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Paper Citation Record · LEDGER

Fixed point actions from convolutional neural networks

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2311.17816.

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

pith.paper-citation-record.v1
2311.17816 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T05:15:04.336409Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-30T23:45:07.434541Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b614e476-becf-4f26-8795-663a1410c43f · inbound

HMC and gradient flow with machine-learned classically perfect fixed-point actions cites this paper.

HMC and gradient flow with machine-learned classically perfect fixed-point actions Fixed point actions from convolutional neural networks

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-09T05:15:04.336409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T05:15:04.336409Z digest=sha256:25e7fe9b736f454c726fa40d86ca5e28cdd1a30d78007a34500e72a1b315adc4

Observation 93633a37-5717-48ad-868e-daa1de6fbd28 · inbound

Machine learning for four-dimensional SU(3) lattice gauge theories cites this paper.

Machine learning for four-dimensional SU(3) lattice gauge theories Fixed point actions from convolutional neural networks

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-05-10T14:25:29.551539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T14:24:26.571044Z digest=sha256:5ea4d3cc2eb73fdd8c2aef446b00eac045f3781f45b196be492867fb808abb73

Observation 93fbf57c-8c75-4d86-b68e-8d1fc479e2b4 · inbound

Lattice fermion formulation via Physics-Informed Neural Networks: Ginsparg-Wilson relation and Overlap fermions cites this paper.

Lattice fermion formulation via Physics-Informed Neural Networks: Ginsparg-Wilson relation and Overlap fermions Fixed point actions from convolutional neural networks

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-11T22:01:11.517817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T03:38:47.436408Z digest=sha256:fe4dc2afb8d207e3480ba635cdb87a12b96970c8e45322d457b45c2929f5fec2

Observation d809bd32-fd06-4437-8252-4ef1f0fa7af7 · inbound

Lattice fermion formulation via Physics-Informed Neural Networks: Ginsparg-Wilson relation and Overlap fermions cites this paper.

Lattice fermion formulation via Physics-Informed Neural Networks: Ginsparg-Wilson relation and Overlap fermions Fixed point actions from convolutional neural networks

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-05-14T22:08:04.061189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-14T22:07:31.058363Z digest=sha256:35ec748a5aad96060507f79507ba8a01be86f3d82ebfc17c43153ac0536965f2

Observation 092909f9-8acc-4645-90c3-4f507eafd9ec · inbound

Lattice fermion formulation via Physics-Informed Neural Networks: Ginsparg-Wilson relation and Overlap fermions cites this paper.

Lattice fermion formulation via Physics-Informed Neural Networks: Ginsparg-Wilson relation and Overlap fermions Fixed point actions from convolutional neural networks

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-06-30T23:45:07.436895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-30T23:44:52.009361Z digest=sha256:5379863ed85dac0599bdad0aecfcba44fa7cf3ab5cbeb27425f36ec9f98bfaf3