Results 61 to 70 of about 2,019,981 (302)
Implantable Ionic Memristors Based on Natural Polymer Heterojunctions
Advanced Functional Materials, EarlyView.We report an implantable natural polymer‐based ionic memristor composed of hyaluronic acid, chitosan, and PDMS. The device achieved 98.94% accuracy in MNIST classification while reducing training time by 36.8% compared with a conventional artificial neural network (ANN).
Dong‐yup Lee +6 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
Hyperparameter Optimization for Effort Estimation
CoRR, 2018 Software analytics has been widely used in software engineering for many tasks such as generating effort estimates for software projects. One of the "black arts" of software analytics is tuning the parameters controlling a data mining algorithm. Such hyperparameter optimization has been widely studied in other software analytics domains (e.g.
Tianpei Xia +5 more
openaire +3 more sources
Artificial Neural Network Hyperparameters Optimization: A Survey [PDF]
, 2022 Machine-learning (ML) methods often utilized in applications like computer vision, recommendation systems, natural language processing (NLP), as well as user behavior analytics.
Kadhim, Zahraa Saddi +2 more
core
Goal-oriented sensitivity analysis of hyperparameters in deep learning
, 2023 International audienceTackling new machine learning problems with neural networks always means optimizing numerous hyperparameters that define their structure and strongly impact their performances.
Congedo, Pietro Marco +7 more
core +1 more source
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
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.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
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