Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2405.12807.
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 2026-08-08T11:46:47.479823Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T01:56:28.438782Z
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 3f0ec22f-5055-46d2-a09d-78f7a9910efd · inbound
Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension cites this paper.
Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T11:46:47.479823Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 64541f16-067b-484f-b057-2717d82b410f · inbound
Beyond the LUMIR challenge: The pathway to foundational registration models cites this paper.
Beyond the LUMIR challenge: The pathway to foundational registration models FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information
Reference 74
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:39:09.533264Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f914b36a-fff1-41dd-bc88-3abc5e3a10d2 · inbound
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks cites this paper.
How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:55:06.577457Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7429b3d1-32a4-4e56-8d1c-ff82fb1e5e4c · inbound
Module-Aware Parameter-Efficient Machine Unlearning on Transformers cites this paper.
Module-Aware Parameter-Efficient Machine Unlearning on Transformers FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T17:06:10.224741Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6138ffe7-e906-4e24-bc8a-54398a9e5772 · inbound
Preconditioned Regularized Wasserstein Proximal Sampling cites this paper.
Preconditioned Regularized Wasserstein Proximal Sampling FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T23:35:45.666978Z
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 2071cd2f-8b67-4d2b-a567-7d87a2790957 · inbound
Natural Riemannian gradient for learning functional tensor networks cites this paper.
Natural Riemannian gradient for learning functional tensor networks FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T07:41:01.558783Z
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 995d9d3b-d39f-4bd7-a8be-f42e5ad9a8d2 · inbound
Revealing Modular Gradient Noise Imbalance in LLMs: Calibrating Adam via Signal-to-Noise Ratio cites this paper.
Revealing Modular Gradient Noise Imbalance in LLMs: Calibrating Adam via Signal-to-Noise Ratio FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T16:41:05.142259Z
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 ae50d6e5-d1ad-4217-a3b3-1a8a66856b7a · inbound
MAdam: Metric-Aware Multi-Objective Adam cites this paper.
MAdam: Metric-Aware Multi-Objective Adam FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T01:56:28.441249Z
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 a66805d2-b764-47fe-b70c-9439513a0923 · inbound
Gradient-Energy Guided Block-Wise Perturbations for Sharpness-Aware Minimization cites this paper.
Gradient-Energy Guided Block-Wise Perturbations for Sharpness-Aware Minimization FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T22:36:56.240433Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.