Paper Citation Record · LEDGER

Non-identifiability distinguishes Neural Networks among Parametric Models

As of 20 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2504.18017.

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

pith.paper-citation-record.v1
2504.18017 v2

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:37:33.207081Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

29 of 29 outbound references displayed

verified exact0
verified fuzzy21
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3c6d7d88-dc8d-4d26-947a-252c892f3633 · outbound

This paper cites Learning and inference in hierarchical models with singularities.

Non-identifiability distinguishes Neural Networks among Parametric Models Learning and inference in hierarchical models with singularities

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.663521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c2449daa-e978-45f1-9648-0087135ba91f · outbound

This paper cites Universal approximation bounds for superpositions of a sigmoidal function.

Non-identifiability distinguishes Neural Networks among Parametric Models Universal approximation bounds for superpositions of a sigmoidal function

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.647656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ba0870b9-5a69-4f2c-94b0-ec3b20c307f7 · outbound

This paper cites Functional vs.

Non-identifiability distinguishes Neural Networks among Parametric Models Functional vs

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.630162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bfb5078c-a620-4f30-b2d3-f7f035a6e7a8 · outbound

This paper cites Approximation by superpositions of a sigmoidal function.

Non-identifiability distinguishes Neural Networks among Parametric Models Approximation by superpositions of a sigmoidal function

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.614109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b7bda96e-cacb-405e-808d-456790d88db9 · outbound

This paper cites Shallow vs.

Non-identifiability distinguishes Neural Networks among Parametric Models Shallow vs

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.596980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 73af1aec-f8f0-49f7-b0a7-44de803c692f · outbound

This paper cites Sharp minima can generalize for deep nets.

Non-identifiability distinguishes Neural Networks among Parametric Models Sharp minima can generalize for deep nets

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.581110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6e743f5b-f009-4b0f-95c8-8ff726eff58b · outbound

This paper cites The power of depth for feedforward neural networks.

Non-identifiability distinguishes Neural Networks among Parametric Models The power of depth for feedforward neural networks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.566025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d22b88f5-2519-4480-ad86-6b5cb3253864 · outbound

This paper cites Real analysis: modern techniques and their applications , volume 40.

Non-identifiability distinguishes Neural Networks among Parametric Models Real analysis: modern techniques and their applications , volume 40

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T10:37:33.101647Z

Source-reported events for the cited work

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Observation 125d4187-9f49-4312-a894-bd7717bcd5ed · outbound

This paper cites Structural degeneracy in neural networks.

Non-identifiability distinguishes Neural Networks among Parametric Models Structural degeneracy in neural networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.542475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0a1e1ce9-c88c-40ab-93ce-97e04f85f87c · outbound

This paper cites A regularity condition of the information matrix of a multilayer perceptron network.

Non-identifiability distinguishes Neural Networks among Parametric Models A regularity condition of the information matrix of a multilayer perceptron network

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.527099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d39e0807-1668-4ee2-b72f-5e1914986c9d · outbound

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

Non-identifiability distinguishes Neural Networks among Parametric Models An investigation into neural net optimization via hessian eigenvalue density

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.512880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9a78e9a6-eab8-4bda-ab20-41165eda8765 · outbound

This paper cites Universal function approximation by deep neural nets with bounded width and relu activations.

Non-identifiability distinguishes Neural Networks among Parametric Models Universal function approximation by deep neural nets with bounded width and relu activations

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T10:37:33.120842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11633dc4-1629-437c-ae3a-f30a160b4f41 · outbound

This paper cites Approximating Continuous Functions by ReLU Nets of Minimal Width.

Non-identifiability distinguishes Neural Networks among Parametric Models Approximating Continuous Functions by ReLU Nets of Minimal Width

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T10:37:33.125694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0adf168f-f78e-42bd-91be-e2eb291ec2c7 · outbound

This paper cites Multilayer feedforward networks are universal approximators.

Non-identifiability distinguishes Neural Networks among Parametric Models Multilayer feedforward networks are universal approximators

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.488798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cb5676c2-35d6-4e8e-bd67-0bbe7adc8ee2 · outbound

This paper cites The normalization method for alleviating pathological sharpness in wide neural networks.

Non-identifiability distinguishes Neural Networks among Parametric Models The normalization method for alleviating pathological sharpness in wide neural networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.474298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e3240f3c-dfb9-4e3c-a1b0-1f24ddd61ec7 · outbound

This paper cites Pathological spectra of the Fisher information metric and its variants in deep neural networks.

Non-identifiability distinguishes Neural Networks among Parametric Models Pathological spectra of the Fisher information metric and its variants in deep neural networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T10:37:33.140322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9cfce06-33ab-41b9-b4a4-ca793eea0ede · outbound

This paper cites Universal statistics of fisher information in deep neural networks: Mean field approach.

Non-identifiability distinguishes Neural Networks among Parametric Models Universal statistics of fisher information in deep neural networks: Mean field approach

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.457497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f3ab8ffe-338b-4a85-b12e-96d315db48e4 · outbound

This paper cites On the ability of neural nets to express distributions.

Non-identifiability distinguishes Neural Networks among Parametric Models On the ability of neural nets to express distributions

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.442506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c03e68f8-2c40-4de5-8946-2fad952c4cc3 · outbound

This paper cites The connection between approximation, depth separation and learnability in neural networks.

Non-identifiability distinguishes Neural Networks among Parametric Models The connection between approximation, depth separation and learnability in neural networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.427668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8f396b7c-b810-4260-8b4f-f615cd74b940 · outbound

This paper cites Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians.

Non-identifiability distinguishes Neural Networks among Parametric Models Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T10:37:33.160086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ff78364-e0b0-4a24-9db8-3f79bd408e0b · outbound

This paper cites Topological properties of the set of functions generated by neural networks of fixed size.

Non-identifiability distinguishes Neural Networks among Parametric Models Topological properties of the set of functions generated by neural networks of fixed size

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.413228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1e5354ae-0c9f-4080-b33c-4eb4d722d72c · outbound

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

Non-identifiability distinguishes Neural Networks among Parametric Models The spectrum of the fisher information matrix of a single-hidden-layer neural network

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.398039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0d70e040-19f8-42d4-b8ca-efa0a40a63e4 · outbound

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

Non-identifiability distinguishes Neural Networks among Parametric Models Empirical Analysis of the Hessian of Over-Parametrized Neural Networks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T10:37:33.174264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79eba81f-c08a-4708-ab20-f19d0be90cce · outbound

This paper cites Understanding machine learning: From theory to algorithms.

Non-identifiability distinguishes Neural Networks among Parametric Models Understanding machine learning: From theory to algorithms

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T10:37:33.179297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2967f474-e37f-4a87-9638-cf1e0d55d5b6 · outbound

This paper cites Representation Benefits of Deep Feedforward Networks.

Non-identifiability distinguishes Neural Networks among Parametric Models Representation Benefits of Deep Feedforward Networks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T10:37:33.184516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f83104d-afb7-4097-ab33-8db553433405 · outbound

This paper cites Algebraic geometry and statistical learning theory , volume 25.

Non-identifiability distinguishes Neural Networks among Parametric Models Algebraic geometry and statistical learning theory , volume 25

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.372300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 821d83b5-fcc8-47d8-a086-bbb6a07f1c03 · outbound

This paper cites Mathematical theory of Bayesian statistics.

Non-identifiability distinguishes Neural Networks among Parametric Models Mathematical theory of Bayesian statistics

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.357248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b29dc414-f9fc-4aa4-b9bd-83fa1e4cedae · outbound

This paper cites Deep learning is singular, and that’s good.

Non-identifiability distinguishes Neural Networks among Parametric Models Deep learning is singular, and that’s good

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.341932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 948ccda7-8b39-4891-9a89-354e4b8a15f6 · outbound

This paper cites The lack of a priori distinctions between learning algorithms.

Non-identifiability distinguishes Neural Networks among Parametric Models The lack of a priori distinctions between learning algorithms

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:37:33.326044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.