Pith. sign in

Paper Citation Record · LEDGER

Gradient Descent as a Shrinkage Operator for Spectral Bias

As of 17 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2504.18207.

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

pith.paper-citation-record.v1
2504.18207 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:30:08.782584Z

measured 28 of 28 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 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

28 of 28 outbound references displayed

  • verified exact0
  • verified fuzzy23
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a64816cf-7d4e-49ae-ae0d-fff2602b06f0 · outbound

This paper cites Stability and generalization of learning algorithms that converge to global 8 optima.

Gradient Descent as a Shrinkage Operator for Spectral Bias Stability and generalization of learning algorithms that converge to global 8 optima

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:09.237777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.654722Z digest=sha256:462b33b16004613f7fe8760bdccfc1781748492696a2e110588161147c1737e2

Observation 7e62e6ee-df9d-4423-8370-5fb33fda475b · outbound

This paper cites Approximation by superpositions of a sig- moidal function.

Gradient Descent as a Shrinkage Operator for Spectral Bias Approximation by superpositions of a sig- moidal function

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:09.220842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.659871Z digest=sha256:83467bc93be62944466ca293613106f7b9a0a9ba560bf0c544899f426fce05d3

Observation 3f17f610-b158-4711-87cc-70b803b83544 · outbound

This paper cites an unresolved cited work.

Gradient Descent as a Shrinkage Operator for Spectral Bias Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-08-16T10:30:09.205413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.664746Z digest=sha256:42ddfafcaf63c9b694aee6f3c2defa4dd54b9681417a28e43a32cbeea48ad9e6

Observation 275765a5-b62b-49fa-950a-045e3250449f · outbound

This paper cites Spectral bias in practice: The role of function fre- quency in generalization.

Gradient Descent as a Shrinkage Operator for Spectral Bias Spectral bias in practice: The role of function fre- quency in generalization

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:09.191065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.669329Z digest=sha256:8f1a4be73dee0ee2f19fb9021f1602a39a46619267da4873ff54d1164b1bdd16

Observation 03b91e58-7d81-4335-b7de-8c20535fb7ed · outbound

This paper cites Optimal shrinkage of singular values.

Gradient Descent as a Shrinkage Operator for Spectral Bias Optimal shrinkage of singular values

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:09.176075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.674868Z digest=sha256:1659eda37633dcb8e5c690a163ca9808cadd9f5a193ea0a795a0f9b0a7bd5981

Observation d6e3c413-3e88-4406-af69-ec5a6682718d · outbound

This paper cites Stochastic training is not nec- essary for generalization.

Gradient Descent as a Shrinkage Operator for Spectral Bias Stochastic training is not nec- essary for generalization

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:09.161443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.679709Z digest=sha256:f29d954df4f615b469e7df141c3d38943b1ab5f9dc48cc3f0e3b5e070b275fc3

Observation e7827710-4ece-4925-b0d4-cf1aaf6ee72c · outbound

This paper cites Train faster, generalize better: Stability of stochastic gradient descent.

Gradient Descent as a Shrinkage Operator for Spectral Bias Train faster, generalize better: Stability of stochastic gradient descent

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-16T10:30:08.685172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:30:08.685172Z digest=sha256:03dfd3ce71ba077fb00d42dc5ff1f70b753227d8d1481fe0125b4f751bd7eb6c

Observation 7c064db6-6c01-4510-83e2-93626671bc41 · outbound

This paper cites How Implicit Regularization of ReLU Neural Networks Characterizes the Learned Function -- Part I: the 1-D Case of Two Layers with Random First Layer.

Gradient Descent as a Shrinkage Operator for Spectral Bias How Implicit Regularization of ReLU Neural Networks Characterizes the Learned Function -- Part I: the 1-D Case of Two Layers with Random First Layer

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-16T10:30:08.689978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:30:08.689978Z digest=sha256:8763ccafedc07e44d56a85afc82cf8458430ad3a57756658f2e1ee87ba2e88c0

Observation 7085e4ef-69a3-4ef5-9041-8a152ec5b8b6 · outbound

This paper cites Train longer, generalize better: closing the generalization gap in large batch training of neural networks.

Gradient Descent as a Shrinkage Operator for Spectral Bias Train longer, generalize better: closing the generalization gap in large batch training of neural networks

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:09.135875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.695000Z digest=sha256:6fbf557937a63d199dae2b45f39495bfba362d4596a3d5174dc1e20a0ffd768a

Observation 804cee8c-df77-4973-82e2-40e620a4951c · outbound

This paper cites Neu- ral tangent kernel: Convergence and generalization in neural networks.

Gradient Descent as a Shrinkage Operator for Spectral Bias Neu- ral tangent kernel: Convergence and generalization in neural networks

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:09.120819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.699894Z digest=sha256:e88148fc1fc6af314a9ee022c1bc7d21357d418cd47a7234964989785fd5a836

Observation 246ead73-152f-4e3e-a526-06accb66910e · outbound

This paper cites A note on the use of principal components in regression.

Gradient Descent as a Shrinkage Operator for Spectral Bias A note on the use of principal components in regression

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:09.105496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.704604Z digest=sha256:ca01df1314f1507ceadac84daeefe3e8966b6414b8ae5471290a1235d2a573c9

Observation 4b8a08c5-a0ac-435e-8c07-64521ec4d58d · outbound

This paper cites Imagenet classification with deep convolutional neural net- works.

Gradient Descent as a Shrinkage Operator for Spectral Bias Imagenet classification with deep convolutional neural net- works

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:09.090388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.709247Z digest=sha256:fd75276f1a2c92732f4def3182be0883d70779de59c415da30c65c79aaf82ec4

Observation 16de50e3-24f9-4134-9008-f151f952bf4d · outbound

This paper cites Beyond lipschitz: Sharp generalization and excess risk bounds for full-batch gd.

Gradient Descent as a Shrinkage Operator for Spectral Bias Beyond lipschitz: Sharp generalization and excess risk bounds for full-batch gd

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:09.075503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.713510Z digest=sha256:7f08286114f4f614590c6c8f916297fa05a821c96f5677411a562a659059a4ad

Observation 8899c65a-dd3a-4ed3-874d-83e74c68e2ff · outbound

This paper cites Beyond periodicity: Towards a unifying framework for activations in coordinate- mlps.

Gradient Descent as a Shrinkage Operator for Spectral Bias Beyond periodicity: Towards a unifying framework for activations in coordinate- mlps

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:09.059660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.718410Z digest=sha256:8e8e1ea7f1f201dbcc7b1a51d0b60a469ec9b91ceaeaade0134ecf725f3e63a0

Observation 6d80c98c-c76a-480b-8b7b-879c89d3bcd3 · outbound

This paper cites Smoothing by spline functions.

Gradient Descent as a Shrinkage Operator for Spectral Bias Smoothing by spline functions

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:09.044344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.722922Z digest=sha256:254a9d5326aeda7ad7b107ecb40892b37193ca21537e8e2fd13361ea16a1600d

Observation ebf585bc-e064-4ba7-851c-4c43f7732367 · outbound

This paper cites A sam- pling theory perspective on activations for implicit neural representations.

Gradient Descent as a Shrinkage Operator for Spectral Bias A sam- pling theory perspective on activations for implicit neural representations

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:09.028413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.727191Z digest=sha256:c6dc1fa8833fde3fc0e0f48cd6cb1a29fe0760958c6925cadfa6908be8f5bb3d

Observation bf5cbe57-aca4-476f-b1e7-20a4388b8456 · outbound

This paper cites Communication in the presence of noise.

Gradient Descent as a Shrinkage Operator for Spectral Bias Communication in the presence of noise

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:09.012544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.731767Z digest=sha256:7048c2c8c0d1a66ebd8062e830a13bfa5bf9a51465e4d7762b889435fec1ca21

Observation f3818ca9-db27-4abb-8594-1e18f17e0c60 · outbound

This paper cites Implicit neural representa- tions with periodic activation functions.

Gradient Descent as a Shrinkage Operator for Spectral Bias Implicit neural representa- tions with periodic activation functions

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:08.997872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.736243Z digest=sha256:03c3fce9deb483dfddef2545bebec5a9151143a3243284ba5224ce3d97538b4a

Observation 0f1ca6ee-a80f-4cca-acdd-874e94b1e2c3 · outbound

This paper cites The implicit bias of gradi- ent descent on separable data.

Gradient Descent as a Shrinkage Operator for Spectral Bias The implicit bias of gradi- ent descent on separable data

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:08.982521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.740694Z digest=sha256:69dd6d55bde8d59287a52e07f8abad5f989b6446fa37449009900c2c707d9d0c

Observation f40c872c-3abf-42eb-82eb-2013630d03a3 · outbound

This paper cites Fourier features let networks learn high frequency functions in low dimen- sional domains.

Gradient Descent as a Shrinkage Operator for Spectral Bias Fourier features let networks learn high frequency functions in low dimen- sional domains

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:08.966584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.746009Z digest=sha256:453d552ec397d6a9a25295e97d6158b691ed55f221f6dff170fb1624a58249b5

Observation ce5a93c3-d1f8-49dd-80ba-158bf239b339 · outbound

This paper cites Sampling-50 years after shannon.

Gradient Descent as a Shrinkage Operator for Spectral Bias Sampling-50 years after shannon

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:08.950853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.750695Z digest=sha256:c6147d305ef1492e93ee7b9364172dcbf2f6731c12db9c86b7a455de8913ad86

Observation 293fc9ca-a1a1-476a-a192-e08acdfd1ab9 · outbound

This paper cites Principles of risk minimization for learn- ing theory.

Gradient Descent as a Shrinkage Operator for Spectral Bias Principles of risk minimization for learn- ing theory

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-16T10:30:08.755297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:30:08.755297Z digest=sha256:42e7d3a580400ab1e655b8bda84abb2faa1942a8fd53099eb95b1db6f1e011b3

Observation efac4bde-15b1-4810-b270-3b77ad71aebe · outbound

This paper cites The “fourier” theory of the cardinal func- tion.

Gradient Descent as a Shrinkage Operator for Spectral Bias The “fourier” theory of the cardinal func- tion

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:08.922909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.759536Z digest=sha256:5c3a0ee1e7889639f55ade3f9875171846bf54fe281472ea6582a1fdfd8a8d5a

Observation fcac2a27-0803-4445-96df-3fa10a2f270f · outbound

This paper cites Gradient dynamics of shallow univariate relu networks.

Gradient Descent as a Shrinkage Operator for Spectral Bias Gradient dynamics of shallow univariate relu networks

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:08.906664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.764283Z digest=sha256:5ad25dec6826c1e0db45502364b8407770e2c26143fc665a7732a88c99cdd7b5

Observation 63c8ab11-0ec2-4135-a43c-222cc543b133 · outbound

This paper cites A mathematical analysis of the motion coherence theory.

Gradient Descent as a Shrinkage Operator for Spectral Bias A mathematical analysis of the motion coherence theory

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:08.890784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.768763Z digest=sha256:1ff25c564a7e9b618e52299172fef8489aafc5a8c705ab1289e08718bd4e4a43

Observation 36d36d9d-051b-4600-8722-3909c3b02f65 · outbound

This paper cites Understanding deep learning re- quires rethinking generalization.

Gradient Descent as a Shrinkage Operator for Spectral Bias Understanding deep learning re- quires rethinking generalization

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:08.875029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.773228Z digest=sha256:d2fa365ce4ea88f1cc5d9c5954e5e3cb574e5586bb2190d6956f1d977155eb82

Observation f6daf10a-7a06-43a9-9a18-0245d2be46de · outbound

This paper cites Rethinking Positional Encoding.

Gradient Descent as a Shrinkage Operator for Spectral Bias Rethinking Positional Encoding

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-16T10:30:08.777697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T10:30:08.777697Z digest=sha256:4d52ae9be557dddffc05461e61e911108f2067fca92502aa61abfe4e43afc904

Observation 5595c28e-ea9a-4c7d-aec7-ee9ae904f157 · outbound

This paper cites Trading positional complexity vs deepness in coordinate networks.

Gradient Descent as a Shrinkage Operator for Spectral Bias Trading positional complexity vs deepness in coordinate networks

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T10:30:08.857993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T10:30:08.782584Z digest=sha256:9382d4bac3020b1821f740dea983bee14134f06d011d2d2e40eeab9c10b97fa9

Pith citing papers

No inbound Pith citation observations are available.