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Paper Citation Record · LEDGER

Entropic bounds for conditionally Gaussian vectors and applications to neural networks

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2504.08335.

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

pith.paper-citation-record.v1
2504.08335 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T09:36:51.257372Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T12:55:43.893964Z

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 18718208-0790-422b-b97d-938af9a43706 · inbound

Posterior Bayesian Neural Networks with Dependent Weights cites this paper.

Posterior Bayesian Neural Networks with Dependent Weights Entropic bounds for conditionally Gaussian vectors and applications to neural networks

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-19T02:52:00.153697Z

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.

source=pdf_text observed=2026-05-19T02:48:46.380966Z digest=sha256:8ad1bdc1cb5d6d00043346d4761097722edfff1f106a7877680355db7a87bf1d

Observation 345f900a-8ccf-4e48-b3ae-410743b43f37 · inbound

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

Large deviation principles for convolutional Bayesian neural networks Entropic bounds for conditionally Gaussian vectors and applications to neural networks

Reference 6

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-15T14:03:39.869199Z digest=sha256:5060afc2dbcadd857518d3c27358a31ecf0294e405243eaff68e0f8960c1094d

Observation a1143789-2b76-4338-8fd6-e554ff5ec34c · inbound

Phase Transitions in the Fluctuations of Functionals of Random Neural Networks cites this paper.

Phase Transitions in the Fluctuations of Functionals of Random Neural Networks Entropic bounds for conditionally Gaussian vectors and applications to neural networks

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-11T13:31:05.179075Z

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.

source=pdf_text observed=2026-05-10T01:32:12.613421Z digest=sha256:7768ca3a2d7f1abe9379da5bdef1d2adce7dd3c21fc0d3a094b589a2888c3210

Observation 004ea723-1414-45a9-afe4-03b119f50e12 · 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 Entropic bounds for conditionally Gaussian vectors and applications to neural networks

Reference 10

Resolution
metadata mismatch
arxiv_id, observed 2026-05-09T06:20:41.998730Z

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.

source=arxiv_source observed=2026-05-08T18:33:50.664857Z digest=sha256:b9794a3578712799eb0ed3b3fad30ca25dbcd8e2f26ed1b66b24bf5dc8610091

Observation a0cb5583-180b-442c-bf1a-d32dafbc4e5a · inbound

Optimal Non-Asymptotic Edgeworth Expansions for Multivariate Neural Network Outputs cites this paper.

Optimal Non-Asymptotic Edgeworth Expansions for Multivariate Neural Network Outputs Entropic bounds for conditionally Gaussian vectors and applications to neural networks

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-06-30T15:34:48.736060Z

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.

source=pdf_text observed=2026-06-30T15:16:43.188809Z digest=sha256:6addc1aa56a44d2d91cdd190d1efc6420c08566f946c96f037d5e99cab22d915

Observation 5f56065a-d2cc-4fd0-a586-f2d48d29b717 · 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 Entropic bounds for conditionally Gaussian vectors and applications to neural networks

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-07-01T12:55:43.895808Z

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.

source=pdf_text observed=2026-07-01T01:42:14.145227Z digest=sha256:ef6395b82fa9054680232955d3e0b0640b43057de4ce0a9c4203aa9651c211cf

Observation 096e2e5b-c3ef-43e0-b6a5-fec12e210186 · 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 Entropic bounds for conditionally Gaussian vectors and applications to neural networks

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-02T09:36:51.257372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T09:36:51.257372Z digest=sha256:52b81e169bfc292429aacb73e3644c5cf8f4f85f53da047897cee5eda954e0d9