Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
SGD and Weight Decay Secretly Minimize the Rank of Your Neural Network
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2206.05794.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T16:21:41.231938Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T16:07:09.448556Z
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 7219d340-2303-4817-a016-d38329641ca0 · inbound
On Generalization Bounds for Neural Networks with Low Rank Layers cites this paper.
On Generalization Bounds for Neural Networks with Low Rank Layers SGD and Weight Decay Secretly Minimize the Rank of Your Neural Network
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T16:21:41.231938Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 706d7da5-4244-4ea5-aa3d-85df53b06cf3 · 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 SGD and Weight Decay Secretly Minimize the Rank of Your Neural Network
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:31:20.239832Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1fdfa8de-876d-4140-be48-13626623bd21 · inbound
Evolutionary Search for Automated Design of Uncertainty Quantification Methods cites this paper.
Evolutionary Search for Automated Design of Uncertainty Quantification Methods SGD and Weight Decay Secretly Minimize the Rank of Your Neural Network
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T19:18:09.217340Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a0e1c6e9-3caf-40c9-ad18-a3986cf9d05a · inbound
Does Weight Decay Enhance Training Stability? cites this paper.
Does Weight Decay Enhance Training Stability? SGD and Weight Decay Secretly Minimize the Rank of Your Neural Network
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T19:53:42.998253Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 99b448d0-bb6a-4de8-9beb-de8a7e182b88 · inbound
Weight Decay Regimes in Grokking Transformers: Cheap Online Diagnostics cites this paper.
Weight Decay Regimes in Grokking Transformers: Cheap Online Diagnostics SGD and Weight Decay Secretly Minimize the Rank of Your Neural Network
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T07:34:02.899180Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ec9025ee-74ed-47e5-9d30-0ac4460eb6e1 · inbound
The Implicit Bias of Depth: From Neural Collapse to Softmax Codes cites this paper.
The Implicit Bias of Depth: From Neural Collapse to Softmax Codes SGD and Weight Decay Secretly Minimize the Rank of Your Neural Network
Reference 137
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T05:26:38.933941Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 8a2839c8-1691-49ff-8d3e-24e1994385e0 · inbound
Deciphering Two Training Clocks in Grokking via Deep Linear Network Theory with Conditional ReLU Reduction cites this paper.
Deciphering Two Training Clocks in Grokking via Deep Linear Network Theory with Conditional ReLU Reduction SGD and Weight Decay Secretly Minimize the Rank of Your Neural Network
Reference 25
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T12:06:56.089444Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation f3c56663-8160-484e-8fb3-f7c1313bdbc8 · inbound
Reversible Foundations: Training a 120B Sparse MoE through State-Preserving Scaling cites this paper.
Reversible Foundations: Training a 120B Sparse MoE through State-Preserving Scaling SGD and Weight Decay Secretly Minimize the Rank of Your Neural Network
Reference 40
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T16:07:09.450255Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.