Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Feature learning as alignment: a structural property of gradient descent in non-linear neural networks
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2402.05271.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T20:29:37.635294Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T13:48:20.879054Z
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 e52ca1e5-c03b-4f84-b573-d5c1b4b75ee1 · inbound
On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations cites this paper.
On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations Feature learning as alignment: a structural property of gradient descent in non-linear neural networks
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T20:29:37.635294Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 95ae1458-79f2-43d5-8129-c599dddfea0c · inbound
Why Geometric Continuity Emerges in Deep Neural Networks: Residual Connections and Rotational Symmetry Breaking cites this paper.
Why Geometric Continuity Emerges in Deep Neural Networks: Residual Connections and Rotational Symmetry Breaking Feature learning as alignment: a structural property of gradient descent in non-linear neural networks
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:41:06.404804Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f48a2322-9c67-480a-9bd6-cc115d890e1a · inbound
Weibull Weight-Scale Parameter Evolution under AdamW Training Dynamics cites this paper.
Weibull Weight-Scale Parameter Evolution under AdamW Training Dynamics Feature learning as alignment: a structural property of gradient descent in non-linear neural networks
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T13:48:20.880534Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.