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

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318

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

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325

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

AlignMiF: Geometry-Aligned Multimodal Implicit Field for LiDAR-Camera Joint Synthesis

Tang, T., Wang, G., Lao, Y., Chen, P., Liu, J., Lin, L., Yu, K., & Liang, X. (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/cvpr52733.2024.02006 · Cited by 16

DPNet: Dynamic Pooling Network for Accurate and Efficient Size-Aware Tiny Object Detection

Gong, L., Chen, H., Chen, Y., Yao, T., Li, C., Zhao, S., & Han, G. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3559921 · Cited by 16

SpecEE: Accelerating Large Language Model Inference with Speculative Early Exiting

Xu, J., Pan, J., Zhou, Y., Chen, S., Li, J., Lian, Y., Wu, J., & Dai, G. (2025) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3695053.3730996 · Cited by 15

Learning Hierarchical Dynamics with Spatial Adjacency for Image Enhancement

Liang, Y., Wang, B., Ren, W., Liu, J., Wang, W., & Zuo, W. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548322 · Cited by 15

ACF: An Adaptive Compression Framework for Multimodal Network in Embedded Devices

Cai, Q., Liu, X., Zhang, K., Xie, X. N., Tong, X., & Li, K. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3303350 · Cited by 15

Contrastive representation learning on dynamic networks

Jiao, P., Chen, H., Tang, H., Bao, Q., Zhang, L., Zhao, Z., & Wu, H. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106240 · Cited by 15

Revisiting the Trade-Off Between Accuracy and Robustness via Weight Distribution of Filters

Wei, X., Zhao, S., & Li, B. H. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3411035 · Cited by 15

Joint Adaptive Resolution Selection and Conditional Early Exiting for Efficient Video Recognition on Edge Devices

Wang, Q., Yu, C., Chen, S., Fang, W. W., & Xiong, N. N. (2025) · Big Data Mining and Analytics · doi:10.26599/bdma.2024.9020093 · Cited by 15

Insights into resource utilization of code small language models serving with runtime engines and execution providers

Durán, F., Martínez, M., Lago, P., & Martínez‐Fernández, S. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112574 · Cited by 15

DyDiT++: Diffusion Transformers With Timestep and Spatial Dynamics for Efficient Visual Generation

Zhao, W., Han, Y., Tang, J., Wang, K., Luo, H., Song, Y., Huang, G. M., Wang, F., & You, Y. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3654201 · Cited by 15

Dynamic CNNs using uncertainty to overcome domain generalization for surgical instrument localization

Philipp, M., Alperovich, A., Gutt‐Will, M., Mathis, A., Saur, S. C., Raabe, A., & Mathis-Ullrich, F. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00179 · Cited by 15

A Two-Level Dynamic Adaptive Network for Medical Image Fusion

Huang, W., Zhang, H., Quan, X., & Wang, J. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3169546 · Cited by 15

Contrastive Haze-Aware Learning for Dynamic Remote Sensing Image Dehazing

Nie, J., Wei, W., Zhang, L., Yuan, J., Wang, Z., & Li, H. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3220940 · Cited by 15

CNO-LSTM: A Chaotic Neural Oscillatory Long Short-Term Memory Model for Text Classification

Shi, N., Chen, Z., Chen, L., & Lee, R. (2022) · IEEE Access · doi:10.1109/access.2022.3228600 · Cited by 15

Learning to Class-Adaptively Manipulate Embeddings for Few-Shot Learning

Zhou, F., Wei, W., Zhang, L., & Zhang, Y. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3248798 · Cited by 15

Towards Inference Efficient Deep Ensemble Learning

Li, Z., Ren, K., Yang, Y., Jiang, X., Yang, Y., & Li, D. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i7.26048 · Cited by 16

Adapting Neural Networks at Runtime: Current Trends in At-Runtime Optimizations for Deep Learning

Sponner, M., Waschneck, B., & Kumar, A. S. (2024) · ACM Computing Surveys · doi:10.1145/3657283 · Cited by 15

Fine-grained Visible Watermark Removal

Niu, L., Zhao, X., Zhang, B., & Zhang, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01173 · Cited by 14

Enabling Large Dynamic Neural Network Training with Learning-based Memory Management

Ren, J., Xu, D., Yang, S., Zhao, J., Li, Z., Navasca, C., Wang, C., Xu, H., & Li, D. (2024) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca57654.2024.00066 · Cited by 14

MTdyg: Multi-scale transformers with continuous time dynamic graph model for link prediction

Xu, L., Pan, Z., Chen, H., & Wang, S. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112245 · Cited by 14

Empirical Study on Real Estate Mass Appraisal Based on Dynamic Neural Networks

Chen, C., Ma, X., & Xiao-jia, Z. (2024) · Buildings · doi:10.3390/buildings14072199 · Cited by 14

Stego-STFAN: A Novel Neural Network for Video Steganography

Vergara, G. F., Fernandes, P. A. G., Serrano, A. L. M., de Mendonça, F. L. L., Rodrigues, G. A. P., Bispo, G. D., Gonçalves, V. P., Albuquerque, R. D. O., & de Sousa, R. T. (2024) · Computers · doi:10.3390/computers13070180 · Cited by 14

A Time-Series Model of Gated Recurrent Units Based on Attention Mechanism for Short-Term Load Forecasting

Xiong, W. (2024) · IEEE Access · doi:10.1109/access.2024.3443876 · Cited by 14

Edge-Cloud Collaborated Object Detection via Bandwidth Adaptive Difficult-Case Discriminator

Cao, Z., Cheng, Y., Zhou, Z., Chen, Y., Hu, Y., Lu, A., Liu, J., & Li, Z. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3474743 · Cited by 14

Dynamic Convolutional Neural Networks Based on Adaptive 2D Memristors

Hong, H., Chen, X., Cho, W., Yoo, H. Y., Oh, J., Kim, M., Hwang, G., Yang, Y., Sun, L., Wang, Z., et al. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202422321 · Cited by 14

Dynamic proximal unrolling network for compressive imaging

Yang, Y., Tao, R., Wei, K., & Fu, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.08.034 · Cited by 14

Turbo

Lu, Y., Jiang, S., Cao, T., & Shu, Y. (2022) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3560905.3568501 · Cited by 14

TLEE: Temporal-Wise and Layer-Wise Early Exiting Network for Efficient Video Recognition on Edge Devices

Wang, Q., Fang, W. W., & Xiong, N. N. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3293506 · Cited by 14

PAD-Net: An Efficient Framework for Dynamic Networks

He, S., Ding, L., Dong, D., Liu, B., Yu, F., & Tao, D. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.803 · Cited by 16

MMExit: Enabling Fast and Efficient Multi-modal DNN Inference with Adaptive Network Exits

Hou, X., Liu, J., Tang, X., Li, C., Cheng, K.-T. T., Li, L., & Guo, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-39698-4_29 · Cited by 14

Window-Based Early-Exit Cascades for Uncertainty Estimation: When Deep Ensembles are More Efficient than Single Models

Xia, G., & Bouganis, C.-S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01593 · Cited by 14

A fourier-enhanced physics-informed Kolmogorov–Arnold network for multi-frequency seismic response analysis of structures

Tao, D., Li, J., Du, K., & Lu, Z. (2026) · Bulletin of Earthquake Engineering · doi:10.1007/s10518-026-02408-w · Cited by 13

Adaptive Inference Pathway-Gated Neural Network Model for Digital Predistortion With Varying Transmission Configurations

Zhang, Q., Jiang, C., Han, R., Yang, G., Wang, J., Chang, H. T., & Liu, F. (2024) · IEEE Transactions on Microwave Theory and Techniques · doi:10.1109/tmtt.2024.3418014 · Cited by 13

Dynamic Slimmable Network for Speech Separation

Elminshawi, M., Chetupalli, S. R., & Habets, E. A. P. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3445304 · Cited by 13

DAFDet: A Unified Dynamic SAR Target Detection Architecture With Asymptotic Fusion Enhancement and Feature Encoding Decoupling

Yang, Y., Du, Y., Zhang, L., Li, G., Chen, Y., Cheng, G., & Song, S. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3521971 · Cited by 13

C3Ghost and C3k2: performance study of feature extraction module for small target detection in YOLOv11 remote sensing images

Xiao, R., Wang, H., Wang, L., & Hong, Y. (2025) · International Conference on Big Data, Computational Intelligence, and Applications (BDCIA) · doi:10.1117/12.3059792 · Cited by 13

DynLeafNet: A dynamic lightweight architecture for plant disease classification using dynamic residual network with explainable artificial intelligence

Verma, P. R., Pantola, D., & Singh, N. P. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110937 · Cited by 13

Graph neural networks: Historical backgrounds, present revolutions, and conventionalization for the future

Maghdid, S. S., Rashid, T. A., & Askar, S. K. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00797-w · Cited by 13

Fast and Accurate Gigapixel Pathological Image Classification with Hierarchical Distillation Multi-Instance Learning

Dong, J., Jiang, J., Jiang, K., Li, J., & Zhang, 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/cvpr52734.2025.02870 · Cited by 13

Edge-Cloud Collaborated Object Detection via Difficult-Case Discriminator

Cao, Z., Li, Z., Chen, Y., Pan, H., Hu, Y., & Liu, J. (2023) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs57875.2023.00062 · Cited by 13

Multiscale Dynamic Curvelet Scattering Network

Gao, J., Jiao, L., Liu, X., Li, L., Chen, P., Liu, F., & Yang, S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3223212 · Cited by 13

One Person, One Model—Learning Compound Router for Sequential Recommendation

Liu, Z., Cheng, M., Li, Z., Liu, Q., & Chen, E. (2022) · Proceedings · doi:10.1109/icdm54844.2022.00039 · Cited by 13

On The Design and Training Strategies for Rnn-Based Online Neural Speech Separation Systems

Li, K., & Luo, Y. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096724 · Cited by 13

Memory-Friendly Scalable Super-Resolution via Rewinding Lottery Ticket Hypothesis

Lin, J. F., Luo, X., Hong, M., Qu, Y., Xie, Y., & Wu, Z. (2023) · 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/cvpr52729.2023.01384 · Cited by 13

AdaSem: Adaptive Goal-Oriented Semantic Communications for End-to-End Camera Relocalization

Liao, Q., & Tung, T.-Y. (2024) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom52122.2024.10621397 · Cited by 13

Boosting medical image segmentation via conditional-synergistic convolution and lesion decoupling

Yang, H., Chen, Q., Fu, K., Zhu, L., Jin, L., Qiu, B., Ren, Q., Du, H., & Lu, Y. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102110 · Cited by 13

A Dynamic Transformer Network With Early Exit Mechanism for Fast Detection of Multiscale Surface Defects

Yu, H., Liu, D., Zhang, Z., & Wang, J. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3312488 · Cited by 12

Inference serving with end-to-end latency SLOs over dynamic edge networks

Nigade, V., Bauszat, P., Bal, H. E., & Wang, L. (2024) · Real-Time Systems · doi:10.1007/s11241-024-09418-4 · Cited by 12

A knowledge-informed dynamic correlation modeling framework for lane-level traffic flow prediction

Jiang, R., Wang, S., Ma, W., Zhang, Y., Fan, P., & Jia, D. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103327 · Cited by 12

Dynamic Deep Neural Network Adversarial Attacks for Edge-based IoT Devices

Ayyat, M., Nukavarapu, S. K., & Nadeem, T. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001235 · Cited by 12

AMC: Adaptive Multi-expert Collaborative Network for Text-guided Image Retrieval

Zhu, H., Wei, Y., Zhao, Y., Zhang, C., & Huang, S. (2023) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3584703 · Cited by 12

Enhanced dynamic feature representation learning framework by Fourier transform for domain generalization

Wang, X., Zhao, Q., Zhang, C., Wang, B., Wang, L., & Liu, W. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119624 · Cited by 12

A Quaternion-Valued Neural Network Approach to Nonsmooth Nonconvex Constrained Optimization in Quaternion Domain

Liu, J., Liao, X., & Dong, J. S. (2023) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3318416 · Cited by 12

Energy Consumption Prediction and Optimization of Electric Vehicles Based on RLS and Improved SOA

Liu, C. (2024) · IEEE Access · doi:10.1109/access.2024.3375355 · Cited by 12

Meta-GF: Training Dynamic-Depth Neural Networks Harmoniously

Sun, Y., Li, J., & Xu, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20083-0_41 · Cited by 12

A divide and conquer framework for Knowledge Editing

Han, X., Li, R., Li, X., & Pan, J. Z. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110826 · Cited by 12

Scenario-Aware Learning Approaches to Adaptive Channel Estimation

Li, R., Sun, J. Z., Xue, J., & Masouros, C. (2023) · IEEE Transactions on Communications · doi:10.1109/tcomm.2023.3330878 · Cited by 11

Introducing Region Based Pooling for handling a varied number of EEG channels for deep learning models

Tveitstøl, T., Tveter, M., T, A. S. P., Hatlestad‐Hall, C., Yazidi, A., Hammer, H. L., & Haraldsen, I. (2024) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2023.1272791 · Cited by 11

VGGM: Variational Graph Gaussian Mixture Model for Unsupervised Change Point Detection in Dynamic Networks

Zhang, X., Jiao, P., Gao, M., Li, T., Wu, Y., Wu, H., & Zhao, Z. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3377548 · Cited by 11

Content-Aware Dynamic In-Loop Filter With Adjustable Complexity for VVC Intra Coding

Man, H., Wang, H., Lu, R., Wan, Z., Xiaopeng Fan, B., & Zhao, D. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3535784 · Cited by 11

Parameter Disentanglement for Diverse Representations

Wang, J., Guo, J., Wang, R., Zhang, Z., Fu, L., & Ye, Q. (2025) · Big Data Mining and Analytics · doi:10.26599/bdma.2024.9020087 · Cited by 11

DSNet: Dynamic Stitchable Neural Network for Hyperspectral Image Classification

Feng, S., Zhao, Z., Xi, B., Zhao, C., Li, W., Tao, R., & Li, Y. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3589364 · Cited by 11

A survey of large language models for data challenges in graphs

Li, M., Zhang, P., Xing, W., Zheng, Y., Zaporojets, K., Chen, J., Zhang, R., Zhang, Y., Gong, S., Hu, J. S., et al. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129643 · Cited by 11

Exploiting neuro-inspired dynamic sparsity for energy-efficient intelligent perception

Zhou, S., Gao, C., Delbrück, T., Verhelst, M., & Liu, S. (2025) · Nature Communications · doi:10.1038/s41467-025-65387-7 · Cited by 11

Next-Gen Digital Predistortion From Hardware Acceleration of Neural Networks: Trends, Challenges, and Future

Khan, M. T., Ding, Y., & Goussetis, G. (2026) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2026.3656642 · Cited by 11

exploring Fine-Grained Sparsity in Convolutional Neural Networks for Efficient Inference

Wang, L., Guo, Y., Dong, X., Wang, Y., Ying, X., Lin, Z., & An, W. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3193925 · Cited by 11

GradMDM: Adversarial Attack on Dynamic Networks

Pan, J., Foo, L. G., Zheng, Q., Fan, Z., Rahmani, H., Ke, Q., & Liu, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3263619 · Cited by 11

FreqSense: Adaptive Sampling Rates for Sensor-Based Human Activity Recognition Under Tunable Computational Budgets

Yang, G., Zhang, L., Bu, C., Wang, S., Wu, H., & Song, A. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3321639 · Cited by 11

RRTrN: A lightweight and effective backbone for scene text recognition

Zhou, Q., Gao, J., Yuan, Y., & Wang, Q. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122769 · Cited by 11

Dynamics-Aware Adversarial Attack of Adaptive Neural Networks

Tao, A., Duan, Y., Wang, Y., Lu, J., & Zhou, J. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3351680 · Cited by 11

Human-Inspired Facial Sketch Synthesis with Dynamic Adaptation

Gao, F., Zhu, Y., Jiang, C., & Wang, N. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00665 · Cited by 11

Deep Incubation: Training Large Models by Divide-and-Conquering

Ni, Z., Wang, Y., Yu, J., Jiang, H., Cao, Y., & Huang, G. M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01590 · Cited by 11

Improving Human Activity Recognition With Wearable Sensors Through BEE: Leveraging Early Exit and Gradient Boosting

Yu, J., Zhang, L., Cheng, D., Huang, W., Wu, H., & Song, A. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3457830 · Cited by 11

Task-Customized Self-Supervised Pre-training with Scalable Dynamic Routing

Liu, Z., Han, J., Hong, L., Xu, H., Chen, K., Xu, C., & Li, Z. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20079 · Cited by 11

Street Floor Segmentation for a Wheeled Mobile Robot

Hyun, J., Woo, S., & Kim, E. (2022) · IEEE Access · doi:10.1109/access.2022.3227203 · Cited by 11

Semantic memory–based dynamic neural network using memristive ternary CIM and CAM for 2D and 3D vision

Zhang, Y., Zhang, W., Wang, S., Lin, N., Yu, Y., He, Y., Wang, B., Jiang, H., Lin, P., Xu, X., et al. (2024) · Science Advances · doi:10.1126/sciadv.ado1058 · Cited by 10

SLEXNet: Adaptive Inference Using Slimmable Early Exit Neural Networks

Kütükçü, B., Baidya, S., & Dey, S. (2024) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3689632 · Cited by 10

Deep Feature Surgery: Towards Accurate and Efficient Multi-exit Networks

Gong, C., Chen, Y., Luo, Q., Lu, Y., Li, T., Zhang, Y., Sun, Y., & Zhang, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72967-6_24 · Cited by 10

Confidence-Based Fusion of AC-LSTM and Kalman Filter for Accurate Space Target Trajectory Prediction

Wang, C., Zhang, J., Wang, J., & Wu, Y. (2025) · Aerospace · doi:10.3390/aerospace12040347 · Cited by 10

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

Dynamic nsNET2: Efficient Deep Noise Suppression with Early Exiting

Miccini, R., Zniber, A., Laroche, C., Piechowiak, T., Schoeberl, M., Pezzarossa, L., Karrakchou, O., Sparsø, J., & Ghogho, M. (2023) · Machine learning for signal processing ... · doi:10.1109/mlsp55844.2023.10285925 · Cited by 10

Boosting Convolutional Neural Networks With Middle Spectrum Grouped Convolution

Su, Z., Zhang, J., Liu, T., Liu, Z., Zhang, S., Pietikäinen, M., & Liu, L. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3355489 · Cited by 10

USDN: A Unified Sample-wise Dynamic Network with Mixed-Precision and Early-Exit

Jeon, J.-Y., Nguyen, X. T., Ryu, S., & Lee, H. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00070 · Cited by 10

The continuous memory: A neural network with ordinary differential equations for continuous-time series analysis

Li, B., Chen, H., An, Z., Yu, Y., Jia, Y. G., Chen, L., & Sun, M. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112275 · Cited by 10

Retraining-free Model Quantization via One-Shot Weight-Coupling Learning

Tang, C., Meng, Y., Jiang, J., Xie, S., Lu, R., Ma, X., Wang, Z., & Zhu, W. (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/cvpr52733.2024.01501 · Cited by 10

Concept Neural Network Based on Time-Delay Regret for Dynamic Stream Learning

Mi, Y. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3535636 · Cited by 10

Model-Distributed Inference in Multi-Source Edge Networks

Li, P., Seferoğlu, H., & Koyuncu, E. (2023) · IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) · doi:10.1109/icasspw59220.2023.10193154 · Cited by 10

A spatial design layout optimization model based on deep learning in the context of rural revitalization

Gao, X. (2023) · Results in Engineering · doi:10.1016/j.rineng.2023.101495 · Cited by 10

Reliable Evaluation of Adversarial Transferability

Yu, W., Gu, J., Li, Z., & Torr, P. H. S. (2025) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml64287.2025.00049 · Cited by 10

Slim-Tasnet: A Slimmable Neural Network for Speech Separation

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