Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices | Paper Citation Record

Observation b4584e6c-e407-40e9-8f32-c3c1606869ec · outbound

This paper cites Integrating sensing and communications for ubiquitous iot: Applications, trends, and challenges,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Integrating sensing and communications for ubiquitous iot: Applications, trends, and challenges,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:27.514121Z

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Observation 0d8c7728-724d-4570-a9bc-069c44482f96 · outbound

This paper cites Machine learning-based 5g ran slicing for broadcasting ser- vices,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Machine learning-based 5g ran slicing for broadcasting ser- vices,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:27.333600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ccc81372-ec0a-42da-b1f1-2b0ebce3a995 · outbound

This paper cites Device-free wireless sensing for human detection: the deep learning perspective,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Device-free wireless sensing for human detection: the deep learning perspective,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:27.174777Z

Source-reported events for the cited work

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Observation 118ae798-83f8-4462-a0d8-39b743225750 · outbound

This paper cites Energy-efficient data collection and device positioning in uav- assisted iot,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Energy-efficient data collection and device positioning in uav- assisted iot,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:26.901019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5ec78a42-2e20-4f2a-851c-1009b5b25a4e · outbound

This paper cites Automatic modulation classification technique for radio monitoring,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Automatic modulation classification technique for radio monitoring,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:26.771570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 961bfaae-af0d-4482-9916-fea12e83116b · outbound

This paper cites Applica- tions of machine learning to cognitive radio networks,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Applica- tions of machine learning to cognitive radio networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:26.643041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3de7381c-fff1-489f-806f-deb817d86c96 · outbound

This paper cites Novel automatic modulation classification using cumulant features for communications via multipath channels,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Novel automatic modulation classification using cumulant features for communications via multipath channels,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:26.496827Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d16ac0b7-163f-4bf7-a598-eeddde332a0a · outbound

This paper cites Automatic modulation classification for cogni- tive radios using cyclic feature detection,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Automatic modulation classification for cogni- tive radios using cyclic feature detection,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:26.348483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9959250f-1dda-41db-afdb-2e144a87921f · outbound

This paper cites Au- tomatic modulation recognition of digital signals using wavelet features and svm,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Au- tomatic modulation recognition of digital signals using wavelet features and svm,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:26.186059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ab6f62b1-9570-4a22-8cba-ff1c83b49300 · outbound

This paper cites Hierarchical digital modulation classification using cumulants,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Hierarchical digital modulation classification using cumulants,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:26.039726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 509e3d7c-1d64-4ff5-8f47-bbb8fc8f265d · outbound

This paper cites Signal classification using statistical moments,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Signal classification using statistical moments,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:25.905193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 32b05dde-4073-4e15-a5fd-14925bc01fb5 · outbound

This paper cites Convolutional radio modulation recognition networks,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Convolutional radio modulation recognition networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:25.715860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 847fbee0-01c7-4e35-b63b-9da617a63db5 · outbound

This paper cites Over-the-air deep learning based radio signal classification,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Over-the-air deep learning based radio signal classification,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:25.576433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fc68ecae-1fed-4b16-b537-c5ff3d7bfad6 · outbound

This paper cites High-Capacity Complex Convolutional Neural Networks For I/Q Modulation Classification.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices High-Capacity Complex Convolutional Neural Networks For I/Q Modulation Classification

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-07T01:08:19.227125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e41f52e0-eb35-41e0-9d4a-191afc81b95f · outbound

This paper cites Sequential convolutional recurrent neural networks for fast automatic mod- ulation classification,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Sequential convolutional recurrent neural networks for fast automatic mod- ulation classification,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:25.427512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5fb691ee-df35-403e-8d95-14b76542df1c · outbound

This paper cites Fast Deep Learning for Automatic Modulation Classification.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Fast Deep Learning for Automatic Modulation Classification

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-07T01:08:18.926569Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bbcf34a0-d29d-4a23-a65b-a5bc6a0cd98a · outbound

This paper cites Multi- signal modulation classification using sliding window detection and complex convolutional network in frequency domain,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Multi- signal modulation classification using sliding window detection and complex convolutional network in frequency domain,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:25.264912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ed6307ba-50bc-4e72-9be2-f8268f88b385 · outbound

This paper cites A lightweight decentralized learning- based automatic modulation classification method for resource- constrained edge devices,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices A lightweight decentralized learning- based automatic modulation classification method for resource- constrained edge devices,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:25.129135Z

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Observation 46404753-7da5-467f-ad6d-9ec693572147 · outbound

This paper cites Automatic modulation classification based on decentralized learning and ensemble learning,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Automatic modulation classification based on decentralized learning and ensemble learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:24.959293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 33cb9b98-8268-4cb6-9198-73ae04761b76 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T01:08:15.574661Z

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Observation ee8131c6-356b-41fa-9dfe-89dc625212f9 · outbound

This paper cites Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:24.774824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c75a1ac2-b8da-4b2d-b1ff-aa7f1cd64fc0 · outbound

This paper cites Levit: a vision transformer in convnet’s clothing for faster inference,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Levit: a vision transformer in convnet’s clothing for faster inference,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:24.571854Z

Source-reported events for the cited work

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Observation 469716ff-c4b3-4fd3-be4c-26f15bf0b790 · outbound

This paper cites Mcformer: A transformer based deep neural network for automatic modulation classification,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Mcformer: A transformer based deep neural network for automatic modulation classification,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:24.411646Z

Source-reported events for the cited work

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Observation cab2c72c-d517-4236-8fb3-676026456a00 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Explaining and Harnessing Adversarial Examples

Reference 24

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Observation 44c893f8-4f55-431a-b4a9-c904e832cc37 · outbound

This paper cites Ac- cessorize to a crime: Real and stealthy attacks on state-of-the- art face recognition,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Ac- cessorize to a crime: Real and stealthy attacks on state-of-the- art face recognition,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:24.166565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 02af281a-ca99-4d75-8b22-8bd768bad648 · outbound

This paper cites Adversarial examples for semantic segmentation and object detection,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Adversarial examples for semantic segmentation and object detection,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:24.025251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7744d437-f59e-4872-81e3-03a953071644 · outbound

This paper cites Universal adversarial perturbations against semantic im- age segmentation,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Universal adversarial perturbations against semantic im- age segmentation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:23.743903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2de051a5-a840-48da-9c46-0704654ffaff · outbound

This paper cites Adversarial Examples for Evaluating Reading Comprehension Systems.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Adversarial Examples for Evaluating Reading Comprehension Systems

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T01:08:16.112322Z

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Observation db7884d3-bc86-4d0f-a795-8021f62ba4b5 · outbound

This paper cites Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-07T01:08:18.704658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5237c2fa-312f-4164-a095-59745c0efeba · outbound

This paper cites Adversarial attacks on deep- learning based radio signal classification,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Adversarial attacks on deep- learning based radio signal classification,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:23.429783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7749fc91-261d-4f64-8018-a13e723233ac · outbound

This paper cites Countermeasures against adversarial examples in radio signal classification,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Countermeasures against adversarial examples in radio signal classification,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:23.111055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 428972df-83fc-4ade-ab5a-54c831bb8350 · outbound

This paper cites Adversarial learning in transformer based neural network in radio signal classifi- cation,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Adversarial learning in transformer based neural network in radio signal classifi- cation,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:22.851179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 59e0b614-6267-4235-884b-3ccd4eaa1101 · outbound

This paper cites Access control and resource allocation for m2m communications in industrial automation,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Access control and resource allocation for m2m communications in industrial automation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:22.625940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 055ebbfc-b3b4-4b56-81a9-1897b490fbc6 · outbound

This paper cites Deep cognitive perspective: Resource allocation for noma-based heterogeneous iot with imperfect sic,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Deep cognitive perspective: Resource allocation for noma-based heterogeneous iot with imperfect sic,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:22.405318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 795f7cdf-a310-49ef-a144-38dc68750929 · outbound

This paper cites Energy- efficient resource allocation for d2d communications underlay- ing cloud-ran-based lte-a networks,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Energy- efficient resource allocation for d2d communications underlay- ing cloud-ran-based lte-a networks,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:22.106705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0fc789da-d158-450e-9611-782cccb9c7c5 · outbound

This paper cites Future intelligent and secure vehicular network toward 6g: Machine-learning approaches,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Future intelligent and secure vehicular network toward 6g: Machine-learning approaches,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:21.837552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e91046f1-21ee-478e-aa99-58daa17c8e41 · outbound

This paper cites Distillation as a defense to adversarial perturbations against deep neural networks,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Distillation as a defense to adversarial perturbations against deep neural networks,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:21.608748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ee269651-8d5c-437f-a2ea-0cb45ad3e646 · outbound

This paper cites Comdefend: An efficient image compression model to defend adversarial exam- ples,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Comdefend: An efficient image compression model to defend adversarial exam- ples,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:21.367874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cf8ec8e2-2f36-46c5-83fa-f426c6d8e9b6 · outbound

This paper cites Adversarial examples in the physical world,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Adversarial examples in the physical world,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:21.131451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 80fa8edb-4bcb-4391-9cc0-6895dfcd9b67 · outbound

This paper cites Char- acterizing adversarial subspaces using local intrinsic dimen- sionality,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Char- acterizing adversarial subspaces using local intrinsic dimen- sionality,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:20.861848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 58f2ba5a-8bef-4bed-be2e-03c5c987b9b8 · outbound

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

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Towards deep learning models resistant to adversarial attacks,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T01:08:17.002811Z

Source-reported events for the cited work

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Observation 3de2dda0-8f7e-4c8f-b5ea-30d6f644f339 · outbound

This paper cites Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T01:08:17.082748Z

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Unavailable: canonical work link unavailable.

Observation e66da926-8b3b-4f87-80ca-1ec5347b6760 · outbound

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

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:20.671562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4cf0269c-bd77-43dd-a381-ea4a0f8d5277 · outbound

This paper cites Adversari- ally robust distillation,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Adversari- ally robust distillation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:20.438361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e3b9af4c-8804-4c5e-bba1-d34ead4aa53d · outbound

This paper cites Reliable Adversarial Distillation with Unreliable Teachers.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Reliable Adversarial Distillation with Unreliable Teachers

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T01:08:17.296105Z

Source-reported events for the cited work

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Observation 90b4423f-0c5a-48f3-8fd1-d685d05caf56 · outbound

This paper cites Robust overfitting may be mitigated by properly learned smoothening,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Robust overfitting may be mitigated by properly learned smoothening,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T01:08:17.385818Z

Source-reported events for the cited work

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Observation c15b998b-afe1-488b-b62f-6efdd8f364e6 · outbound

This paper cites Revisiting adversarial robustness distillation: Robust soft labels make student better,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Revisiting adversarial robustness distillation: Robust soft labels make student better,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:20.267400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dd107b07-ebda-4eae-b88c-5433543f830d · outbound

This paper cites Layer Normalization.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Layer Normalization

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T01:08:17.608495Z

Source-reported events for the cited work

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Observation 0545447c-29b6-4af2-90bf-a372ff56e448 · outbound

This paper cites Deep residual learning for image recognition,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Deep residual learning for image recognition,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T01:08:17.682464Z

Source-reported events for the cited work

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Observation ddf7c06b-8329-4dfb-b184-994a72796b24 · outbound

This paper cites Attention is all you need,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Attention is all you need,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T01:08:17.805030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b41ca3ee-7496-4445-8e02-ab269cb68d64 · outbound

This paper cites Wild patterns: Ten years after the rise of adversarial machine learning,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Wild patterns: Ten years after the rise of adversarial machine learning,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:20.067396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 52c3ed65-7519-424a-ac12-a7d7b3e9ba20 · outbound

This paper cites Radio Machine Learning Dataset Generation with GNU Radio,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Radio Machine Learning Dataset Generation with GNU Radio,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:19.831682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5c4fb780-9560-449a-b498-d1cc74ae421c · outbound

This paper cites Automatic modulation classification: Cauchy-score-function-based cyclic correlation spectrum and fc-mlp under mixed noise and fading channels,.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Automatic modulation classification: Cauchy-score-function-based cyclic correlation spectrum and fc-mlp under mixed noise and fading channels,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:19.650251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0e482dbd-887e-4f54-8571-746f778e5e28 · outbound

This paper cites Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T01:08:18.307665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f37b86d-2009-4c57-ae6e-b29fba689cb3 · outbound

This paper cites degree in mathematics from Guangxi University, Guangxi, China, in 1985, the M.S.

Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices degree in mathematics from Guangxi University, Guangxi, China, in 1985, the M.S

Reference 1963

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:08:19.418345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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