Results 41 to 50 of about 51,507 (261)
Spatial Bayesian neural networks
Spatial Statistics35 pages, 21 ...
Andrew Zammit-Mangion +4 more
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Directed evolution of enzymes at the crossroads of tradition and innovation
FEBS Open Bio, EarlyView.An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova +2 more
wiley +1 more source
On the relative expressiveness of Bayesian and neural networks
International Journal of Approximate Reasoning, 2019 A neural network computes a function. A central property of neural networks is that they are "universal approximators:" for a given continuous function, there exists a neural network that can approximate it arbitrarily well, given enough neurons (and some additional assumptions).
Arthur Choi +2 more
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Prediction of concrete fatigue durability using Bayesian neural networks
Computer Assisted Methods in Engineering and Science, 2022 The utility of Bayesian neural networks to predict concrete fatigue durability as a function of concrete mechanical parameters of a specimen and characteristics of the loading cycle is investigated.
Marek Słoński
doaj
Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach
Real Estate Management and Valuation, 2020 Although deep learning-based valuation models are spreading throughout the real estate industry following the artificial intelligence boom, property owners and investors continue to doubt the accuracy of the results.
Lee Changro, Park Keith Key-Ho
doaj +1 more source
Global Rather Than Vertical‐Selective Saccadic Abnormalities in Progressive Supranuclear Palsy
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To test whether vertical saccades are preferentially affected in Progressive Supranuclear Palsy (PSP). Methods PSP patients (n = 24) were compared to age‐matched controls (n = 94) and two degenerative groups (Alzheimer's disease, n = 20; Lewy body disease, n = 50).
Duy Duan Nguyen +6 more
wiley +1 more source
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 +2 more
wiley +1 more source
Bayesian Neural Networks: An Introduction and Survey [PDF]
, 2020 Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning tasks such as detection, regression and classification across the domains of computer vision, speech recognition and natural language processing. Despite their success, they are often implemented in a frequentist scheme, meaning they are unable to reason ...
Goan, Ethan, Fookes, Clinton
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Time-varying probability model of the reduction in bending capacity of RC beams due to corrosion of steel bars [PDF]
Archives of Civil EngineeringDue to the reduction in bending capacity of RC beams being affected by multiple stochastic uncertainties, employing a deterministic function model to study the bending capacity of RC beams often leads to analysis errors that are difficult to accept. This
Peng Tan, Shibin Kang, Zhanqiang Feng
doaj +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