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

Towards more efficient transformers for objectdetection and tracking

Senthivel, T. (2024) · theses.fr (ABES) · doi:10.70675/793c048dz822ez46f8za18bz06119d39963f

Stability analysis of earth dams using machine learning-based approaches

An, L. (2023) · theses.fr (ABES) · doi:10.70675/1ebd6d69z1fd9z45e5zba94z1d2b79120b0c

Dynamic architectural optimization of artificial neural networks

Maile, K. (2023) · theses.fr (ABES) · doi:10.70675/8a8952e2z422az4072z9397z4629b985f393

Application of a dynamic object detector with adaptive adjustment based on image complexity in the detection of drone aerial images

Neri, F., Zhang, Z., Xue, Y., & Basgalupp, M. P. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114716

A Feasibility Study of Hybrid Deep-Learning Prediction with Online Adaptation of Breathing Irregularities for Long-Term Internal Organ Motion During Radiotherapy

Milewski, A. R., Fayed, U., Gupta, V., Nie, X., & Li, G. (2026) · Technology in Cancer Research & Treatment · doi:10.1177/15330338261420917

MedSAM-2 Large Model-Driven Medical Image Semantic Communication for Telemedicine

Yang, F., Sun, S., Jin, C., Gao, Z., & Niyato, D. T. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3685837

DyPANet: Efficient Event-Driven Eye Tracking via Dynamic Path Adaptation and ROI Filtering

Zhang, Y. W., & Zhao, Q. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11465019

D-GPH: Dynamic Graph-Prior Hybrid Detector with Uncertainty-Aware Refinement

Han, Y. (2026) · Scientific Journal of Technology · doi:10.54691/kc64wt10

A reliability-aware hybrid expert system via rule confidence learning: Application to transformer fault diagnosis

Li, Q., Hu, Y., Wu, Q., Dong, M., Ren, M., & Zhang, C. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132505

МЕТОД АДАПТИВНОГО КЕРУВАННЯ РІВНЕМ ОБРОБКИ В ГІБРИДНОМУ РОЗПІЗНАВАННІ ОБ’ЄКТІВ У ВІДЕОПОТОКАХ З БПЛА

Голенко, М. Ю. (2026) · Computer Science and Applied Mathematics · doi:10.26661/2786-6254-2026-1-07

Neural Networks in Electrical Engineering Applications: A Review

Khan, F. (2026) · Global Trends in Science and Technology · doi:10.70445/gtst.2.2.2026.219-239

MedGAN-SSM: Multimodal brain image synthesis network integration using SSM empowered GAN

Song, T., Wang, M., Ren, Z., Li, W., Zhang, J., & Li, K. (2026) · iScience · doi:10.1016/j.isci.2026.115924

SAFIR: Spatially-aware Activation Function for Implicit Neural Representations

Zeraatkar, E., & Tešić, J. (2026) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2026.104754

Robust visual recognition in microfluidic displays: a knowledge-driven deep learning approach for artifact suppression and real-time analysis

Li, Z., Jiang, X., Zhang, Y., Dong, Z., Ye, F., Zheng, Z., Ma, Y., Liu, Z., Shi, T., Liao, G., et al. (2026) · Displays · doi:10.1016/j.displa.2026.103504

Enhancing dynamic domain generalization with weight perturbation training

Sun, Z., Yu, S., Wang, D., Xu, L., Lin, L., & Yu, Y. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00817-0

DASR+: Training Domain Distance Aware Network for Unsupervised Image Super-Resolution

Zhao, X., Wei, Y., Deng, X., Li, Y., Timofte, R., Song, H., & Gu, S. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02822-2

Balancing Stability and Plasticity in Sequentially Trained Early-Exiting Neural Networks

Zniber, A., Karrakchou, O., & Ghogho, M. (2026) · Proceedings - International Conference on Image Processing · doi:10.1109/icip61757.2026.11630078

Fiber to the home cluster development: Discovering a regression model to forecast fiber to the home take-up rate using fitness, bias/error performance, stability, and computational execution as model criteria

Atmaja, W. H. K., Prabowo, H., Soeparno, H., & Andryani, N. A. C. (2026) · e-Prime – Nexus of Electrical Electronic and Intelligent Engineering · doi:10.1016/j.eprime.2026.201202

Adaptive 3D Human Pose Estimation Based on Spatial–Temporal Complexity Awareness

Zhang, W., Yang, Z., Hu, C., Sun, J. F., & Li, J. (2026) · Electronics · doi:10.3390/electronics15102076

Special Session: Optimizing Edge AI - Current Challenges and the Neuromorphic Outlook

Dincic, M. R., Perić, Z., Bertozzi, D., Bizzarri, A., Syed, R. T., Jones, E. G., Zese, R., Andjelkovic, M. S., Vargas, F. L., Krstic, M. D., et al. (2026) · IEEE International Symposium on Design and Diagnostics of Electronic Circuits & Systems (DDECS) · doi:10.1109/ddecs69233.2026.11521015

DeepNurse: Self-healing Neural Networks for Unknown Domains

Baresi, L., Hu, D. Y. X., & Quattrocchi, G. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3815576

The Architecture as an Explanation: Task Interpretability through Adaptive Neural ArchitecturesPreprint

Cirrincione, G. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6852772

Confidence-Aware Adaptive Neural Computation for Energy-Efficient Deep Learning

Kethireddy, D. R., Kande, N. K., Yarrrabothu, S. R., & Jain, A. H. (2026) · International Conference on Artificial Intelligence, Systems, and Emerging Technologies (ICAISET) · doi:10.1109/icaiset66439.2026.11541686

Side-Channel Privacy Attacks on Energy-Efficient Mobile Agents: A Membership Inference Perspective

Ehsan, I., Niu, J., & Zhang, Y. (2026) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3812836.3814743

Compression Space Search: RL-Based Combinational Compression for Neural Networks

Shen, Y., Ni, Y., Zhu, J., Zhao, J., & Zou, A. (2026) · Design, Automation, and Test in Europe · doi:10.23919/date69613.2026.11539065

Efficient Behavioral Context Recognition With Multilabel Early Exit and Dynamic Multimodal Aggregation on Edge Devices

Liu, Y., Liu, H., Li, Z., Zou, Y., Liu, M., Zong‐Hao, C., & Wang, C. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3697846

YOLO26-Based Multi-Resolution Adaptive Insulator Defect Detection on Ascend NPU Edge Devices

Lin, J., Liu, B., Liu, J., Wu, L., Wu, X., & Huang, H. G. (2026) · Electronics · doi:10.3390/electronics15122532

Multi-QuAD: Multi-Level Quality-Adaptive Dynamic Network for Reliable Multimodal Classification

Shu, S., Chen, C. L. P., & Zhang, T. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3701530

Physics-informed event-frame fusion learning paradigm for high dynamic range 3D industrial metrology

Li, Y., Teng, D., Li, X., & Liu, L. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.104943

HM-NVS: Hierarchical Multi-Modal Novel View Synthesis\\with Uncertainty-Aware Progressive Refinement

Chang, J., Zhao, H., & Zhang, L. (2026) · International Conference on Multimedia Retrieval · doi:10.1145/3805622.3810569

Lightweight Neural Dynamics with Adaptive Grid Scaling for Real-Time AUV Path Planning

Zhao, N., Han, S., & Zhao, J. (2026) · Conference on Fully Actuated System Theory and Applications (FASTA) · doi:10.1109/fasta70174.2026.11549268

LOREN: Low-Rank-Based Code-Rate Adaptation in Neural Receivers

Bolderik, B. V., Menkovski, V., de Groot, S. H., & Gomony, M. D. (2026) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc65185.2026.11555736

CRAFT: A Co-Adaptive Freeze and Train Strategy for Dynamic Neural Networks

Allwin, P. S., Gomony, M. D., & Geilen, M. (2026) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc65185.2026.11555284

ViT-FREE: Efficient Face Recognition via Early Exiting and Synthetic Adaptation

Chettaoui, T., Ozgur, G., Caldeira, E., Damer, N., & Boutros, F. (2026) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg67764.2026.11556518

EX-FIQA: Leveraging Intermediate Early eXit Representations from Vision Transformers for Face Image Quality Assessment

Ozgur, G., Chettaoui, T., Caldeira, E., Kolf, J. N., Atzori, A., Boutros, F., & Damer, N. (2026) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg67764.2026.11557000

Energy-Efficient Fall Detection Using Conditional Dual-Path Spiking Neural Networks with Hierarchical Temporal Dynamics

Li, X., Huang, Y., Su, L., Yao, S., Li, Y., & Zhao, J. (2026) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas66217.2026.11562792

Cross-Layer Reliability Analysis of Slimmable Neural Networks under Permanent Faults

Zazatis, N., Veronesi, A., Varakliotis, K., Tsoumanis, P., Sotiriou, C. P., Poehls, L. M. B., Andjelkovic, M. S., & Bertozzi, D. (2026) · IEEE VLSI Test Symposium (VTS) · doi:10.1109/vts69484.2026.11563389

Hybrid Deep Learning Frameworks for License Plate Recognition in Complex Environments: A Comprehensive Survey and Research Outlook

Deng, S., Sheng, E. L., Kiong, T. S., & Yang, M. (2026) · IET Computer Vision · doi:10.1049/cvi2.70071

Enabling Edge IoT Intelligence: A Survey on Resource-Constrained Continual Learning

Yang, R., Bie, D., Li, D., Yao, Z., & Xiang, J. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3705393

Evaluating the Efficiency of Algorithms for Neural Module Adaptation to Classification Problems in Dynamic Neural Network

Dovzhenko, T., Storchak, K., Senkov, O., Bilavka, V., & Vladarchyk, Y. (2026) · VFAST Transactions on Software Engineering · doi:10.21015/vtse.v14i2.2382

ViewAdapt-Det: view-adaptive detection for soccer broadcasting videos

Zhu, C., Zhang, Y., Gao, S., Wang, C., & Huang, C. (2026) · Scientific Reports · doi:10.1038/s41598-026-59307-y

PD-YOLO: a high-precision detection network for fine-grained lung cancer and multi-scale pulmonary nodules in CT images

Yang, C. C.-K., Zhang, C., Wang, K., Wu, B., & Li, Z. (2026) · Scientific Reports · doi:10.1038/s41598-026-58746-x

EntroBot: Entropy-Based Modeling of Account Behavior for Social Bot Detection

Jiang, S.-Y., Xie, Y., Xu, X.-K., & Lü, L. (2026) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2026.3695076

DPG-RF: An Adaptive Recognition Method Based on Discriminative-Power-Guided Random Forest for Gas-Solid Two-Phase Flow Carriers

Ma, Q., Liu, T., Wang, L. X., Li, L., Peng, G., & Li, H. (2026) · International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA) · doi:10.1109/aiita69518.2026.11567054

MGRN: Multi-granularity reasoning for fine-grained image-text retrieval in open-world scenarios

Liu, T., Sun, Z., Jin, C., Wang, J., Wei, Q., Meng, C., & Chang, S. (2026) · Information Sciences · doi:10.1016/j.ins.2026.124091

A Review of Partial Video Copy Detection: Technical Advances, Evaluation Frameworks, and Future Trends

Hu, Z., Li, X., Zheng, Y., & Shen, W. (2026) · IEEE Access · doi:10.1109/access.2026.3707321

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

Simulation-Based Structural Optimization for Neural Networks

Świderski, S., & Jastrzębska, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29924-6_48

Full-link degradation framework and restoration for Mobile-based telephoto

Qiu, T., Zhou, J., Jiang, Y., 刘月申, Xu, J., Zhang, Q. R., Feng, H., & Chen, Y. (2026) · Optics & Laser Technology · doi:10.1016/j.optlastec.2026.115787

ADEx: Adaptive Early Exit DNNs for Inference Robustness to Popularity Drift

Zhao, Z., Brevi, D., Levorato, M., Malandrino, F., Pastrone, C., & Chiasserini, C. F. (2026) · IFIP Networking Conference (IFIP Networking) · doi:10.23919/ifipnetworking70592.2026.11579204

CMRNet: A multispectral detection framework driven by dynamic intelligence for coastal environments

Li, P., Huang, X., An, S., Zhang, L., & Zhang, Y. (2026) · Ocean Engineering · doi:10.1016/j.oceaneng.2026.126689

Scale-Reconfigurable Multi-Agent Reinforcement Learning for Aerial Networks in Dynamic Environments

Kim, G. S., Roh, E. J., Jung, S., Park, S., & Kim, J. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3709180

DriftSched: Covariance-Guided Adaptive Detection Scheduling for Efficient Multi-Object Tracking

Xu, Y., Jiang, Z., Li, X., & Dahnoun, N. (2026) · Mediterranean Conference on Embedded Computing (MECO) · doi:10.1109/meco70748.2026.11579050

Spatial-Temporal Expert Learning for Video-based Person Re-identification

Hui, X., Wang, P., Ling, E., Huang, D., Ma, K. T., Hur, M., & Liu, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-39518-4_10

DDVT: Dynamic Dual-Level Vision Transformer Fusion Network for Answer Grounding in Visual Question Answering

Zhang, Y., Li, X., Fan, W., Yang, X., & Zhou, D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-22267-1_35

Adaptive Scale Networks: Stable Self-Optimizing AI Models

Sood, A., & Rana, A. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.06.057

Audio-guided visual selection for efficient multimodal fusion via a discrete variational autoencoder

Le, H. M., Huang, H., Mawalim, C. O., Leong, C. W., & Okada, S. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104613

Dynamic tokenization in the Transformer era: A review and taxonomy

Azizi, M. M., BabaAli, B., & Ziaeetabar, F. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114439

A Scene-Aware Meta-learning Framework for Robust Photovoltaic Power Forecasting

Yu, Y., Dang, Y., Zhang, B., Chen, P., Zhang, Y., & Liang, R. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3447-9_45

Integrating biogenic nano seed priming with artificial intelligence and machine learning: enhanced sustainable crop productivity and precision agriculture

Yadav, G., Yadav, R., & Mathur, J. (2026) · 3 Biotech · doi:10.1007/s13205-026-04955-0

Policy-based Foveated Imaging and Perception

Xiao, H., Ackermann, J., Deng, B. H., & Wetzstein, G. (2026) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/3799902.3811218

Adaptive Block Sizing for Speculative Decoding via Logit-Confidence Based Verification

Ouyang, J., & He, X. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3410-3_5

CAIMEN: Causally-Aware Interpretable Modular Ensemble Networks

Sathishkumar, B., Abinaya, M., & M, L. C. (2026) · IEEE International Conference on Intelligent Technologies for a Sustainable and Inclusive Future (ICITSIF) · doi:10.1109/icitsif69060.2026.11608353

Trustworthy Selective Computation for Resource-Constrained Vehicular Edge NetworksPreprint

Albarello, Â. B., Canedo, E. D., Gonçalves, V. P., Villas, L. A., Meneguette, R. I., & Filho, G. P. R. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7194318

MAFPN-DNM: A Multi-Scale Attention Feature Pyramid Network with Dynamic Neural Model for Fine-Grained White Blood Cell Classification

Zhang, C., Chen, C., Cao, Y., Liu, Z., & Gao, S. (2026) · International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs69761.2026.11613379

Прогнозирование временных рядов в интеллектуальных системах поддержки принятия решений реального времени: разработка трансформера с ранним выходом и доверительными интервалами

Игоревич, К. М., & Eremeev, A. P. (2026) · Международный журнал Программные продукты и системы · doi:10.15827/0236-235x.154.165-175

RPA-PCFormer: relative position attention guided transformer for 3D point clouds semantic segmentation with learnable pooling mechanism

Gong, J., Wang, Z., Wang, T., & Zhou, J. (2026) · Measurement · doi:10.1016/j.measurement.2026.122719

ViTNT-FIQA: Training-Free Face Image Quality Assessment With Vision Transformers

Ozgur, G., Caldeira, E., Chettaoui, T., Kolf, J. N., Huber, M., Damer, N., & Boutros, F. (2026) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw68408.2026.00131

Stimulus-Design Confounds in Progressive Rendered 3-D VLM Evaluation

Park, H., Choi, J., & Park, Y. (2026) · IEEE Access · doi:10.1109/access.2026.3722232

Efficient AI-Generated Image Detection via Confidence-Aware Cascading

Chen, X., Yu, Y., Wang, H., Zhang, J., & Guo, E. (2026) · doi:10.1109/icbdse70225.2026.11635577

Resource-efficient Semantic Coding Schemes with Manifold-constrained Hyper-connectionsPreprint

Fu, J., & Xiao, M. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2608.13253

ASFFNet: Adaptive structural-functional brain connectome fusion for dynamic cognitive inference

Wang, C., Wang, H., Liu, T., Lv, P., Fu, B., Huang, W., Pang, Y., Han, S., Chen, H., Xu, M., et al. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104708

Bamboo Slips Scribe Verification Based on Multi-Scale Dynamic Routing and Dual-Sparse Channel Attention

Cui, Q., Li, M., Guo, Y., & Zhang, Y. (2026) · International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl70130.2026.11637434

Adaptive Local LLM Compression Under Dynamic Edge Constraints

Singh, P., Ashraf, A., Sharma, S., Soni, D., & Tomar, J. (2026) · International Conference on Power, Control and Computing Technologies (ICPC2T) · doi:10.1109/icpc2t68221.2026.11646233

Learned region selection and deep learning for generalized source identification of additively manufactured parts

Bimrose, M. V., Shin, J., Zheng, W., Tawfick, S., & King, W. P. (2026) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-026-02929-w

LANDformer: Layered Adaptive Local Patterns and Dynamic Global Routing Transformer for Time Series Forecasting

Sun, Y., Xu, Y., Lyu, W., Guo, Q., & Wang, C. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3725679

Sample-Adaptive Infrared Small Target Detection via Dynamic Path Inference With Early Exit and Deep Reinforcement Learning

Liu, J., Xiong, Q., Fang, S., Du, Y., Liu, J., Hui, B., Qiu, S., & Yongjian, S. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3726061

Deployment-oriented pruning for real-time object detection: a survey

Zhou, G., & Zhang, D. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07416-6

SKIPNAS: Designing Layer-Skipping Networks via Compute-Aware Neural Architecture Search

van Middendorp, L., Khalilian, S., & Meratnia, N. (2026) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot69657.2026.00112

Shape-Aware Gated Early Exit Networks for Efficient Edge AI

Truong, C. Q., & Truong, S. N. (2026) · IEEE Access · doi:10.1109/access.2026.3727805

Efficient Vision-Language Fusion-Cascade for Explainable Manufacturing Workplace Safety MonitoringPreprint

Dongkyu, Y., & Choi, S.-H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7379938

EcoFair: Energy-efficient inference routing for edge AI under data degradation

Anoosha, M., Thakker, D., Paxton, K., Aslansefat, K., Mishra, B. K., Ahmad, B., & Kureshi, R. R. (2026) · Ad Hoc Networks · doi:10.1016/j.adhoc.2026.104403

High-Frequency Guided Feature Aggregation for Strip Steel Surface Defect Detection

Sun, Y., Xu, C., Guo, H., Zhang, L., Feng, Y., Zhang, W., & Tang, Y. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3728911

A gated risk assessment and alarm-screening framework for wildfire monitoring in transmission-line corridors

Xu, X., He, Z., Lu, J., Ye, Y., Feng, T., & Pi, X. (2026) · Electric Power Systems Research · doi:10.1016/j.epsr.2026.114058

The Joule Point: an Energy-Optimal Operating Point for AI InferencePreprint

Apartsin, A., & Aperstein, Y. (2026) · arXiv (Cornell University) · doi:10.13140/rg.2.2.20385.36964

Risk-Aware Adaptive Video Processing with Freshness Control for Low-Latency Assistive VisionPreprint

Shagor, M. S. I. (2026) · Research Square · doi:10.21203/rs.3.rs-10902455/v1

Adaptive Edge Intelligence for Low-Latency Assistive Vision under Dynamic Network and Computational ConstraintsPreprint

Shagor, M. S. I. (2026) · Research Square · doi:10.21203/rs.3.rs-10901871/v1

MSP-Edge: Multi-Stage Pruning With Neuron Reconstruction for Privacy-Aware Edge Vision-Based Crowd Sensing in IoT-Enabled Rail Transit Systems

Li, X., Wang, C., & Hu, B. (2026) · IEEE Access · doi:10.1109/access.2026.3732110

SnakeSAM: A topology-preserving foundation model for medical curvilinear segmentation

Li, J., Zhang, X., Cheng, J., Lu, Y., Su, Y., Wang, W., & Yu, J. (2026) · Array · doi:10.1016/j.array.2026.101221

The Efficiency-Decentralization-Security Trilemma: A Co-Design Framework for Lightweight, Decentralized AI in Cyber-Physical Systems

Ramadan, M. N. A., & Saygın, H. (2026) · AI · doi:10.3390/ai7090358

Meta-Learning-Driven Adaptive Control for Multi-Exit DNN Splitting at the Edge

Wang, L., Xie, J., Hao, H., & Shi, H. (2026) · IoT · doi:10.3390/iot7030080

ARMOR: Pareto‐Aware Dynamic Routing for Energy‐Efficient Heterogeneous Multimodal Perception in Autonomous Driving

Liu, J., Zhang, Y., Chen, Y., & Wang, Y. (2026) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.70397

Early Exit Neural Networks: A Comparative Study Against Static Models and Dynamic Models Across Multi-Domain Datasets

Udhaykrishna, Y., Ezhilvendhan, T. K., Tarun, M., & Thangakumar, J. (2026) · International Conference on Inventive Systems and Control (ICISC) · doi:10.1109/icisc69558.2026.11681425

Optimal Model Activation Policies for Inference Networks of Large Language ModelsPreprint

Charalampakos, F., Alam, M. I. I., Koutsopoulos, I., & Kar, K. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2609.15992

Compute-Adaptive Risk Control: A Finite-Sample Calibration Layer for Early-Exit and Cascade Inference

Jang, S., Park, S., Woo, S., & Lee, A. (2026) · IEEE Access · doi:10.1109/access.2026.3735066

AdvLIF: An Adversarially and Physics-Inspired Framework for Virtual Staining and Nucleus Segmentation

Yu, Q., Shao, C., Yang, R., Lyu, Z., He, S., Bai, C., & Wang, Z. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02320-4

Elastic Deep Neural Network for Adaptive Feature Extraction in Remote Sensing Image Retrieval

Kasetty, S. B., Rajakumar, K., Margret, I. N., Manikandan, S., & Mohammad, A. (2026) · IEEE Access · doi:10.1109/access.2026.3734223

AnytimeHydroTwin: A deployment-valid inference-orchestration layer for AI-driven hydrological digital twins

Zhu, Y., & Liu, Q. (2026) · Journal of Hydrology · doi:10.1016/j.jhydrol.2026.136485

A hybrid SEM–machine learning framework for partner evaluation in innovation networks: Methodological development and empirical demonstration

Labo-Popoola, A. O., & Owolabi, H. A. (2026) · Journal of Open Innovation Technology Market and Complexity · doi:10.1016/j.joitmc.2026.100883

Representation-similarity network stitching for efficient parameter reduction in multi-exit networks

Zhang, L., Li, J., & Guo, Y. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.116538

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)