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Paper Citation Record · LEDGER

CodeJeNN: A simple C++ neural network generator for physics applications

As of 14 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2607.02746.

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

pith.paper-citation-record.v1
2607.02746 v2

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T02:02:19.485565Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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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  • verified fuzzy0
  • unresolved32
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2977adb8-1d45-4c1a-82b3-ad85bf37a01c · outbound

This paper cites Learning incomplete factorization preconditioners for GMRES.

CodeJeNN: A simple C++ neural network generator for physics applications Learning incomplete factorization preconditioners for GMRES

Reference 1

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source=pdf_text observed=2026-08-04T02:02:17.719310Z digest=sha256:accbc2d0d1ca83a44db22e2d91ae5ff2fba3ecc598822b45d9e26459cdf8c07c

Observation 29129cf9-5c17-4430-9980-6157d2cf218d · outbound

This paper cites an unresolved cited work.

CodeJeNN: A simple C++ neural network generator for physics applications Unresolved cited work

Reference 2

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source=pdf_text observed=2026-08-04T02:02:17.722757Z digest=sha256:eea6dced29f469dccad4e56e02370743ef654b9277715af665b54a52b4f55c9d

Observation f9ed7afb-74ab-4abb-9d26-258cde71d0c8 · outbound

This paper cites an unresolved cited work.

CodeJeNN: A simple C++ neural network generator for physics applications Unresolved cited work

Reference 3

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source=pdf_text observed=2026-08-04T02:02:17.725120Z digest=sha256:4011e1c49b6b157ea10a85a079dc99e6c44d2e4226d98957dacef2e85ba5571a

Observation ffbc9f38-48b7-4961-8755-b28fd13df6de · outbound

This paper cites Raissi, P.

CodeJeNN: A simple C++ neural network generator for physics applications Raissi, P

Reference 4

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source=pdf_text observed=2026-08-04T02:02:17.727658Z digest=sha256:dd6bccb22f9addfdba5367aebf34d67bfa0b86382a53b60c2098f1c853e60434

Observation 12da4445-9294-44ac-bf60-7fd86abbd19c · outbound

This paper cites an unresolved cited work.

CodeJeNN: A simple C++ neural network generator for physics applications Unresolved cited work

Reference 5

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source=pdf_text observed=2026-08-04T02:02:17.730159Z digest=sha256:d283e077d133e4b8ede8f9f1d36dd68478c8aff988f2497b59e73a2ebeea9f4a

Observation f77ee2a6-033a-43c9-bc86-c1858ef53052 · outbound

This paper cites an unresolved cited work.

CodeJeNN: A simple C++ neural network generator for physics applications Unresolved cited work

Reference 6

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source=pdf_text observed=2026-08-04T02:02:17.732437Z digest=sha256:1525f1ba9075ecd03f82cb544a6da62e61f2c0639fb22d0f704927ba0a43be08

Observation cbbbf822-ae7e-415c-ad2e-54d4fe6569a9 · outbound

This paper cites Vinuesa, S.

CodeJeNN: A simple C++ neural network generator for physics applications Vinuesa, S

Reference 7

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source=pdf_text observed=2026-08-04T02:02:17.734778Z digest=sha256:28d8c7de4a98fc54e84f1587929f360a8682f1f9bd1f2261c31edaf70bf7d1b9

Observation be5b0978-1ae3-4355-8f4a-4d4993a16c52 · outbound

This paper cites an unresolved cited work.

CodeJeNN: A simple C++ neural network generator for physics applications Unresolved cited work

Reference 8

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source=pdf_text observed=2026-08-04T02:02:17.736881Z digest=sha256:84421e70cc6335d18ca5c3451f6069f952ab96a20101359af5618946b1c7875f

Observation 8c18e1a1-6b7b-4855-9c87-b6811c19b504 · outbound

This paper cites Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghe- mawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Y.

CodeJeNN: A simple C++ neural network generator for physics applications Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghe- mawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Y

Reference 9

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Observation 5812f7b2-af81-4a97-86ba-58ccee0ff7f5 · outbound

This paper cites Chollet, others, Keras (2015).

CodeJeNN: A simple C++ neural network generator for physics applications Chollet, others, Keras (2015)

Reference 10

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Observation 4d176d8a-0453-483b-899a-a86530a62b60 · outbound

This paper cites Ansel, E.

CodeJeNN: A simple C++ neural network generator for physics applications Ansel, E

Reference 11

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Observation 39ca70e3-ff3a-4c19-ab6f-1b304d267cfc · outbound

This paper cites an unresolved cited work.

CodeJeNN: A simple C++ neural network generator for physics applications Unresolved cited work

Reference 12

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Observation b5123425-b40d-4ee2-91ca-049676d35373 · outbound

This paper cites an unresolved cited work.

CodeJeNN: A simple C++ neural network generator for physics applications Unresolved cited work

Reference 13

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Observation dee25229-cee6-4498-bdf1-6602a63546d9 · outbound

This paper cites Maruyama, B.

CodeJeNN: A simple C++ neural network generator for physics applications Maruyama, B

Reference 14

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Observation c82eaf80-0f16-4ea0-bb16-d66143af5f94 · outbound

This paper cites an unresolved cited work.

CodeJeNN: A simple C++ neural network generator for physics applications Unresolved cited work

Reference 15

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Observation 308266be-e94c-46e9-ad9e-ed6141fedc13 · outbound

This paper cites an unresolved cited work.

CodeJeNN: A simple C++ neural network generator for physics applications Unresolved cited work

Reference 16

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source=pdf_text observed=2026-08-04T02:02:17.754589Z digest=sha256:9d2a01dc1ece39041364e2d1502cf0e9b37c52b9318800b64dca2f6e3e9574c1

Observation a6d58c2d-132b-4639-954b-491ddf7cddc5 · outbound

This paper cites Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs).

CodeJeNN: A simple C++ neural network generator for physics applications Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

Reference 17

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Observation bd57fbb1-90f1-416c-bf0b-6d634fdb8142 · outbound

This paper cites Searching for Activation Functions.

CodeJeNN: A simple C++ neural network generator for physics applications Searching for Activation Functions

Reference 18

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source=pdf_text observed=2026-08-04T02:02:17.766677Z digest=sha256:13ce15ac5d475a923aa4e426f98e6941f9995cbfb98ccfdb34a977c1892f6d2f

Observation 11702fb2-a5d5-461d-9196-e66e1271b974 · outbound

This paper cites Gaussian Error Linear Units (GELUs).

CodeJeNN: A simple C++ neural network generator for physics applications Gaussian Error Linear Units (GELUs)

Reference 19

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Observation 6db8c6fd-2e2f-4cc6-8411-9acabefdcbe7 · outbound

This paper cites an unresolved cited work.

CodeJeNN: A simple C++ neural network generator for physics applications Unresolved cited work

Reference 20

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Observation 9354fd7c-970f-457d-ba69-44c03fefdbf8 · outbound

This paper cites Dugas, Y.

CodeJeNN: A simple C++ neural network generator for physics applications Dugas, Y

Reference 21

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Observation 72c97c4c-22ea-4692-9d1e-b2f1f894237a · outbound

This paper cites Efficient Object Localization Using Convolutional Networks.

CodeJeNN: A simple C++ neural network generator for physics applications Efficient Object Localization Using Convolutional Networks

Reference 22

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Observation 0a44b5b3-d4a9-4f1f-a8a1-7bfc972eaa2d · outbound

This paper cites A guide to convolution arithmetic for deep learning.

CodeJeNN: A simple C++ neural network generator for physics applications A guide to convolution arithmetic for deep learning

Reference 23

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Observation 9c4a5904-ac7d-4447-9aba-72424b0490a9 · outbound

This paper cites an unresolved cited work.

CodeJeNN: A simple C++ neural network generator for physics applications Unresolved cited work

Reference 24

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Observation 1c5b235f-1189-4645-b5fe-f098bf048b4b · outbound

This paper cites Zhang, Y.

CodeJeNN: A simple C++ neural network generator for physics applications Zhang, Y

Reference 25

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Observation e5e73f99-1d35-49e9-9973-0cd01306da7c · outbound

This paper cites an unresolved cited work.

CodeJeNN: A simple C++ neural network generator for physics applications Unresolved cited work

Reference 26

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Observation 35f48785-a894-4420-99ff-86669b05d2ff · outbound

This paper cites an unresolved cited work.

CodeJeNN: A simple C++ neural network generator for physics applications Unresolved cited work

Reference 27

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Observation 785e56d6-e662-4b44-b981-c3725103bdd4 · outbound

This paper cites Salim, J.

CodeJeNN: A simple C++ neural network generator for physics applications Salim, J

Reference 28

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Observation c7035bfd-489c-4648-a723-deff6dd9e8ed · outbound

This paper cites an unresolved cited work.

CodeJeNN: A simple C++ neural network generator for physics applications Unresolved cited work

Reference 29

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Observation c865039f-bde3-41ae-84c2-498a24d7ebf5 · outbound

This paper cites Graph Neural Networks in TensorFlow and Keras with Spektral.

CodeJeNN: A simple C++ neural network generator for physics applications Graph Neural Networks in TensorFlow and Keras with Spektral

Reference 30

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Observation 696be40c-2539-4a43-99a1-64de71a14a00 · outbound

This paper cites Gilmer, S.

CodeJeNN: A simple C++ neural network generator for physics applications Gilmer, S

Reference 31

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Observation 3148f9d0-246d-430f-bb26-8e6f32b0b176 · outbound

This paper cites Attention-based Graph Neural Network for Semi-supervised Learning.

CodeJeNN: A simple C++ neural network generator for physics applications Attention-based Graph Neural Network for Semi-supervised Learning

Reference 32

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Observation 0ffb6c40-df03-4b24-8799-26337cd240ee · outbound

This paper cites Guennebaud, B.

CodeJeNN: A simple C++ neural network generator for physics applications Guennebaud, B

Reference 33

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Pith citing papers

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