Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Feature Learning in $L_{2}$-regularized DNNs: Attraction/Repulsion and Sparsity
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2205.15809.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T22:45:54.809438Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T11:54:38.499273Z
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 1abeeb64-ad7c-4b1e-9d7e-60e63e3aa1b0 · inbound
Phase Transitions between Accuracy Regimes in L2 regularized Deep Neural Networks cites this paper.
Phase Transitions between Accuracy Regimes in L2 regularized Deep Neural Networks Feature Learning in $L_{2}$-regularized DNNs: Attraction/Repulsion and Sparsity
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:45:54.809438Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6ca4961b-ec09-41bd-a514-6227a85456cd · inbound
The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently cites this paper.
The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently Feature Learning in $L_{2}$-regularized DNNs: Attraction/Repulsion and Sparsity
Reference 63
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T05:31:24.083012Z
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 dc27f462-21a6-427b-a4d5-13b0d3a251f3 · inbound
Theoretical Analysis of Sparse Optimization with Reparameterization, Weight Decay, and Adaptive Learning Rate cites this paper.
Theoretical Analysis of Sparse Optimization with Reparameterization, Weight Decay, and Adaptive Learning Rate Feature Learning in $L_{2}$-regularized DNNs: Attraction/Repulsion and Sparsity
Reference 37
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-30T11:54:38.501130Z
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.