Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning from Randomly Initialized Neural Network Features
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2202.06438.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T08:10:07.317365Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T06:39:38.255974Z
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 b137041b-2d3c-49dc-8f71-be911397b5bc · inbound
LAMP: Data-Efficient Linear Affine Weight-Space Models for Parameter-Controlled 3D Shape Generation and Extrapolation cites this paper.
LAMP: Data-Efficient Linear Affine Weight-Space Models for Parameter-Controlled 3D Shape Generation and Extrapolation Learning from Randomly Initialized Neural Network Features
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T08:10:07.317365Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1041472c-8aec-4d3f-857a-96a87381f80c · 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 Learning from Randomly Initialized Neural Network Features
Reference 44
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T05:31:24.341369Z
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.
Observation 1894ae28-131b-48a7-a19a-a58369c20368 · inbound
DIPBox: A Multi-scale Testing Framework for Tracking Dataset Regeneration cites this paper.
DIPBox: A Multi-scale Testing Framework for Tracking Dataset Regeneration Learning from Randomly Initialized Neural Network Features
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T06:39:38.257418Z
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.