Paper Citation Record · LEDGER

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers

As of 10 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2509.05086.

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

pith.paper-citation-record.v1
2509.05086 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T05:40:26.436845Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

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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

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Source: cited_works

Reference resolution

34 of 34 outbound references displayed

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verified fuzzy24
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External citation measurements

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Outbound references

Observation 3f832f14-ccef-4f4b-b437-f16b669bb120 · outbound

This paper cites Moe-rbench: Towards building reliable language models with sparse mixture-of-experts.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Moe-rbench: Towards building reliable language models with sparse mixture-of-experts

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:31.963725Z

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Observation 954aa42d-f801-4a35-b306-1b96c8a031a1 · outbound

This paper cites Patch-level routing in mixture-of-experts is provably sample-efficient for con- volutional neural networks.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Patch-level routing in mixture-of-experts is provably sample-efficient for con- volutional neural networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:31.786419Z

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Observation c064f9ca-efb3-40d3-a962-3d6bf2eda3dc · outbound

This paper cites Reliable evalua- tion of adversarial robustness with an ensemble of diverse parameter-free attacks.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Reliable evalua- tion of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:31.562668Z

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Observation 25a8652f-cab8-4928-a5fc-36a1924cf60d · outbound

This paper cites DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 4

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Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Unresolved cited work

Reference 5

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Observation 5b6ab025-2c4c-40ed-8fd7-0e8798cb46ac · outbound

This paper cites Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fe- dus, Maarten P.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fe- dus, Maarten P

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:31.072376Z

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Observation 563068bf-01e5-4c48-88d1-4c80957b18f0 · outbound

This paper cites Learning Factored Representations in a Deep Mixture of Ex- perts.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Learning Factored Representations in a Deep Mixture of Ex- perts

Reference 7

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Observation c12f17cd-3d49-4ab7-ba91-abaeb454f26e · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with sim- ple and efficient sparsity.The Journal of Machine Learning Research, 2022.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Switch transformers: Scaling to trillion parameter models with sim- ple and efficient sparsity.The Journal of Machine Learning Research, 2022

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:30.686611Z

Source-reported events for the cited work

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Observation 9cac7396-c4c1-4362-9dd3-9225cbb90f8c · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 9

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Observation 971dda48-0775-475e-ac8c-51ab0cfe5cd8 · outbound

This paper cites Drawing robust scratch tickets: Subnetworks with inborn robustness are found within randomly initialized networks.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Drawing robust scratch tickets: Subnetworks with inborn robustness are found within randomly initialized networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:30.417725Z

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Observation 9fb3b2b4-2b61-4db9-bd86-52b6659b9d71 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Explaining and Harnessing Adversarial Examples

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:30.218810Z

Source-reported events for the cited work

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Observation c580b59a-c4d4-4ee6-a3ae-ff0f44d75c99 · outbound

This paper cites Sparse dnns with improved adversarial robustness.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Sparse dnns with improved adversarial robustness

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:30.032316Z

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Observation 73db129c-9ebd-4386-9983-eeec423adc6c · outbound

This paper cites Deep residual learning for image recognition.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Deep residual learning for image recognition

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:29.846665Z

Source-reported events for the cited work

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Observation 7bdbd632-5ffe-4b69-88ef-51ea549ff55a · outbound

This paper cites Jacobs, Michael I.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Jacobs, Michael I

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:29.679810Z

Source-reported events for the cited work

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Observation 290c5ba1-4528-464e-9ade-5b5dc9be14a8 · outbound

This paper cites Robustifying routers against in- put perturbations for sparse mixture-of-experts vision trans- formers.IEEE Open Journal of Signal Processing, 2025.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Robustifying routers against in- put perturbations for sparse mixture-of-experts vision trans- formers.IEEE Open Journal of Signal Processing, 2025

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:29.509483Z

Source-reported events for the cited work

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Observation ac72bffd-3c19-4583-a70b-595855b5add7 · outbound

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

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Learning multiple layers of features from tiny images

Reference 16

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Observation 16dc22ae-1d56-4d5f-963a-72eebb976e99 · outbound

This paper cites Gshard: Scaling giant models with conditional com- putation and automatic sharding.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Gshard: Scaling giant models with conditional com- putation and automatic sharding

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:29.330759Z

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Observation 8e6af4be-7bca-4997-8e5e-b7b5bd3fd1a5 · outbound

This paper cites Modeling task relationships in multi-task learning with multi-gate mixture-of-experts.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Modeling task relationships in multi-task learning with multi-gate mixture-of-experts

Reference 18

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Observation d05bb020-1f0d-4334-839e-2a7a69c178df · outbound

This paper cites Towards Deep Learn- ing Models Resistant to Adversarial Attacks.International Conference on Learning Representations (ICLR), 2018.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Towards Deep Learn- ing Models Resistant to Adversarial Attacks.International Conference on Learning Representations (ICLR), 2018

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:29.079388Z

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Observation 2ad716ba-f101-465b-8208-eae519353b72 · outbound

This paper cites Choosing smartly: Adaptive multimodal fusion for object detection in changing environments.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Choosing smartly: Adaptive multimodal fusion for object detection in changing environments

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:28.789203Z

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Observation 5589a1e6-e2a8-4991-bd13-890d3f2d36c2 · outbound

This paper cites Is temper- ature sample efficient for softmax gaussian mixture of ex- perts? InInternational Conference on Machine Learning (ICML), 2024.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Is temper- ature sample efficient for softmax gaussian mixture of ex- perts? InInternational Conference on Machine Learning (ICML), 2024

Reference 21

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Observation 0afc3513-158b-4cd7-8d66-072d9bd38c24 · outbound

This paper cites Marius Z¨ollner.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Marius Z¨ollner

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:28.217816Z

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This paper cites Mar- ius Z ¨ollner.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Mar- ius Z ¨ollner

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:28.060237Z

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Observation 040c1ebd-c80b-4ea3-b1ed-d200385808dc · outbound

This paper cites On the Adversarial Robustness of Mixture of Experts.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers On the Adversarial Robustness of Mixture of Experts

Reference 24

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Observation 2b50ea9e-abc7-41ab-9d44-9bf8e25096b4 · outbound

This paper cites DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next- Generation AI Scale.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next- Generation AI Scale

Reference 25

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Observation 0bf5521e-a68d-4665-86a9-5d82a8cf3b94 · outbound

This paper cites Scaling vision with sparse mix- ture of experts.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Scaling vision with sparse mix- ture of experts

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:27.439314Z

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Observation 352ab2da-29b9-4a26-a328-f72ad1cb4708 · outbound

This paper cites Le, Geoffrey E.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Le, Geoffrey E

Reference 27

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Observation ca2d1a11-6356-4faf-a607-8960108f69ed · outbound

This paper cites Goodfellow, and Rob Fergus.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Goodfellow, and Rob Fergus

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:27.018466Z

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Observation af15c498-e4a3-4f75-813e-d0bc53ed822f · outbound

This paper cites Convo- luted mixture of deep experts for robust semantic segmenta- tion.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Convo- luted mixture of deep experts for robust semantic segmenta- tion

Reference 29

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Observation 8afb7f30-1f97-4607-8217-9e85cb08a043 · outbound

This paper cites Gonzalez.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Gonzalez

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:26.869716Z

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Observation b36adb7c-a25d-4662-a606-32fe8b5dd342 · outbound

This paper cites ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing

Reference 31

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Observation bcf1c4c8-753b-473f-86b6-7d36f45fc4e4 · outbound

This paper cites LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes

Reference 32

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Source-reported events for the cited work

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Observation 93c217f8-2995-431f-b80c-c84a5279d64a · outbound

This paper cites Learning a mixture of granularity-specific experts for fine- grained categorization.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Learning a mixture of granularity-specific experts for fine- grained categorization

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-05T05:40:26.382238Z

Source-reported events for the cited work

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Observation ca501cd3-3e24-451a-ab7a-6f6f71389c1c · outbound

This paper cites Robust mixture-of-expert training for convo- lutional neural networks.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers Robust mixture-of-expert training for convo- lutional neural networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:26.740897Z

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