Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2504.18208.
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 2026-08-07T06:04:02.505287Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T19:55:01.085125Z
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 2c071eb5-c3ce-4474-adad-e0b67ce530f9 · inbound
Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures cites this paper.
Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T06:04:02.505287Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 80e6afbd-2b8b-4f40-bef8-226678459fb1 · inbound
Closed-Form Last Layer Optimization cites this paper.
Closed-Form Last Layer Optimization Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime
Reference 1
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-18T10:01:13.465257Z
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 4d457766-2f47-4506-ad5a-0a628c2b9607 · inbound
Rethinking Neural Network Learning Rates: A Stackelberg Perspective cites this paper.
Rethinking Neural Network Learning Rates: A Stackelberg Perspective Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T14:43:06.662422Z
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 8a9fc212-4fed-4f96-90db-b4893dc3518d · inbound
Rethinking Neural Network Learning Rates: A Stackelberg Perspective cites this paper.
Rethinking Neural Network Learning Rates: A Stackelberg Perspective Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T19:55:01.087709Z
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 426ea73a-23d6-4685-9315-7ced04b2844a · inbound
How are linear representations learned? Exact solutions to the dynamics of abstraction cites this paper.
How are linear representations learned? Exact solutions to the dynamics of abstraction Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T06:19:30.027337Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.