Paper Citation Record · LEDGER

Stress-Aware Resilient Neural Training

As of 9 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2508.00098.

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

pith.paper-citation-record.v1
2508.00098 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:26:25.950100Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

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verified fuzzy36
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External citation measurements

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Outbound references

Observation 27c5ce07-83c4-433f-9ea7-8337f95aabcc · outbound

This paper cites An overview of gradient descent optimization algorithms, 2016.

Stress-Aware Resilient Neural Training An overview of gradient descent optimization algorithms, 2016

Reference 1

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Observation a4e60db9-d51c-4adc-ad44-438fbc07b20f · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Stress-Aware Resilient Neural Training Adam: A Method for Stochastic Optimization

Reference 2

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Observation b5db3310-c4d8-4dd1-8659-e0728dac804b · outbound

This paper cites An overview of gradient descent optimization algorithms.

Stress-Aware Resilient Neural Training An overview of gradient descent optimization algorithms

Reference 3

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This paper cites Incorporating nesterov momentum into adam.

Stress-Aware Resilient Neural Training Incorporating nesterov momentum into adam

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:26:27.082501Z

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Observation 5aef73c4-a8f0-4425-a5f3-1cbb1fe5f169 · outbound

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

Stress-Aware Resilient Neural Training Dropout: A simple way to prevent neural networks from overfitting

Reference 5

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This paper cites Deep networks with stochastic depth.

Stress-Aware Resilient Neural Training Deep networks with stochastic depth

Reference 6

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Observation 96bf8e03-f931-4316-9cbf-5d6168d1373c · outbound

This paper cites Rethinking the inception architecture for computer vision.

Stress-Aware Resilient Neural Training Rethinking the inception architecture for computer vision

Reference 7

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Observation 88e14e49-bf5b-4cc6-a843-ce434d294eb0 · outbound

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

Stress-Aware Resilient Neural Training Sharpness-aware minimization for efficiently improving generalization

Reference 8

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Observation 9dbea262-a0ea-42f1-85ef-68122e3ca488 · outbound

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

Stress-Aware Resilient Neural Training Averaging weights leads to wider optima and better generalization

Reference 9

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

Stress-Aware Resilient Neural Training Entropy-sgd: Biasing gradient descent into wide valleys

Reference 10

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Observation 3e2da785-d634-4092-b244-96a0d3e8c3a7 · outbound

This paper cites Bayesian learning via stochastic gradient langevin dynamics.

Stress-Aware Resilient Neural Training Bayesian learning via stochastic gradient langevin dynamics

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:26:26.953318Z

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Observation 9a3e4d25-69d4-43ad-8718-3ef519bff1df · outbound

This paper cites Noise can help: Accelerating learning through controlled noise injection.

Stress-Aware Resilient Neural Training Noise can help: Accelerating learning through controlled noise injection

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:26:26.937343Z

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Observation a3b60cb9-3f05-4c07-9f66-328480af4f99 · outbound

This paper cites Training robust deep networks with adversarial noise.

Stress-Aware Resilient Neural Training Training robust deep networks with adversarial noise

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:26:26.919127Z

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Observation 5daa0a73-6745-40c6-9d7e-503d5442c784 · outbound

This paper cites Online learning rate adaptation with hypergradient descent.

Stress-Aware Resilient Neural Training Online learning rate adaptation with hypergradient descent

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:26:26.899542Z

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Observation d3ef1006-3709-4ffb-a3a8-267a8e098c45 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

Stress-Aware Resilient Neural Training Model-agnostic meta-learning for fast adaptation of deep networks

Reference 15

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Observation 123b8121-4d6a-4b18-ad98-f674a1a8abf7 · outbound

This paper cites Towards deep learning models resistant to adversarial attacks.

Stress-Aware Resilient Neural Training Towards deep learning models resistant to adversarial attacks

Reference 16

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Observation 2c180cf6-8f5a-4b47-9eed-d771435970d7 · outbound

This paper cites Certifiable distributional robustness with principled adversarial training.

Stress-Aware Resilient Neural Training Certifiable distributional robustness with principled adversarial training

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:26:26.843224Z

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Observation 4d4a03ee-8e14-4a72-a5f8-bb0fecf76520 · outbound

This paper cites Improving dnn robustness to adversarial attacks using jacobian regularization.

Stress-Aware Resilient Neural Training Improving dnn robustness to adversarial attacks using jacobian regularization

Reference 18

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Observation e3364af8-a839-4ef3-a502-85ea139c9f5f · outbound

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Stress-Aware Resilient Neural Training Unresolved cited work

Reference 19

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Stress-Aware Resilient Neural Training Unresolved cited work

Reference 20

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Stress-Aware Resilient Neural Training Unresolved cited work

Reference 21

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Stress-Aware Resilient Neural Training Unresolved cited work

Reference 22

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Observation d08cba57-238b-41c0-baef-47d320fb7a61 · outbound

This paper cites Particle swarm optimization.

Stress-Aware Resilient Neural Training Particle swarm optimization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:26:26.717564Z

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Observation 85396d28-500b-4573-9de8-ac88ca671621 · outbound

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Stress-Aware Resilient Neural Training Unresolved cited work

Reference 24

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This paper cites Grey wolf optimizer.

Stress-Aware Resilient Neural Training Grey wolf optimizer

Reference 25

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Observation dfea4936-8d1d-43f8-80da-f4a9b1e42552 · outbound

This paper cites Daniel Gelatt, and Mario P.

Stress-Aware Resilient Neural Training Daniel Gelatt, and Mario P

Reference 26

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Observation 1494af05-353d-4e4d-81f2-45d408969249 · outbound

This paper cites DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathology.

Stress-Aware Resilient Neural Training DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathology

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-06T10:26:26.278317Z

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Observation f22e9bd9-19bb-4398-b15a-dd20bda93de3 · outbound

This paper cites Unit-based histopathology tissue segmentation via multi-level feature representation.

Stress-Aware Resilient Neural Training Unit-based histopathology tissue segmentation via multi-level feature representation

Reference 28

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Observation b0845e52-e079-454a-a459-55ee4be9ab8d · outbound

This paper cites Transformative ai for automating histopathology workflows, 2025.

Stress-Aware Resilient Neural Training Transformative ai for automating histopathology workflows, 2025

Reference 29

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Observation 28f007b2-1850-438d-aeb8-775d22fda0a1 · outbound

This paper cites Velu: Variance-enhanced learning unit for deep neural networks.

Stress-Aware Resilient Neural Training Velu: Variance-enhanced learning unit for deep neural networks

Reference 30

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Observation 26133c63-b669-4746-aade-5d1899a2926b · outbound

This paper cites Ai for advanced cancer diagnosis: a cad system empowered by a novel vision transformer network for histopathology analysis.

Stress-Aware Resilient Neural Training Ai for advanced cancer diagnosis: a cad system empowered by a novel vision transformer network for histopathology analysis

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:26:26.626337Z

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Observation 4cce3660-ffce-4ef5-8ad4-6a9fe001a31d · outbound

This paper cites A cad system for diagnosing alzheimer’s disease using 2d slices and an improved alexnet-svm method.

Stress-Aware Resilient Neural Training A cad system for diagnosing alzheimer’s disease using 2d slices and an improved alexnet-svm method

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:26:26.607430Z

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Stress-Aware Resilient Neural Training Tcnn: A transformer convolutional neural network for artifact classification in whole slide images

Reference 33

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Observation 5e8b2f53-f07c-409f-ae03-990cfbbcf1a5 · outbound

This paper cites A fast and yet efficient yolov3 for blood cell detection.

Stress-Aware Resilient Neural Training A fast and yet efficient yolov3 for blood cell detection

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:26:26.568489Z

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Observation 73e64fd7-592c-44ca-8013-a1e80d71927d · outbound

This paper cites Densely connected convolutional networks.

Stress-Aware Resilient Neural Training Densely connected convolutional networks

Reference 35

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Observation 7d80ee31-a9f7-4972-988c-016f7ba94d37 · outbound

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Stress-Aware Resilient Neural Training Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 36

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Observation e493c385-1ec0-4d81-b5f3-256384e2e3d1 · outbound

This paper cites Deep residual learning for image recognition.

Stress-Aware Resilient Neural Training Deep residual learning for image recognition

Reference 37

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Observation a2161e2a-fa40-4bbd-943f-44ca09273271 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

Stress-Aware Resilient Neural Training Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 38

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Observation 467da528-a20a-4dcd-8604-33fbe29b2709 · outbound

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Stress-Aware Resilient Neural Training Imagenette: A smaller subset of imagenet for fast experimentation

Reference 39

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Observation d8447c20-709e-4eb2-af48-507a5fa031f7 · outbound

This paper cites Imagewoof: A variant of imagenette with similar classes.

Stress-Aware Resilient Neural Training Imagewoof: A variant of imagenette with similar classes

Reference 40

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Observation 90cc3c2c-4707-4771-bb90-aee614ec1626 · outbound

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Stress-Aware Resilient Neural Training Tiny imagenet challenge

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:26:26.453217Z

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Observation a899f8b2-76be-47ba-8fae-66cb954180ec · outbound

This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification.

Stress-Aware Resilient Neural Training Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:26:26.436752Z

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Observation bf1b0568-23fa-48a9-8394-320a3ed9e1fb · outbound

This paper cites Deeper, broader and artier domain generalization.

Stress-Aware Resilient Neural Training Deeper, broader and artier domain generalization

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:26:26.420174Z

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Observation 79707682-13ef-4976-800d-75847efdf55b · outbound

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

Stress-Aware Resilient Neural Training Learning multiple layers of features from tiny images

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:26:26.401975Z

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Observation 0458cc42-6d06-47b0-9314-5433e6cd70f0 · outbound

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Stress-Aware Resilient Neural Training Unresolved cited work

Reference 45

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Observation 84b55bb3-9127-49a2-8941-857543c3f66e · outbound

This paper cites Lecture 6.5—rmsprop: Divide the gradient by a running average of its recent magnitude, 2012.

Stress-Aware Resilient Neural Training Lecture 6.5—rmsprop: Divide the gradient by a running average of its recent magnitude, 2012

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:26:26.367886Z

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Observation fbb1dce0-37a1-4d09-9fed-432f08679279 · outbound

This paper cites An effective reinforcement learning method for preventing the overfitting of convolutional neural networks.

Stress-Aware Resilient Neural Training An effective reinforcement learning method for preventing the overfitting of convolutional neural networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:26:26.349596Z

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Observation 6c81a2cd-4e3b-464b-a3ce-d9d95ff06618 · outbound

This paper cites Scenegenie: Scene graph guided diffusion models for image synthesis.

Stress-Aware Resilient Neural Training Scenegenie: Scene graph guided diffusion models for image synthesis

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:26:26.332515Z

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.

Pith citing papers

No inbound Pith citation observations are available.