Paper Citation Record · LEDGER

Model Sparsity Can Simplify Machine Unlearning

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 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 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:49:41.287650Z

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 2026-08-20T06:33:59.587034+00:00.

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.

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.

Observation 9f9c98a1-1137-41ce-b69a-ff249739f47d · inbound

OFMU: Optimization-Driven Framework for Machine Unlearning cites this paper.

OFMU: Optimization-Driven Framework for Machine Unlearning Model Sparsity Can Simplify Machine Unlearning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T15:49:41.287650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-20T06:33:59.587034+00:00.

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 2026-08-20T06:33:59.587034+00:00.