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Paper Citation Record · LEDGER

To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2304.09355.

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

pith.paper-citation-record.v1
2304.09355 v5

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 2026-08-21T06:32:19.484+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:04:33.323479Z

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

7
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 8bc7dc0f-5713-488e-97aa-023518d0b04b · inbound

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation cites this paper.

Continual Deep Reinforcement Learning with Task-Agnostic Policy Distillation To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-12T13:04:33.323479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:04:33.323479Z digest=sha256:edd0df54521ec531b45c319176baec41c23335fbde5dd76d7963fb28de6e0fb2

Observation 65cbdc03-e893-4c8b-a156-e88ff104c008 · inbound

Enhancing Content Representation for AR Image Quality Assessment Using Knowledge Distillation cites this paper.

Enhancing Content Representation for AR Image Quality Assessment Using Knowledge Distillation To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-11T20:10:31.486317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:10:31.486317Z digest=sha256:d10547d10f3e921406f589586774459aa9402a15e078f7bd9dc685aaab0a7d7f

Observation 149759fb-d48b-49b6-bed2-754d3c6aa7ff · inbound

Information-Maximized Soft Variable Discretization for Self-Supervised Image Representation Learning cites this paper.

Information-Maximized Soft Variable Discretization for Self-Supervised Image Representation Learning To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-10T21:58:35.567236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:58:35.567236Z digest=sha256:14b43814e1a0f89c0fd36c303d9ff268ab0b7397281e1831e8f271e05f4bc3e1

Observation cec42c8e-d439-4c0f-9d02-a081bfd9b2fc · inbound

Employing Discrete Fourier Transform in Representational Learning cites this paper.

Employing Discrete Fourier Transform in Representational Learning To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-07T05:55:36.376105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:55:36.376105Z digest=sha256:d6ff3e6b17ddc779d59cf1505799ba1bf4c8d7a295207df2d718d76849b3516c

Observation 89613616-14e0-4745-ba5f-fbf5559b1983 · inbound

Energy-Efficient Information Representation in MNIST Classification Using Biologically Inspired Learning cites this paper.

Energy-Efficient Information Representation in MNIST Classification Using Biologically Inspired Learning To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-02T19:56:55.673312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T19:56:55.673312Z digest=sha256:b98bdfe4862b77f1266caea236106e06c4ac34ca61f124a658360b6bfe1df89a

Observation efe7a664-776d-4931-b399-ea0968fdd6b7 · inbound

Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces cites this paper.

Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review

Reference 72

Resolution
verified exact
arxiv_id, observed 2026-05-14T20:17:54.670230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-14T20:17:01.224864Z digest=sha256:3d7b5bbb16c2cdcae311b3df9e4566b0e395023a604ec10e50295dc5b1501b64

Observation 845121ed-1ab9-45e6-b99f-6417a6fd67b2 · inbound

SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning cites this paper.

SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review

Reference 174

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T09:59:45.012250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-06-26T09:19:50.623741Z digest=sha256:4429f7607e6d0d158d3962ef5dc36bde17890898a7594cc538d80e1dfccced10

Observation 2b8d83aa-3190-421d-8e93-3f7d939b49c6 · inbound

SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning cites this paper.

SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review

Reference 173

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T18:55:59.697344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-06-29T01:18:19.195007Z digest=sha256:ba7e2d84e40b791ee8e395e337204f6ba1052aed9b077ca4fc98bdb48dd27f38