Pith. sign in

Paper Citation Record · LEDGER

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

As of 14 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 2026-08-13T06:32:02.005865+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

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:f2fef6b3dba501d9d561738799cbc362e96db0c2af581c425933835f1053c7ad

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=sha256:4c96339f798a1667532182c31f1f0cb5fe1770053b6169cadeb81b0eba1663e5

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=sha256:4f386ac884467a027f3102420788b5635acd71b13ce3a262674fb8e5a5e83962

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:b7cfd1a6b17dfda3f4aba86bc4299eb2f9aaef10d19659507ba6928ea66556d2

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:bb41f010cabdfcaa22f7ae8a928d610efc748990897ffbed85dd181abb3611ad

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 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-05-14T20:17:01.224864Z digest=sha256:3b7f0618c3f8daa23357a55b934602480cd04c3283bd709a0d61539a25c46aaf

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 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-06-26T09:19:50.623741Z digest=sha256:6bc1dfb12ac08f3c9bcb754dd67f94ce2032f82105f5687be992b02244d45787

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 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-06-29T01:18:19.195007Z digest=sha256:0b1e06c27798a7b763f0982c3ab883757db4483498e4153a39604e51308b26f8