Results 61 to 70 of about 720,784 (299)
Structured Sparsity with Group-Graph Regularization
, 2015 In many learning tasks with structural properties, struc-tural sparsity methods help induce sparse models, usu-ally leading to better interpretability and higher gener-alization performance.
Jia-jun Chen +9 more
core +1 more source
Advanced Robotics Research, EarlyView.This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source
Double regularized matrix factorization for image classification and clustering
EURASIP Journal on Image and Video Processing, 2018 Feature selection, which aims to select an optimal feature subset to avoid the “curse of dimensionality,” is an important research topic in many real-world applications.
Wei Zhou +4 more
doaj +1 more source
Consistent Semi-Supervised Graph Regularization for High Dimensional Data
, 2021 International audienceSemi-supervised Laplacian regularization, a standard graph-based approach for learning from both labelled and unlabelled data, was recently demonstrated to have an insignificant high dimensional learning efficiency with respect to ...
Mai, Xiaoyi, Couillet, Romain
core +1 more source
Advanced Science, EarlyView.This study investigates how the internal structure of fiber‐reinforced ceramic composites affects their resistance to damage. By combining 3D X‐ray imaging with acoustic emission monitoring during mechanical testing, it reveals how silicon distribution influences crack formation.
Yang Chen +7 more
wiley +1 more source
IEEE AccessGraph Neural Networks have emerged as powerful tools for analyzing graph-structured data. However, their performance often varies across datasets due to challenges such as noisy edges, sparse connectivity, and over-smoothing in deep layers.
Vinay Santhosh Chitla +3 more
doaj +1 more source
An Integrated NLP‐ML Framework for Property Prediction and Design of Steels
Advanced Science, EarlyView.This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju +5 more
wiley +1 more source
Item-Item Collaborative Filtering via Graph Regularization
, 2022 A recommendation algorithm aims to predict the quality of a user's future interaction with certain items based on their previous interactions. As research progresses, these algorithms are becoming increasingly more complicated with the use of machine ...
Koper ook geschreven Jansen, Melle (author)
core
Graph approximation and generalized Tikhonov regularization for signal deblurring
, 2021 Given a compact linear operator K, the (pseudo) inverse K^† is usually substituted by a family of regularizing operators Rα which depends on K itself. Naturally, in the actual computation we are forced to approximate the true continuous operator K with a
Bianchi D. +3 more
core +1 more source
Advanced Science, EarlyView.Using single‐cell transcriptomic and epigenomic profiling, researchers mapped microglial dysfunction across the lifespan of db/db mice. The authors uncovered a nonlinear microglial remodeling featuring early inflammatory priming, a mid‐stage transcriptional inflection, and late dysfunction.
Jizhou Zhang +6 more
wiley +1 more source