Paper Citation Record · LEDGER

Self-Supervised Learning via Maximum Entropy Coding

As of 24 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2210.11464.

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

pith.paper-citation-record.v1
2210.11464 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:18:11.728266Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T19:03:18.098907Z

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 cc628682-571a-47fa-b972-b58fa0890063 · inbound

Rethinking Generalizability and Discriminability of Self-Supervised Learning from Evolutionary Game Theory Perspective cites this paper.

Rethinking Generalizability and Discriminability of Self-Supervised Learning from Evolutionary Game Theory Perspective Self-Supervised Learning via Maximum Entropy Coding

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-12T05:22:34.446670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e272223-f641-4c5e-9557-585fbcec0571 · inbound

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research cites this paper.

Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research Self-Supervised Learning via Maximum Entropy Coding

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-16T12:18:11.728266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8c1c38b-6f90-4b77-8408-92414a93841c · inbound

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning cites this paper.

Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Self-Supervised Learning via Maximum Entropy Coding

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T15:56:56.165584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f9a4179-83e5-4ffe-8e4d-694f00ead8ed · inbound

A Generalized Learning Framework for Self-Supervised Contrastive Learning cites this paper.

A Generalized Learning Framework for Self-Supervised Contrastive Learning Self-Supervised Learning via Maximum Entropy Coding

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-05T19:03:18.106816Z

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.