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Paper Citation Record · LEDGER

Dimensionality compression and expansion in Deep Neural Networks

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1906.00443.

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

pith.paper-citation-record.v1
1906.00443 v3

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 2026-08-23T06:30:58.430688+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:18:11.253704Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T14:08:21.603819Z

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 2d7548dc-c47e-4fba-bbe3-a62046c54343 · inbound

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection cites this paper.

Optimizing Latent Dimension Allocation in Hierarchical VAEs: Balancing Attenuation and Information Retention for OOD Detection Dimensionality compression and expansion in Deep Neural Networks

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-07T04:42:37.758755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:42:37.758755Z digest=sha256:6e6945850fbcd0decd76081bb18eaac57681f55144e3ccca33aeb9a796bc7189

Observation e150d8a7-1899-48ba-a430-82005e5b429a · inbound

Intermediate Representations are Strong AI-Generated Image Detectors cites this paper.

Intermediate Representations are Strong AI-Generated Image Detectors Dimensionality compression and expansion in Deep Neural Networks

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-05-11T17:56:06.715675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-08T16:55:44.000342Z digest=sha256:748639993bd88aceac5f1db02a9152733184355f5abb1be985d75308d3ddd65a

Observation 0206c378-2070-4e78-aa15-38a73e63a5b5 · inbound

The Stable Recovery Manifold: Geometric Principles Governing Recoverability in Continual Learning cites this paper.

The Stable Recovery Manifold: Geometric Principles Governing Recoverability in Continual Learning Dimensionality compression and expansion in Deep Neural Networks

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-07-03T14:08:21.605593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-06-27T07:15:03.905388Z digest=sha256:7c672a0f108b55d021e4118e3d5967e136fa658a31a6bc39f646c3bdab29b450

Observation b223fd15-503e-48a4-a662-d3a995e29383 · inbound

Formalizing Latent Thoughts: Four Axioms of Thought Representation in LLMs cites this paper.

Formalizing Latent Thoughts: Four Axioms of Thought Representation in LLMs Dimensionality compression and expansion in Deep Neural Networks

Reference 63

Resolution
verified exact
arxiv_id, observed 2026-07-01T13:15:45.602112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-06-30T23:50:48.584391Z digest=sha256:09d545533b15044f985c040445fe67adc2c4dca841185494d879f3185213c777

Observation 4a91d622-0a59-4cd3-b96d-52925bb96075 · inbound

How to Tame Grokking: Representation Geometry as a Control Signal cites this paper.

How to Tame Grokking: Representation Geometry as a Control Signal Dimensionality compression and expansion in Deep Neural Networks

Reference 23

Resolution
unresolved
no resolver link, observed 2026-07-14T04:01:12.361370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T04:01:12.361370Z digest=sha256:5ecd8cfb88f58ea25630e137fc604b76cc8a27c8c3daddbebe5097183c6e4a37

Observation d4ec326a-b65f-4eae-aba4-ff127f03c4ab · inbound

Complexity and Stability of Neural Activity Across Aging and Neurodegenerative Disease cites this paper.

Complexity and Stability of Neural Activity Across Aging and Neurodegenerative Disease Dimensionality compression and expansion in Deep Neural Networks

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-07T21:49:05.467916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T21:49:05.467916Z digest=sha256:dc585c29825deb25cda38ef8caf1f6747125155b9ca0e556cd855b3b51190757

Observation 9450ab03-c749-4a35-932c-9caf397d7a9f · inbound

A Convolutional Layer Activation Dimensionality Reduction for Out-of-Distribution and Adversarial Attack Detection Methods cites this paper.

A Convolutional Layer Activation Dimensionality Reduction for Out-of-Distribution and Adversarial Attack Detection Methods Dimensionality compression and expansion in Deep Neural Networks

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-14T04:18:11.253704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:18:11.253704Z digest=sha256:dceccbdcbbe858d6e53c9da651c2d54ce84f23782e976b865afffdb9cb2865c1