Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2206.01717.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T20:09:35.271048Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-10T05:30:23.456663Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
5
pith, observed 2026-08-10T05:30:23.456663Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation fa6a2f72-b47b-4e36-b34e-5b8fd04ff14b · inbound
Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond cites this paper.
Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features
Reference 110
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T20:09:35.271048Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fb5dc8c5-2d2a-4888-aad6-f1915755851c · inbound
Feature learning is decoupled from generalization in high capacity neural networks cites this paper.
Feature learning is decoupled from generalization in high capacity neural networks A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features
Reference 58
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T14:17:37.553585Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.