Results 101 to 110 of about 6,914,944 (265)

Rethinking Weight Decay for Efficient Neural Network Pruning. [PDF]

J Imaging, 2022
Tessier H, Gripon V, Léonardon M, Arzel M, Hannagan T, Bertrand D.   +5 more
europepmc   +1 more source

Calibration‐Free Electromyography Motor Intent Decoding Using Large‐Scale Supervised Pretraining

Advanced Intelligent Systems, EarlyView.
Calibration‐free electromyography motor intent decoding is enabled through large‐scale supervised pretraining across heterogeneous datasets. A Spatially Aware Feature‐learning Transformer processes variable channel counts and electrode geometries, allowing transfer across users and recording setups. On a held‐out benchmark, fine‐tuned cross‐user models
Alexander E. Olsson, Nebojša Maleševic, Anders Björkman, Christian Antfolk   +3 more
wiley   +1 more source

Differentiable Network Pruning via Polarization of Probabilistic Channelwise Soft Masks. [PDF]

Comput Intell Neurosci, 2022
Ma M, Wang J, Yu Z.
europepmc   +1 more source

Integrating Artificial Intelligence With Droplet‐Based Microfluidics: Advances, Challenges, and Emerging Opportunities

Advanced Intelligent Systems, EarlyView.
Droplet‐based microfluidics enables precise, high‐throughput microscale reactions but continues to face challenges in scalability, reproducibility, and data complexity. This review examines how artificial intelligence enhances droplet generation, detection, sorting, and adaptive control and discusses emerging opportunities for clinical and industrial ...
Junyan Lai, Sukrit Gaira, Rongfeng Wang, Qingtian Zhang, Ming Li, Liao Wu, Diangeng Li, Yi Li, Chengchen Zhang, Jinhong Guo, Shi‐Yang Tang   +10 more
wiley   +1 more source

An automatic pruning method for SAR target detection based on multitask reinforcement learning

International Journal of Applied Earth Observations and Geoinformation
In recent years, research on synthetic aperture radar (SAR) target detection based on deep learning methods has made substantial progress in model accuracy.
Huiyao Wan, Jie Chen, Pazlat Nurmamat, Hongcheng Zeng, Wei Yang, Tao Xu, Bocai Wu, Jie Chen, Zhixiang Huang, Paulo S.R. Diniz   +9 more
doaj   +1 more source

Multi-Class Classification of Medical Data Based on Neural Network Pruning and Information-Entropy Measures. [PDF]

Entropy (Basel), 2022
Sánchez-Gutiérrez ME, González-Pérez PP.   +1 more
europepmc   +1 more source

Optimizing 3D Bin Packing of Heterogeneous Objects Using Continuous Transformations in SE(3)

Advanced Intelligent Systems, EarlyView.
This article presents a method for solving the three‐dimensional bin packing problem for heterogeneous objects using continuous rigid‐body transformations in SE(3). A heuristic optimization framework combines signed‐distance functions, neural network approximations, point‐cloud bin modeling, and physics simulation to ensure feasibility and stability ...
Michele Angelini, Marco Carricato
wiley   +1 more source

Retinal Vessel Segmentation: A Comprehensive Review From Classical Methods to Deep Learning Advances (1982–2025)

Advanced Intelligent Systems, EarlyView.
Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal, Jan Kubíček, Jana Bahrová, Alice Varyšová, Martin Augustýnek, Marek Penhaker, Juraj Timkovič   +6 more
wiley   +1 more source

Tailored Channel Pruning: Achieve Targeted Model Complexity Through Adaptive Sparsity Regularization

IEEE Access
In deep learning, the size and complexity of neural networks have been rapidly increased to achieve higher performance. However, this poses a challenge when utilized in resource-limited environments, such as mobile devices, particularly when trying to ...
Suwoong Lee, Yunho Jeon, Seungjae Lee, Junmo Kim   +3 more
doaj   +1 more source

High-Efficient Parameter-Pruning Algorithm of Decision Tree for Large Dataset [PDF]

Jisuanji gongcheng
Decision tree(DT) have a good effect on data classification but easily develop overfitting. The solution to this problem is to prune the DT. However, the pruning algorithm has shortcomings; for example, prepruning is prone to underfitting, the ...
Zhaoxian XIE, Xingmin ZOU, Wenjing ZHANG
doaj   +1 more source