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Paper Citation Record · LEDGER

Understanding Self-supervised Learning with Dual Deep Networks

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2010.00578.

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

pith.paper-citation-record.v1
2010.00578 v6

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:28:09.249459Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T23:51:50.222210Z

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 6862c42b-0b43-458a-a80e-3c235f6692c5 · inbound

GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection cites this paper.

GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection Understanding Self-supervised Learning with Dual Deep Networks

Reference 40

Resolution
verified exact
arxiv_id, observed 2026-05-16T23:51:50.224917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-16T23:51:50.163520Z digest=sha256:e531cf758a4dd3a958353afcee99b225926c03a23e189eae55749642c180a008

Observation 3094bc29-ff8e-41af-a601-f40ed3188fa0 · inbound

Generalization Analysis for Deep Contrastive Representation Learning cites this paper.

Generalization Analysis for Deep Contrastive Representation Learning Understanding Self-supervised Learning with Dual Deep Networks

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-11T14:28:09.249459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T14:28:09.249459Z digest=sha256:b05b2b5a102794671386be0b63a91374eaa2ff3d42ca9eac542de09e9dafb449

Observation 638cc6ad-01c2-4f94-a770-334d44e06e4b · inbound

Gradient Weight-normalized Low-rank Projection for Efficient LLM Training cites this paper.

Gradient Weight-normalized Low-rank Projection for Efficient LLM Training Understanding Self-supervised Learning with Dual Deep Networks

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-11T00:15:00.470740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:15:00.470740Z digest=sha256:d42fbff2e0b80f541e14647e537a1e698ef177126e2adb2de01e0ea1175bcf23

Observation b3c55a12-7f0f-436f-bc2d-a67a591c09dd · inbound

Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension cites this paper.

Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension Understanding Self-supervised Learning with Dual Deep Networks

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-08T11:46:47.771026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T11:46:47.771026Z digest=sha256:0276a24fca05eef18058d1093f1b23162a23a90799fde980358b9d592aedfac6

Observation 57b73f2a-07ab-40a3-a768-df78700b55ce · inbound

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge cites this paper.

CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge Understanding Self-supervised Learning with Dual Deep Networks

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-03T13:09:39.997759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T13:09:39.997759Z digest=sha256:fa252ecf146e40beadd729ee1bb3b0770e46070d24ad7b600eb57bbbd3356636