Results 61 to 70 of about 1,925,652 (255)
Multimode Oxide‐Based Optoelectronic Memtransistor for In‐Sensor Vision Processing
Advanced Functional Materials, EarlyView.A multimode optoelectronic memtransistor (OEMT) is demonstrated for vision explainable artificial intelligence (VXAI) hardware. By integrating optical sensing, electrical masking, and non‐volatile memory, the device enables key operations required for generating saliency information.
Min Gu Lee +10 more
wiley +1 more source
Neighbor Regularized Bayesian Optimization for Hyperparameter Optimization
CoRR, 2022 Bayesian Optimization (BO) is a common solution to search optimal hyperparameters based on sample observations of a machine learning model. Existing BO algorithms could converge slowly even collapse when the potential observation noise misdirects the optimization.
Lei Cui +4 more
openaire +4 more sources
Hyperparameters of Multilayer Perceptron with Normal Distributed Weights [PDF]
, 2020 Multilayer Perceptrons, Recurrent neural networks, Convolutional networks, and others types of neural networks are widespread nowadays. Neural Networks have hyperparameters like number of hidden layers, number of units for each hidden layer, learning ...
Karaki, Y., Ivanov, N.
core +1 more source
Advanced Functional Materials, EarlyView.This work presents a closed‐loop optimization workflow integrating CatBot, an automated synthesis and testing system, to discover NiMo hydrogen‐evolution catalysts. CatBot synthesised and evaluated 228 unique samples, identifying the best performing catalysts for transfer to nickel‐foam electrodes. The best candidate is highly active, reaching 1 Acm−2${
Paolo Vincenzo Freiesleben de Blasio +7 more
wiley +1 more source
Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimizationb
Journal of Electronic Science and Technology, 2019 Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models.
Jia Wu +5 more
doaj +1 more source
Heart disease classification using optimized Machine learning algorithms
Iraqi Journal for Computer Science and Mathematics, 2023 Early detection of heart disease is exceptionally critical to saving the lives of human beings. Heart attack is one of the primary causes of high death rates throughout the world, due to the lack of human and logistical resources in addition to the high
Mohammad Abood Kadhim +1 more
doaj +1 more source
Full‐Field Damage Monitoring in Architected Lattices Using In situ Electrical Impedance Tomography
Advanced Functional Materials, EarlyView.In situ electrical impedance tomography (EIT) turns 3D‐printed, CNT‐infused architected lattices into full‐field damage‐imaging systems. Tunable Voronoi‐based geometries act as active sensing architectures, enabling conductivity maps to detect early‐stage damage and localise sequential ligament fracture before catastrophic failure.
Akash Deep +4 more
wiley +1 more source
Optimizing Hyperparameters in Deep Learning Models Using Bayesian Optimization [PDF]
Hyperparameter optimization is a crucial aspect of deep learning, as the choice of hyperparameters significantly influences model performance.
Kian Hemant, Madan
core +2 more sources
Tuning Bayesian optimization for materials synthesis: simulating two- and three-dimensional cases
Science and Technology of Advanced Materials: Methods, 2023 Compared to the optimization of a 1D synthesis parameter in materials synthesis, the optimization of multi-dimensional synthesis parameters is challenging for researchers.
Han Xu +8 more
doaj +1 more source
Advanced Functional Materials, EarlyView.Mask‐encoded UV exposure translates digital color information into pixel‐resolved structural‐color patterns in hydroxypropyl cellulose gels. Gaussian‐process‐guided calibration links UV processing conditions, wavelength, and mask opacity, enabling parallel fabrication of multicolor images in a single 10 s exposure.
Mengting Ye +8 more
wiley +1 more source