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Paper Citation Record · LEDGER

Model Sparsity Can Simplify Machine Unlearning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2304.04934.

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

pith.paper-citation-record.v1
2304.04934 v13

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 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:33:07.577411Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T23:02:46.154205Z

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 6a5c36dd-8a70-4785-8772-2e7c91f00517 · inbound

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation cites this paper.

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation Model Sparsity Can Simplify Machine Unlearning

Reference 227

Resolution
verified exact
arxiv_id, observed 2026-05-16T17:56:23.601349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-16T17:56:23.281678Z digest=sha256:be4920b4135b6e61d9d46926d63ab4fdf732d565913da07272df85fc119288be

Observation e4be9c19-7374-4467-97ce-d73aa8f2c4bd · inbound

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning cites this paper.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Model Sparsity Can Simplify Machine Unlearning

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T14:33:07.577411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:33:07.577411Z digest=sha256:1f51d65876a058893265c45b86743a63e4d807289b7651450ea65037b43ca65c

Observation 51018121-bec9-434d-b9b8-70cb5a807afa · inbound

Leveraging Distribution Matching to Make Approximate Machine Unlearning Faster cites this paper.

Leveraging Distribution Matching to Make Approximate Machine Unlearning Faster Model Sparsity Can Simplify Machine Unlearning

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T17:54:17.700274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:54:17.700274Z digest=sha256:270253e4c518657a967ad896ebe1c68c8556026a2f0bb1b53ee7eccde75092a1

Observation 14e18162-322b-483d-83e2-2082ca4f29e0 · inbound

Forgetting to Witness: Efficient Federated Unlearning and Its Visible Evaluation cites this paper.

Forgetting to Witness: Efficient Federated Unlearning and Its Visible Evaluation Model Sparsity Can Simplify Machine Unlearning

Reference 55

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T21:55:52.790583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T20:26:25.834943Z digest=sha256:8628cbdbffcebb34e03dda2fa759b27656d8e7cf2235cce1df35e82afcb3df6a

Observation fc0c54eb-5a23-4790-9981-cf350d5a99cb · inbound

Multi-Objective Reference-Aligned Machine Unlearning cites this paper.

Multi-Objective Reference-Aligned Machine Unlearning Model Sparsity Can Simplify Machine Unlearning

Reference 13

Resolution
metadata mismatch
arxiv_id, observed 2026-06-28T23:02:46.155654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-28T23:00:51.294463Z digest=sha256:b44b97e1bfdf8f172b75f2e5a3b7b31a285d8cb20d8468961470a96776ebeb78