Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A Precise Performance Analysis of Learning with Random Features
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2008.11904.
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 2026-07-14T20:55:11.027028Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-15T01:39:38.335684Z
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 d2fe77bd-ae7d-4f94-8e33-d36bf32ef225 · inbound
Dataset Distillation Efficiently Encodes Low-Dimensional Representations from Gradient-Based Learning of Non-Linear Tasks cites this paper.
Dataset Distillation Efficiently Encodes Low-Dimensional Representations from Gradient-Based Learning of Non-Linear Tasks A Precise Performance Analysis of Learning with Random Features
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T20:55:11.027028Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4ac0783a-83e9-443e-a02a-a359dca114de · inbound
Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation cites this paper.
Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation A Precise Performance Analysis of Learning with Random Features
Reference 30
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T05:51:25.406503Z
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 330d99e1-eaba-4968-9435-22cab4b4facf · inbound
Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model cites this paper.
Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model A Precise Performance Analysis of Learning with Random Features
Reference 141
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T01:39:38.337771Z
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.