Paper Citation Record · LEDGER

FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2405.12807.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2405.12807 v11

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:44:27.046167Z

measured 0 of 1 external citation measurements

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
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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 05c1eb62-aff8-4045-afd3-fc7f1021728d · inbound

Grokking vs. Learning: Same Features, Different Encodings cites this paper.

Grokking vs. Learning: Same Features, Different Encodings FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T14:44:27.046167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+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.