Paper Citation Record · LEDGER

GCSAM: Gradient Centralized Sharpness Aware Minimization

As of 23 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2501.11584.

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

pith.paper-citation-record.v1
2501.11584 v2

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:10:11.532803Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T15:16:58.221358Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T10:51:21.870404Z

Reference resolution

53 of 53 outbound references displayed

verified exact2
verified fuzzy44
unresolved7
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b16801a7-73e3-40a3-be05-f4e35fa36903 · outbound

This paper cites Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang.

GCSAM: Gradient Centralized Sharpness Aware Minimization Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang

Reference 1

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Observation 5632f7b3-0476-477d-93c1-5ab2530ae2d2 · outbound

This paper cites Quantum con- volutional neural network for image classification.

GCSAM: Gradient Centralized Sharpness Aware Minimization Quantum con- volutional neural network for image classification

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.586355Z

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Observation 131a0603-066f-4f81-aee8-82e2cf21cd1f · outbound

This paper cites Sharpness-aware minimization improves language model generalization.

GCSAM: Gradient Centralized Sharpness Aware Minimization Sharpness-aware minimization improves language model generalization

Reference 3

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Observation d75bb46c-79aa-4126-a964-0817026b16e2 · outbound

This paper cites Momentum-sam: Sharpness aware minimization without computational overhead.

GCSAM: Gradient Centralized Sharpness Aware Minimization Momentum-sam: Sharpness aware minimization without computational overhead

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.554908Z

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Observation 40d45ef6-1fa3-4918-a622-0376f1dcf962 · outbound

This paper cites Entropy-sgd: Biasing gradient descent into wide valleys.

GCSAM: Gradient Centralized Sharpness Aware Minimization Entropy-sgd: Biasing gradient descent into wide valleys

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.540031Z

Source-reported events for the cited work

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Observation 236c728d-a6be-40ec-aa23-ca4f489f0c0d · outbound

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

GCSAM: Gradient Centralized Sharpness Aware Minimization When vision transformers outperform resnets without pre-training or strong data augmentations

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.525213Z

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Observation 20dee52a-57f8-4aa5-8951-bf584110cec9 · outbound

This paper cites Riemannian approach to batch normalization.

GCSAM: Gradient Centralized Sharpness Aware Minimization Riemannian approach to batch normalization

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.509890Z

Source-reported events for the cited work

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Observation 8b30ee63-8eee-4c3d-b975-617a1ddf80db · outbound

This paper cites Sharp minima can generalize for deep nets.

GCSAM: Gradient Centralized Sharpness Aware Minimization Sharp minima can generalize for deep nets

Reference 8

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Source-reported events for the cited work

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Observation 1fadb86b-b386-4af4-8477-cff27a4c0676 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

GCSAM: Gradient Centralized Sharpness Aware Minimization An image is worth 16x16 words: Transformers for image recognition at scale

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.480972Z

Source-reported events for the cited work

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Observation 1332bb3a-4522-4580-8681-b05e26ffb302 · outbound

This paper cites Efficient sharpness-aware minimization for improved training of neural networks.

GCSAM: Gradient Centralized Sharpness Aware Minimization Efficient sharpness-aware minimization for improved training of neural networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.466290Z

Source-reported events for the cited work

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Observation bf3ca5f7-d49b-449c-b540-df349ba7baba · outbound

This paper cites an unresolved cited work.

GCSAM: Gradient Centralized Sharpness Aware Minimization Unresolved cited work

Reference 11

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Observation 51c56b75-e0eb-479b-859f-7716abed7f4e · outbound

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

GCSAM: Gradient Centralized Sharpness Aware Minimization Sharpness-aware minimization for efficiently improving generalization

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.435879Z

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Observation fefb612e-d901-4a7a-a4d9-83b2cec7f385 · outbound

This paper cites Weight and gradi- ent centralization in deep neural networks.

GCSAM: Gradient Centralized Sharpness Aware Minimization Weight and gradi- ent centralization in deep neural networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.420513Z

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Observation 4d9fd4e2-745a-4450-81d2-b74f461fb21c · outbound

This paper cites Cnn-based projected gradient descent for consistent image reconstruction.

GCSAM: Gradient Centralized Sharpness Aware Minimization Cnn-based projected gradient descent for consistent image reconstruction

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.404872Z

Source-reported events for the cited work

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Observation 43ce8fdc-8af0-4c9c-96c9-b41c651d3063 · outbound

This paper cites Do sharpness-based optimizers improve generalization in medi- cal image analysis? arXiv preprint arXiv:2408.04065, 2024.

GCSAM: Gradient Centralized Sharpness Aware Minimization Do sharpness-based optimizers improve generalization in medi- cal image analysis? arXiv preprint arXiv:2408.04065, 2024

Reference 15

Resolution: verified exact; raw_fallback, observed 2026-08-10T18:10:11.801392Z

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Observation 2cdfa2bf-d3f5-4d28-a5ff-73ed950c95e5 · outbound

This paper cites Deep residual learning for image recognition.

GCSAM: Gradient Centralized Sharpness Aware Minimization Deep residual learning for image recognition

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.388947Z

Source-reported events for the cited work

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Observation ee359dda-f003-430d-9758-190030bc6471 · outbound

This paper cites Averaging weights leads to wider optima and better generalization.

GCSAM: Gradient Centralized Sharpness Aware Minimization Averaging weights leads to wider optima and better generalization

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.372313Z

Source-reported events for the cited work

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Observation 0ddef49d-9fb5-423e-b911-b31754ee9299 · outbound

This paper cites Fantastic generalization mea- sures and where to find them.

GCSAM: Gradient Centralized Sharpness Aware Minimization Fantastic generalization mea- sures and where to find them

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.354479Z

Source-reported events for the cited work

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Observation 8b335988-604e-4635-abc1-ac457a900b70 · outbound

This paper cites Kus- ner.

GCSAM: Gradient Centralized Sharpness Aware Minimization Kus- ner

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.336948Z

Source-reported events for the cited work

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Observation 72b63e6a-27b5-470f-b5fa-2ad528e234af · outbound

This paper cites Generalization in deep learning.

GCSAM: Gradient Centralized Sharpness Aware Minimization Generalization in deep learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.320757Z

Source-reported events for the cited work

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Observation 1f8eb0ce-1cdb-478b-8ffb-fbb1556e9415 · outbound

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

GCSAM: Gradient Centralized Sharpness Aware Minimization On large-batch training for deep learning: Generalization gap and sharp minima

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.304391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1f1dd2ce-c19a-49b0-ba56-a77c599b9206 · outbound

This paper cites Accurate image super-resolution using very deep convolutional net- works.

GCSAM: Gradient Centralized Sharpness Aware Minimization Accurate image super-resolution using very deep convolutional net- works

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.287461Z

Source-reported events for the cited work

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Observation 6fd8d1d9-f508-4f17-9ab7-681d2dfedb96 · outbound

This paper cites Fisher sam: Information geometry and sharpness aware min- imisation.

GCSAM: Gradient Centralized Sharpness Aware Minimization Fisher sam: Information geometry and sharpness aware min- imisation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.269579Z

Source-reported events for the cited work

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Observation 4ef9e345-2d7a-4d12-9a28-38e2a56e1ba0 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

GCSAM: Gradient Centralized Sharpness Aware Minimization Adam: A Method for Stochastic Optimization

Reference 24

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Source-reported events for the cited work

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Observation becfbd88-c28c-4539-91db-b4d2408bcdd9 · outbound

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

GCSAM: Gradient Centralized Sharpness Aware Minimization Learning multiple layers of features from tiny images

Reference 25

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Source-reported events for the cited work

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Observation 628b2626-4e0d-47a9-8b85-7ceecf4a8342 · outbound

This paper cites A simple weight decay can improve generalization.

GCSAM: Gradient Centralized Sharpness Aware Minimization A simple weight decay can improve generalization

Reference 26

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Source-reported events for the cited work

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Observation 2f4ef738-0de6-4279-a0e0-790c4d4c841e · outbound

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GCSAM: Gradient Centralized Sharpness Aware Minimization Unresolved cited work

Reference 27

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Source-reported events for the cited work

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Observation 97c47d07-4391-4d8f-8505-4e75f2c558c4 · outbound

This paper cites Asam: Adaptive sharpness-aware minimiza- tion for scale-invariant learning of deep neural networks.

GCSAM: Gradient Centralized Sharpness Aware Minimization Asam: Adaptive sharpness-aware minimiza- tion for scale-invariant learning of deep neural networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.210837Z

Source-reported events for the cited work

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Observation 1a4110a0-c092-4273-9c6c-393f8039875b · outbound

This paper cites A projected gradient descent method for crf inference allowing end-to-end training of arbitrary pairwise potentials.

GCSAM: Gradient Centralized Sharpness Aware Minimization A projected gradient descent method for crf inference allowing end-to-end training of arbitrary pairwise potentials

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.193394Z

Source-reported events for the cited work

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Observation 7a94a1a0-8199-4fe2-a543-d99317cfab8a · outbound

This paper cites Visualizing the loss landscape of neural nets.

GCSAM: Gradient Centralized Sharpness Aware Minimization Visualizing the loss landscape of neural nets

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.175120Z

Source-reported events for the cited work

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Observation 4098d583-2874-4773-ab36-42046fde5fd5 · outbound

This paper cites Visualizing the loss landscape of neural nets.

GCSAM: Gradient Centralized Sharpness Aware Minimization Visualizing the loss landscape of neural nets

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.158467Z

Source-reported events for the cited work

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Observation 80298786-ded3-4718-b96e-aec303ed4a9e · outbound

This paper cites Towards efficient and scalable sharpness-aware minimization.

GCSAM: Gradient Centralized Sharpness Aware Minimization Towards efficient and scalable sharpness-aware minimization

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.140863Z

Source-reported events for the cited work

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Observation 64b81589-7f21-4c3e-91d6-a14544cbf0a4 · outbound

This paper cites Swin trans- former: Hierarchical vision transformer using shifted win- dows.

GCSAM: Gradient Centralized Sharpness Aware Minimization Swin trans- former: Hierarchical vision transformer using shifted win- dows

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.123303Z

Source-reported events for the cited work

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Observation 66cd9120-2822-40e6-bd42-3660402fe5ea · outbound

This paper cites Understanding the generalization benefit of normalization layers: Sharpness reduction.

GCSAM: Gradient Centralized Sharpness Aware Minimization Understanding the generalization benefit of normalization layers: Sharpness reduction

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.106184Z

Source-reported events for the cited work

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Observation 4f2c82a4-ddac-4e05-907b-3e32205a132c · outbound

This paper cites A retinal vessel segmentation method based on the sharpness-aware minimization model.

GCSAM: Gradient Centralized Sharpness Aware Minimization A retinal vessel segmentation method based on the sharpness-aware minimization model

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.089223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f5dfa0d7-d48f-4640-9916-35f8a6f5d308 · outbound

This paper cites Hover-trans: Anatomy-aware hover-transformer for roi-free breast cancer 8 diagnosis in ultrasound images.

GCSAM: Gradient Centralized Sharpness Aware Minimization Hover-trans: Anatomy-aware hover-transformer for roi-free breast cancer 8 diagnosis in ultrasound images

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.073113Z

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Observation f511a9a8-4f27-4999-b416-d2609987b29c · outbound

This paper cites A method for solving a convex programming problem with convergence rate o(1/k2).

GCSAM: Gradient Centralized Sharpness Aware Minimization A method for solving a convex programming problem with convergence rate o(1/k2)

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.055876Z

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Observation d4de2477-b67f-40e7-b02d-265cea7d3782 · outbound

This paper cites Un- derstanding the exploding gradient problem.

GCSAM: Gradient Centralized Sharpness Aware Minimization Un- derstanding the exploding gradient problem

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.038215Z

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Observation ddcdf7c4-1d08-4b50-95eb-a8f272d6d61c · outbound

This paper cites On the momentum term in gradient descent learning algorithms.

GCSAM: Gradient Centralized Sharpness Aware Minimization On the momentum term in gradient descent learning algorithms

Reference 39

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This paper cites A benchmark for breast ultrasound image classification.

GCSAM: Gradient Centralized Sharpness Aware Minimization A benchmark for breast ultrasound image classification

Reference 40

Resolution: verified exact; doi, observed 2026-08-10T18:10:11.577468Z

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Observation c723ad4c-11f5-48b4-916f-0dbfe09ed6a5 · outbound

This paper cites Very deep convo- lutional networks for large-scale image recognition.

GCSAM: Gradient Centralized Sharpness Aware Minimization Very deep convo- lutional networks for large-scale image recognition

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:12.011910Z

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Observation e99f3f32-4a89-4278-a2f9-462f076c33c7 · outbound

This paper cites A bayesian perspective on gen- eralization and stochastic gradient descent.

GCSAM: Gradient Centralized Sharpness Aware Minimization A bayesian perspective on gen- eralization and stochastic gradient descent

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:11.996514Z

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Observation f627d27a-79ad-4f36-9059-776502a4bba1 · outbound

This paper cites On the origin of implicit regularization in stochastic gradient descent.

GCSAM: Gradient Centralized Sharpness Aware Minimization On the origin of implicit regularization in stochastic gradient descent

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:11.981174Z

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Observation 7d11f847-52eb-4a65-8ff9-b9b72075da55 · outbound

This paper cites L2 Regularization versus Batch and Weight Normalization.

GCSAM: Gradient Centralized Sharpness Aware Minimization L2 Regularization versus Batch and Weight Normalization

Reference 44

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Source-reported events for the cited work

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Observation 0d4b0195-0839-4441-8ede-ea681e730cf8 · outbound

This paper cites On orthogonality and learning recurrent networks with long term dependencies.

GCSAM: Gradient Centralized Sharpness Aware Minimization On orthogonality and learning recurrent networks with long term dependencies

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:11.965001Z

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Observation 8f685d8b-4292-4085-90d1-198eef35e706 · outbound

This paper cites Cr-sam: Curva- ture regularized sharpness-aware minimization.

GCSAM: Gradient Centralized Sharpness Aware Minimization Cr-sam: Curva- ture regularized sharpness-aware minimization

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:11.948530Z

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Observation 505499ab-df46-4ed7-8753-a6c74d90f7a9 · outbound

This paper cites Adver- sarial weight perturbation improves generalization in graph neural networks.

GCSAM: Gradient Centralized Sharpness Aware Minimization Adver- sarial weight perturbation improves generalization in graph neural networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:11.928461Z

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Observation c46a76ad-b3b1-43a2-a09d-0486b5a34262 · outbound

This paper cites Understanding and improving layer normaliza- tion.

GCSAM: Gradient Centralized Sharpness Aware Minimization Understanding and improving layer normaliza- tion

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:11.908452Z

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Observation dc9e9dcf-bbd2-4414-8edb-8c9218ada4a7 · outbound

This paper cites Gradient centralization: A new optimization tech- nique for deep neural networks.

GCSAM: Gradient Centralized Sharpness Aware Minimization Gradient centralization: A new optimization tech- nique for deep neural networks

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:11.889072Z

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Observation d7f00fb7-2711-4649-97ea-1cff8f55bf7b · outbound

This paper cites Understanding deep learning re- quires rethinking generalization.

GCSAM: Gradient Centralized Sharpness Aware Minimization Understanding deep learning re- quires rethinking generalization

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:10:11.853828Z

Source-reported events for the cited work

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Observation 0ede5a23-f643-4578-abbd-a9f8c6bbbd70 · outbound

This paper cites Toward understanding the importance of noise in training neural networks.

GCSAM: Gradient Centralized Sharpness Aware Minimization Toward understanding the importance of noise in training neural networks

Reference 51

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This paper cites Surrogate gap minimization im- proves sharpness-aware training.

GCSAM: Gradient Centralized Sharpness Aware Minimization Surrogate gap minimization im- proves sharpness-aware training

Reference 52

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GCSAM: Gradient Centralized Sharpness Aware Minimization Unresolved cited work

Reference 2020

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Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations cites this paper.

Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations GCSAM: Gradient Centralized Sharpness Aware Minimization

Reference 9

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