Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2306.04815.
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 2026-08-06T19:31:23.560693Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation af7e1ca8-b352-49da-a3f4-621f29fa83df · 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 Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:31:23.560693Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b1702c3e-1070-4189-83a0-ceba8119b40e · inbound
SoftSignSGD(S3): An Enhanced Optimizer for Practical DNN Training and Loss Spikes Minimization Beyond Adam cites this paper.
SoftSignSGD(S3): An Enhanced Optimizer for Practical DNN Training and Loss Spikes Minimization Beyond Adam Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:10:16.595651Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bb15f699-163c-48d1-be38-1628b22a6daa · inbound
Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data cites this paper.
Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T05:57:21.850912Z
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.