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

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

Scale Is Not All You Need: Revisiting the Biomimetic Roots of Deep Learning to Overcome Fundamental Limitations

Wahidi, M., Rizk, A., & Imad, R. (2025) · IEEE Access · doi:10.1109/access.2025.3589514 · Cited by 1

Strategies at a glance: A comparative analysis of training techniques for optimizing early-exit deep neural networks

Haseena Rahmath, P., Chaurasia, K., & Bansal, A. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107970 · Cited by 1

GliaNet: Adaptive Neural Network Structure Learning with Glia-Driven

Han, M., Pan, L., & Liu, X. (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/cvpr52734.2025.02350 · Cited by 1

Adaptive laser beam forming based on dynamic intensity transfer neural network and its application in wireless power transmission

Huang, G., Liu, C., Geng, C., & Li, X. (2025) · Optics & Laser Technology · doi:10.1016/j.optlastec.2025.113837 · Cited by 1

A module selection-based approach for efficient skeleton human action recognition

Chai, S., Jain, R. K., Teng, S., Liu, J., Tateyama, T., & Chen, Y. (2025) · Displays · doi:10.1016/j.displa.2025.103233 · Cited by 1

FlexRFML: Dynamic Neural Networks on FPGAs for Next-Generation Radio Spectrum Perception

Pessia, F., Sayyed, S., & Restuccia, F. (2025) · ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc) · doi:10.1145/3704413.3764472 · Cited by 1

Input Conditioned Layer Dropping in Speech Foundation Models

Hannan, M. A., Falavigna, D., & Brutti, A. (2025) · Machine learning for signal processing ... · doi:10.1109/mlsp62443.2025.11204255 · Cited by 1

DG-IVF: A Dual-Gated IMU-Vision Fusion Method for Efficient HAR

Li, T., Li, D., Chen, Y., Wang, Y., Feng, X. T., Ma, Y., Liu, D., Wu, J. Y., & Guo, C. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-4091-4_28 · Cited by 1

Sample-adaptive multi-branch network for efficient inference under time constraints

Zhu, L., Che, M., Chang, J., Xu, Z., & Wei, T. (2025) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2025.103633 · Cited by 1

Dynamic fusion of medium-resolution optical and SAR imagery for methane source infrastructure classification

He, Y., Zhang, X., 肖鹏峰, Li, Z., Muhtar, D., Gu, F., Liu, B., & Feng, P. (2025) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2025.104876 · Cited by 1

Special Session - Intermittent TinyML: Powering Sustainable Deep Intelligence Without Batteries

Mendis, H. R., Yıldırım, K. S., Zimmerling, M., Mottola, L., & Hsiu, P.-C. (2025) · ACM International Conference on Embedded Software (EMSOFT) · doi:10.1145/3742874.3757084 · Cited by 1

Implicit Layer-Empowered Deep Learning Networks for 6G Adaptive Channel Estimation

Qiao, Z., Xue, J., Zhang, J., Khan, F. A., & Thompson, J. S. (2025) · IEEE Transactions on Communications · doi:10.1109/tcomm.2025.3643794 · Cited by 1

Leveraging similarity and community-based features for link prediction in temporal social networks using attention-enabled LSTM

Ahuja, R., Kaur, S., Shakya, H. K., & Singh, S. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-25702-0 · Cited by 1

Bio-Inspired Neural Network Dynamics-Aware Reinforcement Learning for Spiking Neural Network

Zheng, Y., Xue, J., Yang, J., & Zhang, Y. (2026) · Biomimetics · doi:10.3390/biomimetics11010047 · Cited by 1

Multi-view dynamic perception framework for Chinese harmful meme detection

Hu, J., Lyu, Y., Chen, Y., Li, K., Xue, Y., & Liang, J. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2025.104602 · Cited by 1

Toward Accurate Image Generation via Dynamic Generative Image Transformer

Mao, Z., Huang, M., Lin, Y., Wang, Q., Zhang, L., & Zhang, Y. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3653620 · Cited by 1

A Survey on Missing Data Generation in Networks

Shao, Q., Shi, R., Zhang, X., & Chen, D. (2026) · Mathematics · doi:10.3390/math14020341 · Cited by 1

Hopf Bifurcation Analysis of a Time Fractional Delayed Inertial BAM Neural Networks With Anisotropic Diffusion

Zhao, K., & Li, N. (2026) · International Journal of Circuit Theory and Applications · doi:10.1002/cta.70330 · Cited by 1

FT-Reg: Unsupervised Multimodal Medical Image Registration Using Dynamic Feature Translation

Xu, F., Zhao, M., Wu, Z., Liu, H., & Meng, G. (2026) · Machine Intelligence Research · doi:10.1007/s11633-025-1578-9 · Cited by 1

Prediction of airfoil dynamic stall unsteady loads using dynamic neural networks architectures

Camacho, E. A., Silva, A. R. R., & Marques, F. D. (2026) · Meccanica · doi:10.1007/s11012-026-02093-8 · Cited by 1

Reinforcement learning in continuous-time memory-dependent systems via neural fractional differential equations

Vellappandi, M., & Lee, S. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133216 · Cited by 1

RAViT: Resolution-Adaptive Vision Transformer

Guidez, M., Duffner, S., & García, C. (2026) · Procedia Computer Science · doi:10.48550/arxiv.2602.24159 · Cited by 1

AdaParse: Personalized Fingerprinting for Visual Generative Model Reverse Engineering

Zheng, Y., Li, Z., Yu, B., Zhou, J., & Lu, J. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3671095 · Cited by 1

RA-GAN: Region-adaptive GAN for binarization on degraded document images

Liu, M., Yu, L., Qin, Y., Wang, G., & Shen, L. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115860 · Cited by 1

Improving Machine Translation With PPO and Token-Adaptive Early Exit in Transformer-Based Models

Gangwani, R., Kumaraguru, D., Sawant, A. C., & Devipriya, A. (2026) · IEEE Access · doi:10.1109/access.2026.3680344 · Cited by 1

A Comparative Study of CNN Optimization Methods for Edge AI: Exploring the Role of Early Exits

Fernandez, N., Valdés, I., Arratibel, J., Vaerenbergh, S. V., & de la Iglesia, I. (2026) · IEEE Journal of Selected Areas in Sensors · doi:10.1109/jsas.2026.3731343 · Cited by 1

When Do Early-Exit Networks Generalize? A PAC-Bayesian Theory of Adaptive Depth

Guo, D., Wu, J., & Yiu, S. M. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11729661 · Cited by 1

From Diet to Free Lunch: Estimating Auxiliary Signal Properties Using Dynamic Pruning Masks in Speech Enhancement Networks

Miccini, R., Laroche, C., Piechowiak, T., Fafoutis, X., & Pezzarossa, L. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463996 · Cited by 1

MGFNet: A Multi-Granularity Fusion Network with Coupling-Guided Sparse Routing for Hybrid EEG-fNIRS Decoding

Zhang, Y., Gong, X., & Yuan, X. (2026) · Sensors · doi:10.3390/s26113402 · Cited by 1

Channel-Adaptive Edge AI: Maximizing Inference Throughput by Adapting Computational Complexity to Channel States

Zhang, J., Huang, J., & Huang, K. (2026) · IEEE Transactions on Communications · doi:10.1109/tcomm.2026.3717007 · Cited by 1

A Survey of Dynamic Token Computation in Transformers: Taxonomy, Stability, and Budget-Aware Evaluation

Khan, A., Junaid, A., Iqbal, A., Husnain, G., Shin, W., & Lim, S. (2026) · IEEE Access · doi:10.1109/access.2026.3720106 · Cited by 1

Dynamic Image Super-resolution Network Based on Semantic Prior

Yang, M., Xu, X., Cheng, L., & Zhang, Z. (2021) · International Conference on Big Data and Information Analytics (BigDIA) · doi:10.1109/bigdia53151.2021.9619702 · Cited by 1

Model Compression for Non-Sequential and Sequential Visual Pattern Recognition Networks ― A Hybrid Approach

Bhalgaonkar, S., Munot, M. V., & Anuse, A. D. (2022) · International Conference on Signal and Information Processing · doi:10.1109/iconsip49665.2022.10007461 · Cited by 1

Dynamic Neural Network Accelerator for Multispectral detection Based on FPGA

Wang, X., Zhao, L., Wu, W., & Jin, X. (2023) · International Conference on Advanced Communication Technology · doi:10.23919/icact56868.2023.10079367 · Cited by 1

Maximal sparse convex surrogate-assisted evolutionary convolutional neural architecture search for image segmentation

Wang, W., Wang, X., & Song, X. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01166-5 · Cited by 1

Augmenting Legacy Networks for Flexible Inference

Clemons, J., Frosio, I., Shen, M., Álvarez, J. M., & Keckler, S. W. (2023) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55152.2023.10186809 · Cited by 1

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

Uncertainty-Guided Spatial Pruning Architecture for Efficient Frame Interpolation

Cheng, R., Jiang, X., He, R., Zhou, S., Tan, W., & Yan, B. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611752 · Cited by 1

Dynamic deep neural network inference via adaptive channel skipping

Zou, M., LI, X., FANG, J., Wen, H., & Fang, W. W. (2023) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.55730/1300-0632.4020 · Cited by 1

Distilled Routing Transformer for Driving Behavior Prediction

Jun, G., Yi, J., & Murphey, Y. L. (2023) · SAE International Journal of Transportation Safety · doi:10.4271/09-12-01-0003 · Cited by 1

Node-dependent Semantic Search over Heterogeneous Graph Neural Networks

Wang, Z., Zhao, H., Liang, F., & Shi, C. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614989 · Cited by 1

CAS-NN: A Robust Cascade Neural Network Without Compromising Clean Accuracy

Chen, Z., He, Z., Zhou, Y., Chan, P. P. K., Zhang, F., & Situ, H. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8082-6_38 · Cited by 1

Cross-model temporal cooperation via saliency maps for efficient frame classification

Trinci, T., Bianconcini, T., Sarti, L., Taccari, L., & Sambo, F. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00125 · Cited by 1

Cross-Model Temporal Cooperation Via Saliency Maps for Efficient Recognition and Classification of Relevant Traffic Lights

Trinci, T., Bianconcini, T., Sarti, L., Taccari, L., & Sambo, F. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10421938 · Cited by 1

Channel-Adaptive Early Exiting Using Reinforcement Learning for Multivariate Time Series Classification

Pantiskas, L., Verstoep, K., Hoogendoorn, M., & Bal, H. E. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00073 · Cited by 1

Multi-Resolution Model Compression for Deep Neural Networks: A Variational Bayesian Approach

Xia, C., Guo, H., Ma, H., Tsang, D. H. K., & Lau, V. K. N. (2024) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2024.3382405 · Cited by 1

ESEN: Efficient GPU sharing of Ensemble Neural Networks

Wang, J., Shi, Y., Chen, Z., & Wen, M. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128030 · Cited by 1

Back to the Future: Reversible Runtime Neural Network Pruning for Safe Autonomous Systems

Abraham, D., Maity, B., Donyanavard, B., & Dutt, N. D. (2024) · Design, Automation, and Test in Europe · doi:10.23919/date58400.2024.10546571 · Cited by 1

DyPIM: Dynamic-Inference-Enabled Processing - In-Memory Accelerator

Xie, T., Zhao, T., Zhu, Z., Ning, X., Li, B., Dai, G., Yang, H., & Wang, Y. (2024) · Design, Automation, and Test in Europe · doi:10.23919/date58400.2024.10546612 · Cited by 1

$$\text {I}^2$$Net: Exploiting Misaligned Contexts Orthogonally with Implicit-Parameterized Implicit Functions for Medical Image Segmentation

Yu, J., Duan, F., & Chen, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72111-3_31 · Cited by 1

DynSplit: A Dynamic Split Learning Scheme for 5G-Enpowered Metaverse

Shu, Y., Gu, P., Adjih, C., Chen, C. S., & Serhrouchni, A. (2024) · IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom) · doi:10.1109/metacom62920.2024.00043 · Cited by 1

Data Overfitting for On-device Super-Resolution with Dynamic Algorithm and Compiler Co-design

Li, G., Shu, Z., Ji, J., Qin, M., Afghah, F., Niu, W., & Ma, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72855-6_21 · Cited by 1

Differential neural network based adaptive average output feedback control design for dosage determination on cancer based immunotherapy treatment

Aguilar-Blas, N., Chairez, I., & Cabrera, A. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112368 · Cited by 1

DD-Net: Dynamic Network Architecture for Optimized Curve Segmentation and Reduce Computational Redundancy

Cao, Y., Chen, L., Wang, Y., Feng, Z., & Tian, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78383-8_27 · Cited by 1

Empowering Limited Data Domains: Innovative Transfer Learning Strategies for Small Labeled Datasets

Navaneetha Krishnan, S., Mohnish Karthikeyan, B., Arun, S. K., & Christopher Columbus, C. (2024) · IEEE International Conference on Computing, Applications and Systems (COMPAS) · doi:10.1109/compas60761.2024.10796411 · Cited by 1

Augmented Artificial Intelligence in 5G, 6G, and Beyond: A Quantum Leap

Sinha, S., Lambrechts, W., Bimana, A., & Ashipala, A. (2025) · Computer · doi:10.1109/mc.2024.3445532 · Cited by 1

A Dynamic pre-trained Model for Chinese Classical Poetry

Wang, X., Liu, X., Wang, H., & Wu, B. (2025) · Lecture notes in computer science · doi:10.1007/978-981-97-5779-4_30 · Cited by 1

Robustifying Routers Against Input Perturbations for Sparse Mixture-of-Experts Vision Transformers

Kada, M., Yoshihashi, R., Ikehata, S., Kawakami, R., & Sato, I. (2025) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2025.3536853 · Cited by 1

MT-DyNN: Multi-Teacher Distilled Dynamic Neural Network for Instance-Adaptive Detection in Autonomous Driving

Shen, H., Liu, Q., Wang, Y., Wang, T., & Bai, G. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3548467 · Cited by 1

Multi-Attention Enhanced Segmentation Network for Medical Image

Zhou, W., Zhao, L., Tian, J., Gao, X. B., Chen, Z., Sui, N., & Lou, Y. (2025) · International Conference on Electrical Automation and Artificial Intelligence (ICEAAI) · doi:10.1109/iceaai64185.2025.10956401 · Cited by 1

Data classification with dynamically growing and shrinking neural networks

Świderski, S., & Jastrzębska, A. (2025) · Journal of Computational Science · doi:10.1016/j.jocs.2025.102660 · Cited by 1

Real-Time Surface Defect Detection with Compound Scaling Dynamic Neural Networks

Han, F., Liang, Y., Jing, R., & Zhu, S. (2024) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc64274.2024.00102 · Cited by 1

A New Framework for Computationally Unbounded AI

Khudiyev, A., & Jeannin‐Girardon, A. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-032-00206-8_17 · Cited by 1

Improved Post-Trained Adaptive Network with Texture Bias Robustness

Khabarlak, K. (2025) · International Conference on Advanced Computer Information Technologies (ACIT) · doi:10.1109/acit65614.2025.11185636 · Cited by 1

AI Model Inference and Deployment Optimization for Heterogeneous Intelligent Embedded Systems—A Survey and Perspective from Model Lightweighting to System-Level Acceleration

庞, 为. (2025) · Embedded Technology and Intelligent Systems · doi:10.12677/etis.2025.24023 · Cited by 1

Market-based Architectures in RL and Beyond

Sudhir, A. P., & Tran-Thanh, L. (2025) · Adaptive Agents and Multi-Agents Systems · doi:10.65109/aytk2617 · Cited by 1

Temporal Early Exits for Efficient Video Object DetectionPreprint

Sabet, A., Xun, L., Hare, J. S., Al‐Hashimi, B. M., & Merrett, G. V. (2023) · doi:10.36227/techrxiv.24602019.v1 · Cited by 1

Dynamic Kernels and Channel Attention for Low Resource Speaker VerificationPreprint

Ollerenshaw, A., Jalal, M. A., & Hain, T. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.02000 · Cited by 1

Generative Model for Models: Rapid DNN Customization for Diverse Tasks and Resource ConstraintsPreprint

Xu, W., Li, Y., Liu, J., Sun, Y., Cao, Z., Li, Y., Wen, H., & Liu, Y. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.15003 · Cited by 1

Plugin Speech Enhancement: A Universal Speech Enhancement Framework Inspired by Dynamic Neural NetworkPreprint

Cui, Z., Chen, Y., Gao, Y., Feng, J., Deng, C., & Zhang, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4838997 · Cited by 1

Adaptive Rank Allocation: Speeding Up Modern Transformers with RaNA AdaptersPreprint

Garcia, R., Liu, J., Sorvisto, D., & Eyuboglu, S. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2503.18216 · Cited by 1

When Does Sparse MoE Help in Vision? The Role of Backbone Compute Leverage in Sparse RoutingPreprint

Sun, L., Harn, P.-W., He, P., & Qin, X. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2605.15484 · Cited by 1

Dynamic Degradation for Image Restoration and FusionPreprint

Fang, A., Zhao, X., Yang, J., & Zhang, Y. (2021) · arXiv (Cornell University)

DRDF

Hong, H., Jin, X. H., Zhang⋆, Y., Hu, Y., Zhang, J., He, Y., & Xue, H. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475702

Semantic Information in Gating Patterns of Dynamic Convolutional Neural Networks

Theodorakopoulos, I., & Economou, G. (2021) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa52424.2021.9555567

A Multimodal Dynamic Neural Network for Call for Help Recognition in Elevators

Ju, R., Chu, H., Wang, Y., Deng, Q., Cheng, M., & Li, M. (2021) · Companion Publication of the International Conference on Multimodal Interaction (ICMI Companion) · doi:10.1145/3461615.3491112

DNA: Dynamic Network AugmentationPreprint

Mahan, S. P., Doster, T. J., & Kvinge, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.09277

Dual Complementary Dynamic Convolution for Image RecognitionPreprint

Yan, L., Qin, Y., Liu, S., & Chen, J. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2211.06163

Dynamic Neural Network-based Solutions for Acoustic Echo Suppression

Li, J., Liu, S., & Wu, Y. (2022) · Highlights in Science Engineering and Technology · doi:10.54097/hset.v15i.2795

Switchable Representation Learning Framework with Self-Compatibility

Wu, S., Bai, Y., Lou, Y., Linghu, X., He, J., & Duan, L. (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/cvpr52729.2023.01530

Latency-Critical Inference Serving for Deep Learning

Nigade, V. (2023) · doi:10.5463/thesis.308

Autonomous evolutionary learning for unmanned mobile platforms: Research progress and prospects

Zhang, Y., Wang, P., Zhang, L., & Yan, Q. (2023) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/tb-2023-0811

ARTIFICIAL NEURAL NETWORKS WITH DYNAMIC SYNAPSES: A REVIEW

Dumpis, M. (2023) · Mokslas - Lietuvos ateitis · doi:10.3846/mla.2023.20144

Temporal Early Exits for Efficient Video Object DetectionPreprint

Sabet, A., Xun, L., Hare, J. S., Al‐Hashimi, B. M., & Merrett, G. V. (2023) · doi:10.36227/techrxiv.24602019.v2

Length-Adaptive Distillation: Customizing Small Language Model for Dynamic Token Pruning

Liu, C., Tao, C., Liang, J., Feng, J., Shen, T., Huang, Q., & Zhao, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.294

Dynamic Neural Networks for Adaptive Implicit Image Compression

Huang, B., Zhang, Y., Hu, Y., Dai, S., Huang, Z., & Chao, F. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8552-4_34

Reinforcement Learning-Guided Channel Selection Across Time for Multivariate Time Series Classification

Pantiskas, L., Verstoep, K., Hoogendoorn, M., & Bal, H. E. (2023) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci52147.2023.10371794

Improved Scene Classification by Dynamic CNNs

Akbaba, E. E., Günsel, B., & Gürkan, F. (2023) · IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs58634.2023.10382836

Research on Speech Emotion Recognition Algorithm Based on Soft Decision Trees and Contrastive LearningPreprint

Wang, C., & Shen, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4690520

Face Forgery Detection via Texture and Saliency Enhancement

Guo, S., Yang, H., & Lin, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-53305-1_37

Plugin Speech Enhancement: A Universal Speech Enhancement Framework Inspired by Dynamic Neural NetworkPreprint

Chen, Y., Cui, Z., Gao, Y., Feng, J., Deng, C., & Zhang, S. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.12746

Learning Dynamic Spatial Graphs and Spatial Patterns for Accurate Traffic Prediction

Duan, W., Lin, C., Zhu, J., He, X., Huang, W., & Rao, H. (2023) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads60453.2023.00200

CDCP: A Framework for Accelerating Dynamic Channel Pruning Methods

Xiang, Z., Luo, Y., Xie, Y., She, H., Liang, W., & Zhao, L. (2023) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads60453.2023.00031

Efficient Recurrent Neural Networks via Importance-Based Sparsification

Ren, J., Zheng, N., Su, X., Zhang, H., Li, H., & Li, S. (2024) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126624502712

Searching Heterophily-Agnostic Graph Neural NetworksPreprint

Wei, L., He, Z., Zhao, H., & Yao, Q. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4825405

The Dynamic Paradox: How Layer-skipping DNNs Amplify Cache Side-Channel Leakages

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