Dynamic Neural Networks: A Survey

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

Long Short-Term Memory

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Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566

Squeeze-and-Excitation Networks

Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667

CBAM: Convolutional Block Attention Module

Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_1 · Cited by 25,853

Language Models are Few-Shot Learners

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372

HADAS: Hardware-Aware Dynamic Neural Architecture Search for Edge Performance Scaling

Bouzidi, H., Odema, M., Ouarnoughi, H., Faruque, M. A. A., & Niar, S. (2023) · Design, Automation, and Test in Europe · doi:10.23919/date56975.2023.10137095 · Cited by 36

A dynamic inference network (DI-Net) for online fabric defect detection in smart manufacturing

Zhao, S., Zhong, R. Y., Xu, C., Wang, J., & Zhang, J. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02387-2 · Cited by 35

Prompt-Driven Dynamic Object-Centric Learning for Single Domain Generalization

Li, D., Wu, A., Wang, Y., & Han, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01667 · Cited by 35

Graph similarity learning for change-point detection in dynamic networks

Sulem, D., Kenlay, H., Cucuringu, M., & Dong, X. (2023) · Machine Learning · doi:10.1007/s10994-023-06405-x · Cited by 35

I3D: Transformer Architectures with Input-Dependent Dynamic Depth for Speech Recognition

Peng, Y., Lee, J., & Watanabe, S. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096662 · Cited by 34

A Lightweight and Dynamic Open-Set Intrusion Detection for Industrial Internet of Things

Yang, X., Tong, F., Jiang, F. M., & Cheng, G. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3546849 · Cited by 34

Multi-Scale MLP-Mixer for image classification

Zhang, H., Dong, Z., Li, B., & He, S. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109792 · Cited by 34

MADTP: Multimodal Alignment-Guided Dynamic Token Pruning for Accelerating Vision-Language Transformer

Cao, J., Ye, P., Li, S., Yu, C., Tang, Y., Lu, J., & Chen, T. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01487 · Cited by 33

DyFADet: Dynamic Feature Aggregation for Temporal Action Detection

Yang, L., Zheng, Z., Han, Y., Cheng, H., Song, S., Huang, G. M., & Li, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72952-2_18 · Cited by 33

GRA: Detecting Oriented Objects Through Group-Wise Rotating and Attention

Wang, J., Pu, Y., Han, Y., Guo, J., Wang, Y., Li, X., & Huang, G. M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72643-9_18 · Cited by 33

Temporal Dynamic Convolutional Neural Network for Text-Independent Speaker Verification and Phonemic Analysis

Kim, S.-H., Nam, H., & Park, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747421 · Cited by 33

Responsible AI starts with the artifact: Challenging the concept of responsible AI in IS research

Mikalef, P., Benlian, A., Conboy, K., & Tarafdar, M. (2025) · European Journal of Information Systems · doi:10.1080/0960085x.2025.2506875 · Cited by 33

DNNShifter: An efficient DNN pruning system for edge computing

Eccles, B. J., Rodgers, P., Kilpatrick, P., Spence, I. T. A., & Varghese, B. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.09.025 · Cited by 32

Dyformer: A dynamic transformer-based architecture for multivariate time series classification

Yang, C., Wang, X., Yao, L., Long, G., & Xu, G. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119881 · Cited by 32

Dynamic Neural Network-Based Resource Management for Mobile Edge Computing in 6G Networks

Ma, L., Cheng, N., Zhou, C., Wang, X., Lu, N., Zhang, N., Aldubaikhy, K., & Alqasir, A. (2023) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2023.3346824 · Cited by 32

Latency-Aware Unified Dynamic Networks for Efficient Image Recognition

Han, Y., Liu, Z., Yuan, Z., Pu, Y., Wang, C., Song, S., & Huang, G. M. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3393530 · Cited by 32

Multiomics dynamic learning enables personalized diagnosis and prognosis for pancancer and cancer subtypes

Lu, Y., Peng, R., Dong, L., Xia, K., Wu, R., Xu, S., & Wang, J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad378 · Cited by 31

Task2Sim: Towards Effective Pre-training and Transfer from Synthetic Data

Mishra, S., Panda, R., Phoo, C. P., Chen, C.-F. R., Karlinsky, L., Saenko, K., Saligrama, V., & Feris, R. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00898 · Cited by 31

Adaptive Convolutional Subspace Reasoning Network for Few-Shot SAR Target Recognition

Ren, H., Yu, X., Liu, S., Zou, L., Wang, X., & Tang, H. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3288987 · Cited by 31

Cache-based GNN System for Dynamic Graphs

Li, H., & Chen, L. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482237 · Cited by 30

Sparser spiking activity can be better: Feature Refine-and-Mask spiking neural network for event-based visual recognition

Yao, M., Zhang, H., Zhao, G., Zhang, X., Wang, D., Cao, G., & Li, G. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.07.008 · Cited by 30

PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation

Zheng, N., Jiang, H., Zhang, Q., Han, Z., Ma, L., Yang, Y., Yang, F., Zhang, C., Qiu, L., Yang, M., et al. (2023) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3600006.3613139 · Cited by 30

A Hypernetwork Based Framework for Non-Stationary Channel Prediction

Liu, G., Hu, Z., Wang, L., Zhang, H., Xue, J., & Matthaiou, M. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3355557 · Cited by 29

AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and Pruning

Zhong, Y., Liu, Z., Li, Y., & Wang, L. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01877 · Cited by 32

Dynamic Slimmable Denoising Network

Jiang, Z., Li, C., Chang, X., Chen, L., Zhu, J., & Yang, Y. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3246792 · Cited by 29

DSE-GAN: Dynamic Semantic Evolution Generative Adversarial Network for Text-to-Image Generation

Huang, M., Mao, Z., Wang, P., Wang, Q., & Zhang, Y. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547881 · Cited by 29

Fixing Overconfidence in Dynamic Neural Networks

Meronen, L., Trapp, M., Pilzer, A., Yang, L., & Solin, A. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00266 · Cited by 29

Scalable frame resolution for efficient continuous sign language recognition

Hu, L., Gao, L., Liu, Z., & Feng, W. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109903 · Cited by 28

Learning multi-granular worker intentions from incomplete visual observations for worker-robot collaboration in construction

Pan, Z., & Yu, Y. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105184 · Cited by 28

DNA: A General Dynamic Neural Network Accelerator

Liu, L., Yu, J., Wang, M., Li, X., Han, Y., & Wang, Y. (2025) · IEEE Transactions on Computers · doi:10.1109/tc.2025.3587617 · Cited by 28

Controllable Dynamic Multi-Task Architectures

Raychaudhuri, D. S., Suh, Y., Schulter, S., Yu, X., Faraki, M., Roy–Chowdhury, A. K., & Chandraker, M. K. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01068 · Cited by 28

Robust Haze and Thin Cloud Removal via Conditional Variational Autoencoders

Ding, H., Xie, F., Qiu, L., Zhang, X., & Shi, Z. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3349779 · Cited by 28

A Crowd-Ai Dynamic Neural Network Hyperparameter Optimization Approach for Image-Driven Social Sensing Applications

Zhang, Y., Zong, R., Shang, L., & Wang, D. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110864 · Cited by 27

Unsupervised Degradation Representation Learning for Unpaired Restoration of Images and Point Clouds

Wang, L., Guo, Y., Wang, Y., Dong, X., Xu, Q., Yang, J., & An, W. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3471571 · Cited by 27

Autoencoding With a Classifier System

Preen, R. J., Wilson, S. W., & Bull, L. (2021) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2021.3079320 · Cited by 27

Token Dropping for Efficient BERT Pretraining

Hou, L., Pang, R. Y., Zhou, T., Wu, Y., Song, X., Song, X., & Zhou, D. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.262 · Cited by 27

Human-inspired spatiotemporal feature extraction and fusion network for weather forecasting

Wu, H., Liang, Y., & Zuo, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118089 · Cited by 27

Spatial–Spectral Cross-Correlation Embedded Dual-Transfer Network for Object Tracking Using Hyperspectral Videos

Lei, J., Liu, P., Xie, W., Gao, L., Li, Y., & Du, Q. (2022) · Remote Sensing · doi:10.3390/rs14153512 · Cited by 27

EVA$^{2}$: Event-Assisted Video Frame Interpolation via Cross-Modal Alignment and Aggregation

Xiao, Z., Weng, W., Zhang, Y., & Xiong, Z. (2022) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2022.3228747 · Cited by 27

Hybrid Series/Parallel All-Nonlinear Dynamic-Static Neural Networks: Development, Training, and Application to Chemical Processes

Mukherjee, A., & Bhattacharyya, D. (2023) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.2c03339 · Cited by 27

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications

Sun, J., Xu, M., Ru, J., James-Bott, A., Xiong, D., Wang, X., & Cribbs, A. P. (2023) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2023.115500 · Cited by 27

Temporal and Topological Augmentation-based Cross-view Contrastive Learning Model for Temporal Link Prediction

Li, D., Tan, S., Wang, Y., Funakoshi, K., & Okumura, M. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615231 · Cited by 26

Neural network developments: A detailed survey from static to dynamic models

Verma, P. R., Singh, N. P., Pantola, D., & Cheng, X. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109710 · Cited by 26

Demystifying Local Vision Transformer: Sparse Connectivity, Weight Sharing, and Dynamic WeightPreprint

Han, Q., Fan, Z., Dai, Q., Sun, L., Cheng, M., Liu, J., & Wang, J. (2021) · arXiv (Cornell University) · Cited by 26

CADFormer: Fine-Grained Cross-Modal Alignment and Decoding Transformer for Referring Remote Sensing Image Segmentation

Liu, M., Jiang, X., & Zhang, X. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3576595 · Cited by 25

Pre-training Methods in Information RetrievalPreprint

Fan, Y., Xie, X., Cai, Y., Chen, J., Ma, X., Li, X., Zhang, R., & Guo, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.13853 · Cited by 25

SpaceByte: Towards Deleting Tokenization from Large Language Modeling

Slagle, K. (2024) · neural information processing systems · doi:10.52202/079017-3967 · Cited by 24

ClassyNet: Class-Aware Early-Exit Neural Networks for Edge Devices

Ayyat, M., Nadeem, T., & Krawczyk, B. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3344120 · Cited by 24

Multi-Exit Vision Transformer for Dynamic InferencePreprint

Bakhtiarnia, A., Zhang, Q., & Iosifidis, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.15183 · Cited by 24

Graph Neural Networks with Diverse Spectral Filtering

Guo, J., Huang, K., Yi, X., & Zhang, R. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583324 · Cited by 23

Lifting the Curse of Capacity Gap in Distilling Language Models

Zhang, C., Yang, Y., Liu, J., Wang, J., Xian, Y., Wang, B., & Song, D. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.249 · Cited by 23

A Stitch in Time Saves Nine: Small VLM is a Precise Guidance for Accelerating Large VLMs

Zhao, W., Han, Y., Tang, J., Li, Z., Song, Y., Wang, K., Wang, Z., & You, Y. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01845 · Cited by 23

ART: Anonymous Region Transformer for Variable Multi-Layer Transparent Image Generation

Pu, Y., Zhao, Y., Tang, Z., Yin, R., Ye, H., Yuan, Y., Chen, D., Bao, J., Zhang, S., Wang, Y., et al. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00745 · Cited by 22

A comprehensive review of lightweight deep learning models for edge computing with future directions

Lai, N., Dewi, D. A., Maidin, S. S., Xiao, W., Zhao, S., & Hu, Q. (2026) · Discover Computing · doi:10.1007/s10791-026-10021-3 · Cited by 22

DANet: Image Deraining via Dynamic Association Learning

Jiang, K., Wang, Z., Wang, Z., Yi, P., Jiang, J., Xiao, J., & Lin, C. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/137 · Cited by 22

AdaFocusV3: On Unified Spatial-Temporal Dynamic Video Recognition

Wang, Y., Yue, Y., Xu, X., Hassani, A., Kulikov, V., Orlov, N., Song, S., Shi, H., & Huang, G. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19772-7_14 · Cited by 22

Boosted Dynamic Neural Networks

Yu, H., Li, H., Hua, G., Huang, G. M., & Shi, H. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i9.26302 · Cited by 22

In-Situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networks

Huang, K., Hai, W., Liu, Z., & Qi, X. (2023) · IEEE Wireless Communications · doi:10.1109/mwc.010.2200546 · Cited by 22

Dynamic Graph Information Bottleneck

Yuan, H., Sun, Q., Fu, X., Ji, C., & Li, J. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645411 · Cited by 22

Efficient Visual Recognition: A Survey on Recent Advances and Brain-inspired Methodologies

Wu, Y., Wang, D., Lu, X., Yang, F., Yao, M., Dong, W., Shi, J., & Li, G. (2022) · Machine Intelligence Research · doi:10.1007/s11633-022-1340-5 · Cited by 22

Spatially Adaptive Feature Refinement for Efficient Inference

Han, Y., Huang, G. M., Song, S., Yang, L., Zhang, Y., & Jiang, H. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3125263 · Cited by 22

Advanced Design for High-Performance and AI Chips

Cao, Y., Chen, Y., Fan, X., Fu, H. C., & Xu, B. (2025) · Nano-Micro Letters · doi:10.1007/s40820-025-01850-w · Cited by 21

NSNet: Non-saliency Suppression Sampler for Efficient Video Recognition

Xia, B., Wu, W., Wang, H., Su, R., He, D., Yang, H., Fan, X., & Ouyang, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19830-4_40 · Cited by 21

EvAnGCN: Evolving Graph Deep Neural Network Based Anomaly Detection in Blockchain

Patel, V., Rajasegarar, S., Pan, L., Liu, J., & Zhu, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-22064-7_32 · Cited by 21

A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

Butola, R., Li, Y., & Kola, S. R. (2023) · IEEE Open Journal of Nanotechnology · doi:10.1109/ojnano.2023.3328425 · Cited by 21

Inference latency prediction for CNNs on heterogeneous mobile devices and ML frameworks

Li, Z., Paolieri, M., & Golubchik, L. (2024) · Performance Evaluation · doi:10.1016/j.peva.2024.102429 · Cited by 21

Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches

Wu, Y., Bai, D., Zhang, K., Li, Y., & Yang, F. (2025) · Journal of Materials Informatics · doi:10.20517/jmi.2024.84 · Cited by 20

A Dual-System Vision-Language-Action Model for Rational Manipulation

Song, W., Chen, J., Li, W., He, X., Zheng, X., Liu, Z., Wang, H., & Li, H. (2026) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2026.3658581 · Cited by 20

3D-Pruning: A Model Compression Framework for Efficient 3D Action Recognition

Guo, J., Liu, J., & Xu, D. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3197395 · Cited by 20

Intra-graph and Inter-graph joint information propagation network with third-order text graph tensor for fake news detection

Cui, B., Ma, K., Li, L., Zhang, W., Ji, K., Chen, Z., & Abraham, A. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04455-1 · Cited by 20

Learn by Oneself: Exploiting Weight-Sharing Potential in Knowledge Distillation Guided Ensemble Network

Zhao, Q., Lyu, S., Chen, L., Liu, B., Xu, T.-B., Cheng, G., & Feng, W. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3267115 · Cited by 20

Time-Aware Random Walk Diffusion to Improve Dynamic Graph Learning

Lee, J.-W., & Jung, J. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i7.26021 · Cited by 20

A Review of Trajectory Prediction Methods for the Vulnerable Road User

Schuetz, E., & Flohr, F. B. (2023) · Robotics · doi:10.3390/robotics13010001 · Cited by 20

Earthquake monitoring using deep learning with a case study of the Kahramanmaras Turkey earthquake aftershock sequence

Li, W., Chakraborty, M., Köhler, J., Quinteros-Cartaya, C., Rümpker, G., & Srivastava, N. (2024) · Solid Earth · doi:10.5194/se-15-197-2024 · Cited by 20

Evaluating Wind Speed Forecasting Models: A Comparative Study of CNN, DAN2, Random Forest and XGBOOST in Diverse South African Weather Conditions

Mugware, F. W., Sigauke, C., & Ravele, T. (2024) · Forecasting · doi:10.3390/forecast6030035 · Cited by 20

Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic SegmentationPreprint

Chen, R., Rong, Y., Guo, S., Han, J., Sun, F., Xu, T., & Huang, W. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2203.07988 · Cited by 20

Effective Multi-Species weed detection in complex wheat fields using Multi-Modal and Multi-View image fusion

Xu, K., Xie, Q., Zhu, Y. P., Cao, W., & Ni, J. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.109924 · Cited by 19

Convolutional neural networks and mixture of experts for intrusion detection in 5G networks and beyond

Ilias, L., Doukas, G. S., Lamprou, V., Ntanos, C., & Askounis, D. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1708953 · Cited by 19

When Does Confidence-Based Cascade Deferral Suffice?

Jitkrittum, W., Gupta, N., Menon, A. K., Narasimhan, H., Rawat, A. S., & Kumar, S. (2023) · neural information processing systems · doi:10.52202/075280-0431 · Cited by 19

Alternative Auto-Encoder for State Estimation in Distribution Systems With Unobservability

Sundaray, P., & Weng, Y. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3204524 · Cited by 19

Discussion on a new paradigm of endogenous security towards 6G networks

Ji, X., Wu, J., Jin, L., Huang, K., Chen, Y., Sun, X., You, W., Huo, S., & Yang, J. (2022) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2200060 · Cited by 19

Dynamically evolving deep neural networks with continuous online learning

Zhong, Y., Zhou, J., Li, P., & Gong, J. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119411 · Cited by 19

LDCNet: Lightweight dynamic convolution network for laparoscopic procedures image segmentation

Yin, Y., Luo, S., Zhou, J., Kang, L., & Chen, C. Y. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.11.055 · Cited by 19

Conditional computation in neural networks: Principles and research trends

Scardapane, S., Baiocchi, A., Devoto, A., Marsocci, V., Minervini, P., & Pomponi, J. (2024) · Intelligenza Artificiale · doi:10.3233/ia-240035 · Cited by 19

Dynamic Stream Selection Network for Subject-Independent EEG-Based Emotion Recognition

Li, W., Dong, J., Liu, S., Fan, L., & Wang, S. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3390799 · Cited by 19

Uni-AdaFocus: Spatial-Temporal Dynamic Computation for Video Recognition

Wang, Y., Zhang, H., Yue, Y., Song, S., Deng, C., Feng, J., & Huang, G. M. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3514654 · Cited by 19

Architecting Efficient Multi-modal AIoT Systems

Hou, X., Liu, J., Tang, X., Li, C., Chen, J., Liang, L., Cheng, K.-T. T., & Guo, M. (2023) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3579371.3589066 · Cited by 19

Online V2X Scheduling for Raw-Level Cooperative Perception

Jia, Y., Mao, R., Sun, Y., Zhou, S., & Niu, Z. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9838439 · Cited by 18

Dynamic neural networks: advantages and challenges

Huang, G. M. (2024) · National Science Review · doi:10.1093/nsr/nwae088 · Cited by 18

Efficient analysis of deep neural networks for vision via biologically-inspired receptive field angles: An in-depth survey

Ma, Y., Meng, Y., Lin, H., Liu, C., Hu, M., & Song, Q. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102582 · Cited by 18

Energy-Efficient Inference With Software–Hardware Co-Design for Sustainable Artificial Intelligence of Things

Dai, S., Luo, Z., Luo, W., Wang, S., Dai, C., Guo, B., & Zhou, X. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3482288 · Cited by 18

Uncertainty-Guided Refinement for Fine-Grained Salient Object Detection

Yuan, Y., Gao, P., Dai, Q., Qin, J., & Xiang, W. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3557562 · Cited by 18

Knowledge transfer evolutionary search for lightweight neural architecture with dynamic inference

Qian, X., Liu, F., Jiao, L., Zhang, X., Huang, X., Li, S., Chen, P., & Liu, X. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109790 · Cited by 18

Dynamic Grained Encoder for Vision TransformersPreprint

Song, L., Zhang, S., Liu, S., Li, Z., He, X., Sun, H., Sun, J. Z., & Zheng, N. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2301.03831 · Cited by 18

From multi-scale grids to dynamic regions: Dual-relation enhanced transformer for image captioning

Zhou, W., Song, C., Chen, D., Su, T., Hu, H., & Shan, C. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113127 · Cited by 17

DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization

Chen, S., Wei, S., Liu, C., & Yang, W. P. (2023) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3597926.3598082 · Cited by 16

A resource-efficient ECG diagnosis model for mobile health devices

Tao, R., Wang, L., & Wu, B. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119628 · Cited by 16

Improving the Robustness of Transformer-based Large Language Models with Dynamic Attention

Shen, L., Pu, Y., Ji, S., Li, C., Zhang, X., Ge, C., & Wang, T. (2024) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.24115 · Cited by 16

Training a Dynamic Neural Network to Detect False Data Injection Attacks Under Multiple Unforeseen Operating Conditions

Hu, D., Wu, S., Wang, J., & Shi, D. (2023) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2023.3339975 · Cited by 16

Precise facial landmark detection by Dynamic Semantic Aggregation Transformer

Wan, J., Liu, H., Wu, Y., Lai, Z., Min, W., & Liu, J. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110827 · Cited by 16

Neural network developments: A detailed survey from static to dynamic models

Verma, P. R., Singh, N. P., Pantola, D., & Cheng, X. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109710 · Cited by 26

Exploits and advances in dynamic neural networks

Chia, K. J., Maul, T., Chen, Z., Jauw, V. L., & Yap, Y. J. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-12187-9

Dynamic inference techniques for deep neural networks

Zhang, Z., & Shi, L. (2023) · Applied and Computational Engineering · doi:10.54254/2755-2721/2/20220534 · Cited by 1

A survey on dynamic neural networks: From computer vision to multi-modal sensor fusion

Montello, F., Güldenring, R., Scardapane, S., & Nalpantidis, L. (2026) · Image and Vision Computing · doi:10.1016/j.imavis.2026.105980 · Cited by 10

The Design of Dynamic Neural Networks for Efficient Learning and Inference

Wang, X. (2020) · eScholarship (California Digital Library)