Results 61 to 70 of about 3,905,952 (289)
dynoGP: Deep Gaussian Processes for Dynamic System Identification
International Journal of Adaptive Control and Signal Processing, EarlyView.This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
wiley +1 more source
Generalisation for neural networks through data sampling and training procedures, with applications to streamflow predictions [PDF]
Hydrology and Earth System Sciences, 2004 Since the 1990s, neural networks have been applied to many studies in hydrology and water resources. Extensive reviews on neural network modelling have identified the major issues affecting modelling performance; one of the most important is ...
F. Anctil +3 more
doaj
Bayesian neural networks for fast SUSY predictions
Physics Letters B, 2021 One of the goals of current particle physics research is to obtain evidence for new physics, that is, physics beyond the Standard Model (BSM), at accelerators such as the Large Hadron Collider (LHC) at CERN.
B.S. Kronheim +3 more
doaj +1 more source
Probabilistic inference in Bayesian neural networks [PDF]
Despite widespread applicability and the dominant role in machine learning, neural networks remain highly non-transparent and are often regarded as black boxes due to the lack of human-understandable interpretations. Conventional deep models tend to be
Sheinkman, Alisa
core +1 more source
Advanced Engineering Materials, EarlyView.We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang +2 more
wiley +1 more source
PREDICTION OF ALZHEIMER'S DISEASE USING BAYESIAN NEURAL NETWORKS
Компютерні системи та інформаційні технологіїThis article presents a methodology for optimizing Bayesian neural networks and their application to complex prediction tasks, with a focus on diagnosing Alzheimer’s disease.
Сергій ГЛАДІГОЛОВ +1 more
doaj +1 more source
Bayesian Perceptron: Towards fully Bayesian Neural Networks [PDF]
2020 59th IEEE Conference on Decision and Control (CDC), 2020 Accepted for publication at the 59th IEEE Conference on Decision and Control (CDC) 2020.
openaire +3 more sources
Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying
Advanced Engineering Materials, EarlyView.An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara +8 more
wiley +1 more source
Bayesian continual learning via spiking neural networks
Frontiers in Computational Neuroscience, 2022 Among the main features of biological intelligence are energy efficiency, capacity for continual adaptation, and risk management via uncertainty quantification.
Nicolas Skatchkovsky +2 more
doaj +1 more source
Advanced Engineering Materials, EarlyView.This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer +4 more
wiley +1 more source