Results 61 to 70 of about 234,090 (282)
Investigating Performance of Bayesian and Levenberg-Marquardt Neural Network in Comparison Classical Models in Stock Price Forecasting [PDF]
تحقیقات مالی, 2017 Accurate forecasting of stock prices according to high volatility and inherent risk of stock market is a major concern of investors and financial analysts, hence applying novel approaches to predict the stock priceisan inevitable necessity.
Hossein Fakhari +2 more
doaj +1 more source
Unsupervised Spiking Neural Network with Dynamic Learning of Inhibitory Neurons
Sensors, 2023 A spiking neural network (SNN) is a type of artificial neural network that operates based on discrete spikes to process timing information, similar to the manner in which the human brain processes real-world problems.
Geunbo Yang +7 more
doaj +1 more source
Raiders of the Lost Architecture: Kernels for Bayesian Optimization in Conditional Parameter Spaces [PDF]
, 2014 In practical Bayesian optimization, we must often search over structures with differing numbers of parameters. For instance, we may wish to search over neural network architectures with an unknown number of layers. To relate performance data gathered for
Duvenaud, David +4 more
core
Advanced Materials, EarlyView.This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley +1 more source
Research on Fault Diagnosis of Chillers Based on Improved BP Network
Zhileng xuebao, 2015 The overall detection rate using conventional neural networks to detect and diagnose the chillers’ fault is low, even this method can’t detect the fault completely.
Shi Shubiao +6 more
doaj
Bayesian Neural Networks: Essentials
, 2021 Bayesian neural networks utilize probabilistic layers that capture uncertainty over weights and activations, and are trained using Bayesian inference. Since these probabilistic layers are designed to be drop-in replacement of their deterministic counter parts, Bayesian neural networks provide a direct and natural way to extend conventional deep neural ...
openaire +2 more sources
Hydrogel‐Based Functional Materials: Classifications, Properties, and Applications
Advanced Materials Technologies, EarlyView.Conductive hydrogels have emerged as promising materials for smart wearable devices due to their outstanding flexibility, multifunctionality, and biocompatibility. This review systematically summarizes recent progress in their design strategies, focusing on monomer systems and conductive components, and highlights key multifunctional properties such as
Zeyu Zhang, Zao Cheng, Patrizio Raffa
wiley +1 more source
Frontiers in Earth ScienceExperimental variogram modelling is an essential process in geostatistics. The use of artificial intelligence (AI) is a new and advanced way of automating experimental variogram modelling.
Saâd Soulaimani +6 more
doaj +1 more source
Improved Uncertainty Quantification for Neural Networks With Bayesian Last Layer
IEEE Access, 2023 Uncertainty quantification is an important task in machine learning - a task in which standard neural networks (NNs) have traditionally not excelled. This can be a limitation for safety-critical applications, where uncertainty-aware methods like Gaussian
Felix Fiedler, Sergio Lucia
doaj +1 more source
Reducing Personalization Time and Energy Cost While Walking Outdoors with a Portable Exosuit
Advanced Robotics Research, EarlyView.Rapid Real‐World Optimization! An AF‐based human‐in‐the‐loop optimization strategy rapidly personalizes a portable hip extension exosuit for incline walking. Real‐time Bayesian optimization of assistive force significantly reduces metabolic energy—up to 16.2%—while converging in just 3 min 24 s.
Kimoon Nam +7 more
wiley +1 more source