Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Mechanism of feature learning in convolutional neural networks
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2309.00570.
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 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T00:25:16.775131Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-14T20:07:53.352476Z
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 2d90223a-b4d2-4ccb-9699-e8c4a52fd406 · inbound
Towards a Statistical Understanding of Neural Networks: Beyond the Neural Tangent Kernel Theories cites this paper.
Towards a Statistical Understanding of Neural Networks: Beyond the Neural Tangent Kernel Theories Mechanism of feature learning in convolutional neural networks
Reference 2007
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T04:36:32.019182Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 88e5ce86-8a4e-427b-ad8d-028614980bb1 · inbound
The Features at Convergence Theorem: a first-principles alternative to the Neural Feature Ansatz for how networks learn representations cites this paper.
The Features at Convergence Theorem: a first-principles alternative to the Neural Feature Ansatz for how networks learn representations Mechanism of feature learning in convolutional neural networks
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:31:19.028549Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 756f83ab-1d4d-4150-b935-63e6e8578997 · inbound
AGOP as Explanation: From Feature Learning to Per-Sample Attribution in Image Classifiers cites this paper.
AGOP as Explanation: From Feature Learning to Per-Sample Attribution in Image Classifiers Mechanism of feature learning in convolutional neural networks
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T20:07:53.356131Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 5d8d9e96-32bf-45c3-b134-0dc405a85b1a · inbound
Mechanist: AI as a Scientific Instrument for Discovering the Mechanisms of Intelligence cites this paper.
Mechanist: AI as a Scientific Instrument for Discovering the Mechanisms of Intelligence Mechanism of feature learning in convolutional neural networks
Reference 100
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T00:25:16.775131Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 42a9182b-5bd5-4a86-a290-73148b9096ba · inbound
Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws cites this paper.
Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws Mechanism of feature learning in convolutional neural networks
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T13:30:46.556266Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.