Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Privacy-preserving Learning via Deep Net Pruning
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2003.01876.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T19:06:25.820295Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T17:56:23.373565Z
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 6c4c0d19-efd3-43c9-9891-26699273e0db · 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 Privacy-preserving Learning via Deep Net Pruning
Reference 176
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T17:56:23.376136Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation ef1dd743-873a-41ef-b270-edd163578ee7 · inbound
AdaDPIGU: Differentially Private SGD with Adaptive Clipping and Importance-Based Gradient Updates for Deep Neural Networks cites this paper.
AdaDPIGU: Differentially Private SGD with Adaptive Clipping and Importance-Based Gradient Updates for Deep Neural Networks Privacy-preserving Learning via Deep Net Pruning
Reference 2019
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:06:25.820295Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 505b1bc2-9360-4eb6-a49d-be658c8f986f · inbound
Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization cites this paper.
Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Privacy-preserving Learning via Deep Net Pruning
Reference 97
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T21:06:26.201232Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.