ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices | Paper Citation Record

Observation 25a20a7d-7389-4758-b2f3-b7ffcb0e404b · outbound

This paper cites Transformers: State- of-the-art natural language processing,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Transformers: State- of-the-art natural language processing,

Reference 1

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Observation 78f5dc7e-97f4-4232-bfa8-a89905c85e22 · outbound

This paper cites Pytorch image models,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Pytorch image models,

Reference 2

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Observation 453490b0-8724-4f40-b343-ae7e865f5902 · outbound

This paper cites Deep residual learning for image recognition,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Deep residual learning for image recognition,

Reference 3

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Observation 492dec3a-dc28-40ac-a70e-e9f977cd2ae4 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 4

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Observation 9c8bc8ae-c78e-4d50-93db-3a42d7784ee4 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 5

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Observation 34f6cfed-5d39-44d8-a966-5a652d12d8e2 · outbound

This paper cites Training data-efficient image transformers & distillation through attention,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Training data-efficient image transformers & distillation through attention,

Reference 6

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Observation 9fd6af6a-6143-4335-b312-d56ea95f91d2 · outbound

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

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

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Observation edd6f453-53ab-42f7-a600-4b7c82157375 · outbound

This paper cites Davit: Dual attention vision transformers,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Davit: Dual attention vision transformers,

Reference 8

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Observation dd6526b4-ba13-4ca0-abf0-00c904c02b9d · outbound

This paper cites Hiera: A hierarchi- cal vision transformer without the bells-and-whistles,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Hiera: A hierarchi- cal vision transformer without the bells-and-whistles,

Reference 9

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Observation 01b3f301-febf-4dc9-99d1-199333a380e0 · outbound

This paper cites Scalable vision transformers with hierarchical pooling,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Scalable vision transformers with hierarchical pooling,

Reference 10

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Observation d9a51a62-1922-4f16-a1ca-7888269cc5a5 · outbound

This paper cites Understanding the dynamics of dnns using graph modularity,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Understanding the dynamics of dnns using graph modularity,

Reference 11

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Observation 4aa87eda-9bb3-4d1b-aa55-ea4db0187268 · outbound

This paper cites A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Models.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Models

Reference 12

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Observation feb04c80-797e-413a-ac72-1b7e6e4b4828 · outbound

This paper cites RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively

Reference 13

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Observation da2ac9fd-c990-4834-8092-c2587bef2f28 · outbound

This paper cites Sglp: A similarity guided fast layer partition pruning for compressing large deep models,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Sglp: A similarity guided fast layer partition pruning for compressing large deep models,

Reference 14

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Observation cdcccab2-eb25-42d3-9cea-3434fc5a8c19 · outbound

This paper cites FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition

Reference 15

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Observation 3e6dc251-eea9-49d5-8281-32332f1a2b18 · outbound

This paper cites SepPrune: Structured Pruning for Efficient Deep Speech Separation.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices SepPrune: Structured Pruning for Efficient Deep Speech Separation

Reference 16

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Observation e82cb4d2-288f-4c0f-a32e-967c90a39f45 · outbound

This paper cites Reassessing Layer Pruning in LLMs: New Insights and Methods.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Reassessing Layer Pruning in LLMs: New Insights and Methods

Reference 17

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Observation c00a43f1-3203-48e5-9ee7-753a25707f0f · outbound

This paper cites Distilling the Knowledge in a Neural Network.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Distilling the Knowledge in a Neural Network

Reference 19

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Observation 494529f4-e3d7-4fe9-8f99-714b6260b654 · outbound

This paper cites A semi-supervised federated learning scheme via knowledge distillation for intrusion detection,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices A semi-supervised federated learning scheme via knowledge distillation for intrusion detection,

Reference 20

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Observation 6a487a56-c640-4234-8f7d-a9af8952bd4f · outbound

This paper cites Knowledge distillation: A survey,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Knowledge distillation: A survey,

Reference 21

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Observation f5984013-7f4c-442e-8447-c806dbc1993b · outbound

This paper cites On the efficacy of knowledge distillation,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices On the efficacy of knowledge distillation,

Reference 22

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Observation 73f6b62d-cb70-4af5-bbb2-b532a3d67e9c · outbound

This paper cites Knowledge distillation from a stronger teacher,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Knowledge distillation from a stronger teacher,

Reference 23

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Observation c21bf622-c23a-4cab-b558-aa5b34981b8d · outbound

This paper cites Q-vit: Accurate and fully quantized low-bit vision transformer,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Q-vit: Accurate and fully quantized low-bit vision transformer,

Reference 24

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Observation 9c78b94d-0259-4719-8dc9-e21634321c64 · outbound

This paper cites PTQD: Accurate Post-Training Quantization for Diffusion Models.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices PTQD: Accurate Post-Training Quantization for Diffusion Models

Reference 25

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Observation 09112e2d-fb7f-460e-b6c2-69415edd50ef · outbound

This paper cites Bit- shrinking: Limiting instantaneous sharpness for improving post-training quantization,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Bit- shrinking: Limiting instantaneous sharpness for improving post-training quantization,

Reference 26

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Observation 816cc52b-fe6f-483d-9114-171ee853ab2d · outbound

This paper cites Post-training quantization for vision transformer,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Post-training quantization for vision transformer,

Reference 27

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Observation a4662401-df3e-4513-9087-d8e98b6b8d64 · outbound

This paper cites Zeroq: A novel zero shot quantization framework,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Zeroq: A novel zero shot quantization framework,

Reference 28

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Observation b34b4f5e-ebcb-4138-893b-bb91764e246e · outbound

This paper cites Quantization networks,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Quantization networks,

Reference 29

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Observation fd8d99f2-f9a3-45a7-82d1-6e90b67dfbd9 · outbound

This paper cites Understanding image representations by measuring their equivariance and equivalence,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Understanding image representations by measuring their equivariance and equivalence,

Reference 30

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Observation c71fb885-cfdb-420f-818d-fec19488379e · outbound

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

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Learning multiple layers of features from tiny images,

Reference 31

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Observation ed47a69c-14d8-4e5d-9768-a5f5539f8744 · outbound

This paper cites Describing textures in the wild,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Describing textures in the wild,

Reference 32

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Observation 8de53164-65c9-4f60-a0a5-5fd1739cab19 · outbound

This paper cites Cats and dogs,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Cats and dogs,

Reference 33

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Observation d9bc112b-72c5-451a-b786-6ec7942d4ee2 · outbound

This paper cites Imagenette: A smaller subset of 10 easily classified classes from imagenet,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Imagenette: A smaller subset of 10 easily classified classes from imagenet,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:44:17.536144Z

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Observation 2e177c52-dc86-40cf-903a-66727acf54e6 · outbound

This paper cites Performance-optimized hierarchical models predict neural responses in higher visual cortex,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Performance-optimized hierarchical models predict neural responses in higher visual cortex,

Reference 35

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Observation ef5c0a48-4a9d-4525-8487-69cb02702b4a · outbound

This paper cites Canonical correlation analysis: An overview with application to learning methods,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Canonical correlation analysis: An overview with application to learning methods,

Reference 36

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Observation 36965ecb-57f2-4f0e-a824-7fb757dfffa3 · outbound

This paper cites Svcca: singular vector canonical correlation analysis for deep learning dynamics and interpretability,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Svcca: singular vector canonical correlation analysis for deep learning dynamics and interpretability,

Reference 37

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Observation 4d405b14-feec-4849-924a-21c0728602b6 · outbound

This paper cites Similarity of neural network representations revisited,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Similarity of neural network representations revisited,

Reference 38

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Observation 15625643-8c78-4224-98cf-7f5e97a4e0d3 · outbound

This paper cites Graph- based similarity of deep neural networks,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Graph- based similarity of deep neural networks,

Reference 39

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Observation 7cc1f426-3607-476a-b2ef-256823fe9071 · outbound

This paper cites Similarity-preserving knowledge distillation,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Similarity-preserving knowledge distillation,

Reference 40

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Observation e9380d3a-b8c5-4687-bdff-36f7e2c802fa · outbound

This paper cites Deconfounded repre- sentation similarity for comparison of neural networks,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Deconfounded repre- sentation similarity for comparison of neural networks,

Reference 41

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Observation b86d331c-53c3-4292-933a-66791727f9ad · outbound

This paper cites Representation similarity analysis for efficient task taxonomy & transfer learning,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Representation similarity analysis for efficient task taxonomy & transfer learning,

Reference 42

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Observation 4409cffc-05a7-400c-a12e-324bf48083d3 · outbound

This paper cites Similarity and matching of neural network representations,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Similarity and matching of neural network representations,

Reference 43

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Observation f08d4fb5-6444-4802-bc40-15c7a39bdfa9 · outbound

This paper cites Revisiting model stitching to compare neural representations,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Revisiting model stitching to compare neural representations,

Reference 44

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Observation 3534d411-7f0c-4e82-8175-a40d15f136fb · outbound

This paper cites How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

Reference 45

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Observation 202dd913-b646-4534-aef5-50984e4f9d85 · outbound

This paper cites Stitchable neural networks,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Stitchable neural networks,

Reference 46

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Observation c2253dbc-ca6f-4d3a-a2c1-8ce115fabc72 · outbound

This paper cites Measuring statistical dependence with hilbert-schmidt norms,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Measuring statistical dependence with hilbert-schmidt norms,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:44:14.042519Z

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Observation 4438524a-f284-4231-bd32-f50335fcaa1e · outbound

This paper cites Low-resource scenario classification through model pruning towards refined edge intelligence,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Low-resource scenario classification through model pruning towards refined edge intelligence,

Reference 48

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Observation c096fb8c-6f30-444c-883c-d2d4b6aad197 · outbound

This paper cites Mobilenet and knowledge distillation-based automatic scenario recognition method in vehicle-to- vehicle systems,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Mobilenet and knowledge distillation-based automatic scenario recognition method in vehicle-to- vehicle systems,

Reference 49

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Observation 21a913af-cd5c-4e51-a870-dcd70ac0f00c · outbound

This paper cites An improved neural network pruning technology for automatic modulation classification in edge devices,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices An improved neural network pruning technology for automatic modulation classification in edge devices,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:44:13.475840Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 33285729-e4a4-40b3-ada4-697591157c13 · outbound

This paper cites Glr-sei: green and low resource specific emitter identification based on complex networks and fisher pruning,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Glr-sei: green and low resource specific emitter identification based on complex networks and fisher pruning,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:44:13.155020Z

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Observation d6cc3c53-ef9a-4055-992e-0d5b45e78a09 · outbound

This paper cites Rgp: Neural network pruning through regular graph with edges swap- ping,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Rgp: Neural network pruning through regular graph with edges swap- ping,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:44:12.863674Z

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Observation b2154d8e-1418-4dde-a345-476cd4b5a6f4 · outbound

This paper cites Lightweight automatic modulation classification via progres- sive differentiable architecture search,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Lightweight automatic modulation classification via progres- sive differentiable architecture search,

Reference 53

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Observation 0eb654eb-c530-4358-b0bd-5c47cd3abd1b · outbound

This paper cites Complex-valued networks for automatic modulation classification,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Complex-valued networks for automatic modulation classification,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:44:12.579964Z

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Observation 5ed0e493-5f71-4aac-b93b-cf4bf6e729f8 · outbound

This paper cites Surgical Fine-Tuning Improves Adaptation to Distribution Shifts.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Surgical Fine-Tuning Improves Adaptation to Distribution Shifts

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T05:44:11.207780Z

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Observation 4791e96c-7d95-4bc7-bd66-52539162fa09 · outbound

This paper cites Backdoor Pre-trained Models Can Transfer to All.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Backdoor Pre-trained Models Can Transfer to All

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T05:44:11.309534Z

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Observation b5d58d80-afb1-4dbb-b82e-e560b67f09e2 · outbound

This paper cites Semantic rela- tion reasoning for shot-stable few-shot object detection,.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Semantic rela- tion reasoning for shot-stable few-shot object detection,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:44:12.308776Z

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.

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