Paper Citation Record · LEDGER

Wide Deep Neural Networks with Gaussian Weights are Very Close to Gaussian Processes

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2312.11737.

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

pith.paper-citation-record.v1
2312.11737 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:05:40.865469Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T12:55:43.875079Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

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 6263c103-2046-477c-a23a-fac1c310b486 · inbound

Proportional infinite-width infinite-depth limit for deep linear neural networks cites this paper.

Proportional infinite-width infinite-depth limit for deep linear neural networks Wide Deep Neural Networks with Gaussian Weights are Very Close to Gaussian Processes

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T15:05:40.865469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 567c484c-d8c8-42f5-916d-85ac7c7ca80c · inbound

Posterior Bayesian Neural Networks with Dependent Weights cites this paper.

Posterior Bayesian Neural Networks with Dependent Weights Wide Deep Neural Networks with Gaussian Weights are Very Close to Gaussian Processes

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-19T02:52:00.178282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 567d6109-06c3-4b68-83cb-5b06c3510553 · inbound

Large deviation principles for convolutional Bayesian neural networks cites this paper.

Large deviation principles for convolutional Bayesian neural networks Wide Deep Neural Networks with Gaussian Weights are Very Close to Gaussian Processes

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-15T14:03:39.869199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97275d32-f9b8-444e-a497-85fd18cf49c4 · inbound

Stochastic Scaling Limits and Synchronization by Noise in Deep Transformer Models cites this paper.

Stochastic Scaling Limits and Synchronization by Noise in Deep Transformer Models Wide Deep Neural Networks with Gaussian Weights are Very Close to Gaussian Processes

Reference 51

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T09:26:26.024596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6495927b-984b-4a36-b39a-7c49ac09322c · inbound

Universality in Deep Neural Networks: An approach via the Lindeberg exchange principle cites this paper.

Universality in Deep Neural Networks: An approach via the Lindeberg exchange principle Wide Deep Neural Networks with Gaussian Weights are Very Close to Gaussian Processes

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T06:20:41.986257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8dfcb8b3-7385-4a30-823a-27d9801f610b · inbound

Bayesian Inference with Shaped Deep Non-linear MLPs cites this paper.

Bayesian Inference with Shaped Deep Non-linear MLPs Wide Deep Neural Networks with Gaussian Weights are Very Close to Gaussian Processes

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T20:42:37.077603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 97a7916b-3d98-414f-99a7-f911e9ba44fa · inbound

Geometric Dyson Brownian Motions and the Free Log-Normal Limit for a Non-Square Gaussian Matrix Product cites this paper.

Geometric Dyson Brownian Motions and the Free Log-Normal Limit for a Non-Square Gaussian Matrix Product Wide Deep Neural Networks with Gaussian Weights are Very Close to Gaussian Processes

Reference 34

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T12:55:43.877640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8ccbdf2f-022e-463f-867c-901bddb7c94e · inbound

Geometric Dyson Brownian Motions and the Free Log-Normal Limit for a Non-Square Gaussian Matrix Product cites this paper.

Geometric Dyson Brownian Motions and the Free Log-Normal Limit for a Non-Square Gaussian Matrix Product Wide Deep Neural Networks with Gaussian Weights are Very Close to Gaussian Processes

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-02T09:36:51.067321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.