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Paper Citation Record · LEDGER

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

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 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 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:39:09.533264Z

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
  • 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 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.

source=arxiv_source observed=2026-08-07T12:39:09.533264Z digest=sha256:84d1d45094a6e27159b5bdc8056814031b7463680632345c32140128657e6115

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.

source=pdf_text observed=2026-08-06T20:55:06.577457Z digest=sha256:ada0ff80ec22be37256787c38c5d6d35eb7eaf946b766d4ce863afbd63b904ea

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.

source=pdf_text observed=2026-08-05T17:06:10.224741Z digest=sha256:9149c07b5a211f2772746776298ecd92827b1233ac16f5239c912b87c416d354

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-21T23:35:06.350853Z digest=sha256:62f3f60f41c1dc4e7490da12a38f904966a306db27caf0c6211440095a600ba6

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T17:01:05.411513Z digest=sha256:3216f2b200a08161b6ab35bc2f4d95f264bf435a6b9fb2d27506ab4e0fef8c63

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-09T15:39:51.611115Z digest=sha256:24ff5c4f45b9bb952606ee16e8e44f65347731336239233f0962a3e6627b5387

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-28T11:21:53.393381Z digest=sha256:3a3d96d6133f26f546a75c551cda53009708197b6d42b6c4b4e470ae9a1ab843

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.

source=pdf_text observed=2026-08-01T22:36:56.240433Z digest=sha256:fb0ae45cf9ecf85e97b7f6294c0812c5523b6054026c29030195e4ddec4083e1