Pith. sign in

Paper Citation Record · LEDGER

A ZeNN architecture to avoid the Gaussian trap

As of 20 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2505.20553.

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

pith.paper-citation-record.v1
2505.20553 v2

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:57:42.821519Z

measured 33 of 33 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

33 of 33 outbound references displayed

  • verified exact2
  • verified fuzzy15
  • unresolved16
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 24ff2c9b-4e4b-4e75-b2f0-235ee3b09baa · outbound

This paper cites Why bigger is not always better: on finite and infinite neural networks.

A ZeNN architecture to avoid the Gaussian trap Why bigger is not always better: on finite and infinite neural networks

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:57:45.681790Z

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.

source=pdf_text observed=2026-08-07T13:57:41.163550Z digest=sha256:add79eb27fe7b6b46aeb13a937f3e194b41870362c1f52c7c079d2a9a4ebea62

Observation bbea2125-384a-49d4-adde-81d5c6e159dd · outbound

This paper cites Arora, S.

A ZeNN architecture to avoid the Gaussian trap Arora, S

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:57:45.571933Z

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.

source=pdf_text observed=2026-08-07T13:57:41.266661Z digest=sha256:cd854ae344c3c94776d9e4276a2dd2c03ccf9bfb6dcbf90a6130d5f78f88ecb3

Observation 8b7fe50e-6ee7-4b1e-9c47-121ff2c5b3eb · outbound

This paper cites Arora, S.

A ZeNN architecture to avoid the Gaussian trap Arora, S

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:57:45.484215Z

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.

source=pdf_text observed=2026-08-07T13:57:41.315139Z digest=sha256:ccf92f5805b7abd9d8c2b21c776e64324dc75023144fb2a3deef430cbbb68d3f

Observation 449185db-ac7c-4eb3-a641-7ac844677583 · outbound

This paper cites Neural tangent kernel at initialization: linear width suffices.

A ZeNN architecture to avoid the Gaussian trap Neural tangent kernel at initialization: linear width suffices

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:57:45.432092Z

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.

source=pdf_text observed=2026-08-07T13:57:41.391733Z digest=sha256:e693668d8f8566be3469d4ee495d5124c97bf39ffbd4e0edfe07d8b524d05203

Observation 7737afdf-6f53-4986-9e09-9091802e759d · outbound

This paper cites Towards understanding the spectral bias of deep learning.

A ZeNN architecture to avoid the Gaussian trap Towards understanding the spectral bias of deep learning

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T13:57:41.454828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:57:41.454828Z digest=sha256:5243794deec570e477b6254179b49bfb10fa12994b18183fb8aa5436aeb57167

Observation 46916288-ba21-4ea6-bd94-19ed3c2882ac · outbound

This paper cites Wide neural networks: From non-gaussian random fields at initialization to the NTK geometry of training.

A ZeNN architecture to avoid the Gaussian trap Wide neural networks: From non-gaussian random fields at initialization to the NTK geometry of training

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-08-07T13:57:43.538497Z

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.

source=pdf_text observed=2026-08-07T13:57:41.505309Z digest=sha256:a723a62878ff31f12e94377dbbd2a264b68fbd49ae604160b543a3da8691774e

Observation 6012550a-c592-413b-8cab-b4237c434d2b · outbound

This paper cites Jena climate dataset.

A ZeNN architecture to avoid the Gaussian trap Jena climate dataset

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:57:45.382361Z

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.

source=pdf_text observed=2026-08-07T13:57:41.544182Z digest=sha256:9b9bd688c22f88272a99ba1fe7f6a81b6a10a454cf696b150a95e4014256998d

Observation f82467e2-6b82-46e7-9900-6761fe0fbcb4 · outbound

This paper cites Mathematical aspects of deep learning.

A ZeNN architecture to avoid the Gaussian trap Mathematical aspects of deep learning

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:57:45.236729Z

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.

source=pdf_text observed=2026-08-07T13:57:41.580797Z digest=sha256:cbd54a966cb8e1ebbb05806b67e3eda910105b561afbe4f225549374448ddd32

Observation 014fb4ab-672b-4fc3-a1a1-aaec2249db86 · outbound

This paper cites Approximation capabilities of multilayer feedforward networks.

A ZeNN architecture to avoid the Gaussian trap Approximation capabilities of multilayer feedforward networks

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T13:57:41.620681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:57:41.620681Z digest=sha256:96562f4b761f85590a11d3333ca8232ef5f1d8e9f91d1cf5d70ee8d3773fd13c

Observation d477264d-49c3-4f70-8c6e-f400510c4d18 · outbound

This paper cites Jacot, G.

A ZeNN architecture to avoid the Gaussian trap Jacot, G

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:57:45.126749Z

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.

source=pdf_text observed=2026-08-07T13:57:41.628407Z digest=sha256:f021a4dc286bf9decd236c8e6dc3c2dade32f4dafb12cda2a8ee27667bd0ef52

Observation b37e49f5-6555-4551-8d3b-6e99de032b89 · outbound

This paper cites Unsupervised Neural Networks for Quantum Eigenvalue Problems.

A ZeNN architecture to avoid the Gaussian trap Unsupervised Neural Networks for Quantum Eigenvalue Problems

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T13:57:41.640698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:57:41.640698Z digest=sha256:c92f4c0b5a5aa1f29db236d436379be34f7ef7a31f88fa12fdbf08dd4e30184c

Observation a7c4f79e-ff0c-4829-ac3f-9b79ffa3b5d6 · outbound

This paper cites Physics-Informed Neural Networks for Quantum Eigenvalue Problems.

A ZeNN architecture to avoid the Gaussian trap Physics-Informed Neural Networks for Quantum Eigenvalue Problems

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T13:57:41.653305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:57:41.653305Z digest=sha256:7c3e6aec1759675c0032fc6ed4921a5e0d8604b376a8246b6c35d6f07c9297ce

Observation f97ab870-ac03-4a9d-8adb-aa05cc07d561 · outbound

This paper cites Generalization ability of wide neural networks on R.

A ZeNN architecture to avoid the Gaussian trap Generalization ability of wide neural networks on R

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:57:44.998039Z

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.

source=pdf_text observed=2026-08-07T13:57:41.675048Z digest=sha256:ffebd530cca63779cc146babdcb1d8cf4cc7f19ddf9bc4f47d6a6e817de5f482

Observation 67c89f51-05fc-4dbd-94d6-7974eec76ecc · outbound

This paper cites an unresolved cited work.

A ZeNN architecture to avoid the Gaussian trap Unresolved cited work

Reference 14

Resolution
unresolved
raw_fallback, observed 2026-08-07T13:57:44.859141Z

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.

source=pdf_text observed=2026-08-07T13:57:41.736115Z digest=sha256:fefd2f0d6b3990d2ba5067d55f165386709433d4ad787ce18295c2fd415abd75

Observation 3f68e172-3596-42df-a7ee-675bbd92a42e · outbound

This paper cites Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, and Jascha Sohl-Dickstein.

A ZeNN architecture to avoid the Gaussian trap Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, and Jascha Sohl-Dickstein

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:57:44.723390Z

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.

source=pdf_text observed=2026-08-07T13:57:41.763078Z digest=sha256:1dd4a919a082defaf94d38e041e4bd55c8bfeabb6bbe235f4fe5648c2ba99427

Observation dd096a38-4fb7-4db6-bb74-4d53a251f60e · outbound

This paper cites Lin, Allan Pinkus, and Shimon Schocken.

A ZeNN architecture to avoid the Gaussian trap Lin, Allan Pinkus, and Shimon Schocken

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T13:57:41.795555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:57:41.795555Z digest=sha256:8e0bbea17c70b2eb43eaf2206a0b274e6d6b5a903b278c3ac5b60e1befde61f1

Observation a2147cd3-5d77-4738-9c8b-9a8ee631710e · outbound

This paper cites Statistical optimality of deep wide neural networks.

A ZeNN architecture to avoid the Gaussian trap Statistical optimality of deep wide neural networks

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:57:44.599000Z

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.

source=pdf_text observed=2026-08-07T13:57:41.832513Z digest=sha256:07a4b0835e7e98f69694f6ccec2f7ea7b2bca3650c2f92a0accb255fd1adbbaf

Observation 2bb43f8f-bbc9-4a14-85f4-e40eba91cfdb · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

A ZeNN architecture to avoid the Gaussian trap KAN: Kolmogorov-Arnold Networks

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T13:57:41.945208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:57:41.945208Z digest=sha256:6b2a21f46e08cddf71d7f4d2a756fe2242123cac0adf8ed07575ed63bde11175

Observation 0af213b2-f87e-4cdc-8baf-998033f198c1 · outbound

This paper cites Multi-scale deep neural network (mscalednn) for solving poisson-boltzmann equation in complex domains.

A ZeNN architecture to avoid the Gaussian trap Multi-scale deep neural network (mscalednn) for solving poisson-boltzmann equation in complex domains

Reference 19

Resolution
verified exact
doi, observed 2026-08-07T13:57:42.942807Z

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.

source=pdf_text observed=2026-08-07T13:57:42.007314Z digest=sha256:20989135af4469e3ce111fc4f279f4a2d11e91274d41c7eb64d1634009f48246

Observation 6e59a18e-70ca-49cb-bcea-a2ddfdb37f0f · outbound

This paper cites On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains.

A ZeNN architecture to avoid the Gaussian trap On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T13:57:41.872473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:57:41.872473Z digest=sha256:e66333dab4c71f6ab7482daa64feff4f1bf27b7f6462776ef372082b7c91fcbc

Observation 59e23fe7-637d-4174-af4d-28c0f47be6a0 · outbound

This paper cites an unresolved cited work.

A ZeNN architecture to avoid the Gaussian trap Unresolved cited work

Reference 21

Resolution
unresolved
raw_fallback, observed 2026-08-07T13:57:44.360314Z

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.

source=pdf_text observed=2026-08-07T13:57:42.128581Z digest=sha256:b9d0c9b1ce5e1807f696d2a0b80a9f08af3b9a808edfe627b02651e7b1d6947e

Observation 424d6467-4f2c-4a5c-ba12-2fac5d17d3a7 · outbound

This paper cites On the spectral bias of neural networks.

A ZeNN architecture to avoid the Gaussian trap On the spectral bias of neural networks

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:57:44.208100Z

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.

source=pdf_text observed=2026-08-07T13:57:42.180827Z digest=sha256:f8efba477a032ef8666adaf8a6e73887e98a4addd83b9642a6a6af854c7e2790

Observation d6402c6d-b7f4-4711-a39f-f18ca3e564bd · outbound

This paper cites The limitations of large width in neural networks: A deep gaussian process perspective.

A ZeNN architecture to avoid the Gaussian trap The limitations of large width in neural networks: A deep gaussian process perspective

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:57:44.469150Z

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.

source=pdf_text observed=2026-08-07T13:57:42.074081Z digest=sha256:2224bbcc73c417cd953330d7d1846e9240afe1e6b538157b18e804f235427a3f

Observation 4dbf21d4-cd6a-49c4-a885-a0d51ae568b7 · outbound

This paper cites Martel, Alexander W.

A ZeNN architecture to avoid the Gaussian trap Martel, Alexander W

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:57:43.938054Z

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.

source=pdf_text observed=2026-08-07T13:57:42.387722Z digest=sha256:24c7230129a3307fb86e34b87f51c5566137739c1498330339633787060e21cc

Observation 0da6d759-4c58-40f1-9961-02eaddd04cf0 · outbound

This paper cites fourier-feature-networks.

A ZeNN architecture to avoid the Gaussian trap fourier-feature-networks

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:57:43.795111Z

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.

source=pdf_text observed=2026-08-07T13:57:42.460277Z digest=sha256:02f5be7b01964b63494a575950064dd8a1c7d8aad41eb1e06d01779d82d22c50

Observation 4a50c8fe-85ca-4bba-becc-34b3e766de29 · outbound

This paper cites an unresolved cited work.

A ZeNN architecture to avoid the Gaussian trap Unresolved cited work

Reference 26

Resolution
unresolved
raw_fallback, observed 2026-08-07T13:57:44.089531Z

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.

source=pdf_text observed=2026-08-07T13:57:42.279662Z digest=sha256:248e807f6e9b866c9e24245362b8a12ecef9bbff8d16ed16db0544858a6e6334

Observation 9d6a6772-cdbb-42bf-aa0e-061bbce7cca3 · outbound

This paper cites On the eigenvector bias of fourier feature net- works: From regression to solving multi-scale pdes with physics-informed neural networks.

A ZeNN architecture to avoid the Gaussian trap On the eigenvector bias of fourier feature net- works: From regression to solving multi-scale pdes with physics-informed neural networks

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T13:57:42.557997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:57:42.557997Z digest=sha256:2ea7e5ceabb100473bb43c4c60d6ab49c2108e088f7e5177bdc6c7c9e47b7f95

Observation 41335b8f-584e-467f-ac71-528bd2e812ab · outbound

This paper cites Fourier features let networks learn high frequency functions in low dimensional domains.

A ZeNN architecture to avoid the Gaussian trap Fourier features let networks learn high frequency functions in low dimensional domains

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T13:57:42.512995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:57:42.512995Z digest=sha256:ffe708e8292a7beff97ba105423df3c64ba530532be7c7eed0478c99aa14e554

Observation cfb6856f-6604-481a-8abb-1ba2473e29a7 · outbound

This paper cites Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation.

A ZeNN architecture to avoid the Gaussian trap Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-07T13:57:42.783867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:57:42.783867Z digest=sha256:2ef9dabf797af6c52237b580a6e4c15536a7465aabbfd27c5657876ba409dcb1

Observation c0a210b3-d598-4137-9a34-03e5d45ad7b5 · outbound

This paper cites An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks.

A ZeNN architecture to avoid the Gaussian trap An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T13:57:42.821519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:57:42.821519Z digest=sha256:ff10f96b47e56ba6712f46ee73a02087cd9b6854e473dda5786e69f11e73a016

Observation de43c385-8f76-459f-9082-0ad5bbd49581 · outbound

This paper cites Yang and E.

A ZeNN architecture to avoid the Gaussian trap Yang and E

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:57:43.682889Z

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.

source=pdf_text observed=2026-08-07T13:57:42.741666Z digest=sha256:258be3cbc3572d059d2f3558f67b91beeb0d1687e9d4c6a01a878f254248a1c0

Observation ba2a9b05-ceeb-4398-9dbe-34f6ff41977d · outbound

This paper cites an unresolved cited work.

A ZeNN architecture to avoid the Gaussian trap Unresolved cited work

Reference 2020

Resolution
unresolved
raw_fallback, observed 2026-08-07T13:57:45.637095Z

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.

source=pdf_text observed=2026-08-07T13:57:41.205868Z digest=sha256:29149c2a41435a3a224ee32777a3009379f88215d6ce2b505ca3b04f40cd429b

Observation 2031aa42-4661-40d5-a54d-274f5dc7f218 · outbound

This paper cites Generalization Ability of Wide Neural Networks on $\mathbb{R}$.

A ZeNN architecture to avoid the Gaussian trap Generalization Ability of Wide Neural Networks on $\mathbb{R}$

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-07T13:57:41.705012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:57:41.705012Z digest=sha256:0023e9296c52570d4db80f9b5a197fbb608237615a663d9814eaca4be8353525

Pith citing papers

No inbound Pith citation observations are available.