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Paper Citation Record · LEDGER

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm

As of 11 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2501.06603.

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

pith.paper-citation-record.v1
2501.06603 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:03:54.945060Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:21:20.602760Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-09T12:21:20.649779Z

Reference resolution

36 of 36 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cd564273-d512-4c53-b88f-ced01750a168 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 1

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Observation 793b8bc6-2a09-47d7-bb86-ca4ecd11462f · outbound

This paper cites AST: Audio Spectrogram Transformer.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm AST: Audio Spectrogram Transformer

Reference 2

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Observation 552e3464-7efa-418b-98dd-c8a67eaaf648 · outbound

This paper cites When vision transformers out- perform resnets without pre-training or strong data augmentations,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm When vision transformers out- perform resnets without pre-training or strong data augmentations,

Reference 3

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Observation 6a0f548d-6574-46d0-a15a-3454d610563e · outbound

This paper cites Un- derstanding deep learning (still) requires rethinking generalization,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Un- derstanding deep learning (still) requires rethinking generalization,

Reference 4

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Observation b201b411-4961-44e2-8512-98edf04b4d1c · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Dropout: a simple way to prevent neural networks from overfitting,

Reference 5

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Observation 83864ca5-533b-4789-85ac-ab7e4bd3afb2 · outbound

This paper cites Au- toaugment: Learning augmentation strategies from data,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Au- toaugment: Learning augmentation strategies from data,

Reference 6

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Observation f8bdb589-5421-4396-a35f-ad62644eac49 · outbound

This paper cites The Effects of Regularization and Data Augmentation are Class Dependent.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm The Effects of Regularization and Data Augmentation are Class Dependent

Reference 7

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Observation 6cc5a387-89d8-4687-b938-90b0081962de · outbound

This paper cites On large-batch training for deep learning: Generalization gap and sharp minima.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm On large-batch training for deep learning: Generalization gap and sharp minima

Reference 8

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Observation baa46c82-479c-4e05-8cbd-3fcfe9456050 · outbound

This paper cites Towards understanding sharpness-aware minimization.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Towards understanding sharpness-aware minimization

Reference 9

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Source-reported events for the cited work

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Observation 8b0db0f8-652b-4135-b9db-293b2f3ab9de · outbound

This paper cites How does sharpness-aware minimiza- tion minimizes sharpness,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm How does sharpness-aware minimiza- tion minimizes sharpness,

Reference 10

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Source-reported events for the cited work

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Observation ba72502b-e7b8-4349-9aa2-dd6bc6eab9b2 · outbound

This paper cites Fantastic Generalization Measures and Where to Find Them.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Fantastic Generalization Measures and Where to Find Them

Reference 11

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Source-reported events for the cited work

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Observation 7c5eb73d-673a-4765-ba44-79a3787ed851 · outbound

This paper cites Sharpness-aware minimization for efficiently improving generalization,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Sharpness-aware minimization for efficiently improving generalization,

Reference 12

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Observation b064d62f-a382-42e7-83f0-1f62772fe6d3 · outbound

This paper cites ASAM: Adaptive sharpness- aware minimization for scale-invariant learning of deep neural net- works,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm ASAM: Adaptive sharpness- aware minimization for scale-invariant learning of deep neural net- works,

Reference 13

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Source-reported events for the cited work

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Observation 6209988b-774b-4a5a-9337-f25ef662583a · outbound

This paper cites Fisher SAM: Information geometry and sharpness aware minimisation.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Fisher SAM: Information geometry and sharpness aware minimisation

Reference 14

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Observation 88f46fff-9b43-4c2b-a01d-07abb1a0521b · outbound

This paper cites Surrogate gap minimization improves sharpness-aware training.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Surrogate gap minimization improves sharpness-aware training

Reference 15

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Source-reported events for the cited work

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Observation 3bb0d539-5966-47f6-8a68-802d284bd8d9 · outbound

This paper cites Penalizing gradient norm for efficiently improving generalization in deep learning,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Penalizing gradient norm for efficiently improving generalization in deep learning,

Reference 16

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Observation c013e037-28b1-43ae-ad9b-f7782777dfa1 · outbound

This paper cites Implicit regularization of sharpness-aware minimization for scale-invariant problems,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Implicit regularization of sharpness-aware minimization for scale-invariant problems,

Reference 17

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Observation 374d54f7-188a-4515-afa1-1d6b93739828 · outbound

This paper cites Make sharpness-aware minimization stronger: A sparsified perturbation ap- proach,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Make sharpness-aware minimization stronger: A sparsified perturbation ap- proach,

Reference 18

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Source-reported events for the cited work

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Observation 7eda21b3-0443-4e73-9d8c-415903902c50 · outbound

This paper cites An Adaptive Policy to Employ Sharpness-Aware Minimization.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm An Adaptive Policy to Employ Sharpness-Aware Minimization

Reference 19

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Source-reported events for the cited work

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Observation 5521b8bf-f591-44cb-a606-26d5d7b166b7 · outbound

This paper cites Randomized Sharpness-Aware Training for Boosting Computational Efficiency in Deep Learning.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Randomized Sharpness-Aware Training for Boosting Computational Efficiency in Deep Learning

Reference 20

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Observation 2ba0936d-2abf-4870-9f60-2894f9b032cd · outbound

This paper cites Enhancing sharpness-aware optimization through variance suppression,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Enhancing sharpness-aware optimization through variance suppression,

Reference 21

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Observation 1e2be162-7750-4954-a153-a4426aea4e89 · outbound

This paper cites Stochastic first-and zeroth-order methods for nonconvex stochastic programming,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Stochastic first-and zeroth-order methods for nonconvex stochastic programming,

Reference 22

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Observation d3a0fe84-dc76-44e8-a6cb-1137a45bc1cc · outbound

This paper cites Optimization methods for large- scale machine learning,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Optimization methods for large- scale machine learning,

Reference 23

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Observation 9d7356a7-c606-4fe9-8e3e-0f2a525f2021 · outbound

This paper cites Sharp minima can generalize for deep nets,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Sharp minima can generalize for deep nets,

Reference 24

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Observation f1611d18-b23b-4fdc-956e-7ce16ce9bdce · outbound

This paper cites Sparse prediction with the k- support norm,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Sparse prediction with the k- support norm,

Reference 25

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Observation 626fbbf2-a8a0-4734-b04e-e6c8ce5c8d44 · outbound

This paper cites AdaBelief optimizer: Adapting stepsizes by the belief in observed gradients,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm AdaBelief optimizer: Adapting stepsizes by the belief in observed gradients,

Reference 26

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Observation ef4fea22-795b-4709-ae61-17f89a81c1cf · outbound

This paper cites Learning multiple layers of features from tiny images,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Learning multiple layers of features from tiny images,

Reference 27

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Observation 8b340799-b37b-49e7-8cc4-07532b2224d8 · outbound

This paper cites Deep residual learning for image recognition,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Deep residual learning for image recognition,

Reference 28

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Source-reported events for the cited work

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Observation 4f0212b0-c477-4c97-8e26-9b9dff26bfc4 · outbound

This paper cites Densely connected convolutional networks,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Densely connected convolutional networks,

Reference 29

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Observation d547cc87-f0fd-4a54-9ef6-62f942705646 · outbound

This paper cites Wide Residual Networks.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Wide Residual Networks

Reference 30

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Observation 30ac6866-ec77-41fb-9b95-c4e435b43711 · outbound

This paper cites Deep pyramidal residual networks,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Deep pyramidal residual networks,

Reference 31

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Observation 07495ada-829f-48a5-9839-acfee000a8c8 · outbound

This paper cites ImageNet: A large-scale hierarchical image database,.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm ImageNet: A large-scale hierarchical image database,

Reference 32

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Source-reported events for the cited work

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Observation e0e4a3f3-ed83-4938-b31d-acd55ad5b91c · outbound

This paper cites Asymmetric valleys: Beyond sharp and flat local minima.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Asymmetric valleys: Beyond sharp and flat local minima

Reference 33

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Source-reported events for the cited work

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Observation 938f4382-5fbb-41f2-9a89-6728fe74df90 · outbound

This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Improved Regularization of Convolutional Neural Networks with Cutout

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 59efc412-cae5-46fc-80a4-3d503299f804 · outbound

This paper cites xt finishes the proof.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm xt finishes the proof

Reference 35

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Source-reported events for the cited work

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Observation 7ac8bace-3ee9-40d2-9eda-867f9c68190c · outbound

This paper cites Hyperparameters used in our experiments are summarized in Tabs.

Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm Hyperparameters used in our experiments are summarized in Tabs

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T21:03:55.476577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T21:03:54.945060Z digest=sha256:e11ddabbaeb0b714801d9348b16e18c0431f5acfe720fb20458999474506e9a1

Pith citing papers

Observation 5d80aa7d-64bc-480a-bd88-74774ea4ce44 · inbound

Avoiding spurious sharpness minimization broadens applicability of SAM cites this paper.

Avoiding spurious sharpness minimization broadens applicability of SAM Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm

Reference 62

Resolution
verified exact
local_arxiv, observed 2026-08-09T12:21:20.656668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-09T12:21:20.602760Z digest=sha256:42625b11b052e9f921c3cea5653646ed969ac87bd925f2d0f1a81d148fbcbca2