference 30

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Source-reported events for the cited work

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Observation 0c93bcb6-219c-4ec9-a3b7-63ddaf061522 · outbound

This paper cites Asymptotic learning curves of kernel methods: em- pirical data versus teacher–student paradigm,.

Emergence of Structure in Ensembles of Random Neural Networks Asymptotic learning curves of kernel methods: em- pirical data versus teacher–student paradigm,

Reference 31

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Source-reported events for the cited work

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Observation 2cf22678-9559-4303-be94-58239f16b537 · outbound

This paper cites sign nX i exp ˜βEx′ h sign(wT ∗ x′x′T wi) i sign(wT ∗ xxT wi) !# , which can be rewritten, employing the identity sign(x) = 2· 1 ≥(x)− 1 that holds for everyx̸= 0, as −¯L∝ Ex,W1.

Emergence of Structure in Ensembles of Random Neural Networks sign nX i exp ˜βEx′ h sign(wT ∗ x′x′T wi) i sign(wT ∗ xxT wi) !# , which can be rewritten, employing the identity sign(x) = 2· 1 ≥(x)− 1 that holds for everyx̸= 0, as −¯L∝ Ex,W1

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation cb7ec712-30a7-4539-b2da-3241b33eecd3 · outbound

This paper cites Train accuracy : %.3 f.

Emergence of Structure in Ensembles of Random Neural Networks Train accuracy : %.3 f

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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