Paper Citation Record · LEDGER

Applications of Statistical Field Theory in Deep Learning

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2502.18553.

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

pith.paper-citation-record.v1
2502.18553 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:39:45.574183Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9dae2fe6-1e2e-4d92-9ac9-2bd67b76e801 · inbound

Microscopic and collective signatures of feature learning in neural networks cites this paper.

Microscopic and collective signatures of feature learning in neural networks Applications of Statistical Field Theory in Deep Learning

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T14:46:58.475489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2664a31e-bcb9-4550-ae43-e572b679ee08 · inbound

Statistical physics of deep learning: Optimal learning of a multi-layer perceptron near interpolation cites this paper.

Statistical physics of deep learning: Optimal learning of a multi-layer perceptron near interpolation Applications of Statistical Field Theory in Deep Learning

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-04T07:44:12.976899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d17d07f-b42c-4c3b-9670-699ea166767a · inbound

Bulk-boundary decomposition of neural networks cites this paper.

Bulk-boundary decomposition of neural networks Applications of Statistical Field Theory in Deep Learning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T00:22:01.166987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6897709-130d-4de0-8ac5-b9c9d1601847 · inbound

There Will Be a Scientific Theory of Deep Learning cites this paper.

There Will Be a Scientific Theory of Deep Learning Applications of Statistical Field Theory in Deep Learning

Reference 278

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:21:09.261247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c313700f-6815-4f75-901f-2bd4c896a11b · inbound

Competing nonlinearities, criticality, and order-to-chaos transition in deep networks cites this paper.

Competing nonlinearities, criticality, and order-to-chaos transition in deep networks Applications of Statistical Field Theory in Deep Learning

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:26:12.247356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 20ee6ce4-8758-42ee-a94f-734fd25a8fdf · inbound

Discrete signaling mediates chaotic regularization in recurrent neural networks cites this paper.

Discrete signaling mediates chaotic regularization in recurrent neural networks Applications of Statistical Field Theory in Deep Learning

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:26:54.931354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation addc3294-64c8-41dd-bf34-53af24a2e68d · inbound

Phase Transitions in Attention: A Bayesian Theory of Copy Head Emergence cites this paper.

Phase Transitions in Attention: A Bayesian Theory of Copy Head Emergence Applications of Statistical Field Theory in Deep Learning

Reference 43

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T13:18:13.103001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 34610e01-cb36-44f7-a422-29141a1b226d · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Applications of Statistical Field Theory in Deep Learning

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-26T15:39:33.129525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7fa9af1e-abe8-4cb8-b1bb-2d5d9f75018d · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Applications of Statistical Field Theory in Deep Learning

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-02T21:57:25.314965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2278f015-a4b2-4215-9362-b8ed3001b833 · inbound

Machine Learning is Good for Physics - and Vice Versa cites this paper.

Machine Learning is Good for Physics - and Vice Versa Applications of Statistical Field Theory in Deep Learning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T23:04:30.429284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e9414df-a6e2-4634-a117-ad9976ddcd57 · inbound

Cascading Through the Hierarchy: Regularizer-Induced Feature Detection as Phase Transitions in Deep Linear Neural Networks cites this paper.

Cascading Through the Hierarchy: Regularizer-Induced Feature Detection as Phase Transitions in Deep Linear Neural Networks Applications of Statistical Field Theory in Deep Learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T14:39:45.574183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.