Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Applications of Statistical Field Theory in Deep Learning
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2502.18553.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T23:04:30.429284Z
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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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.