Results 41 to 50 of about 224,776 (267)
Using topological data analysis for building Bayesan neural networks
Научно-технический вестник информационных технологий, механики и оптикиFor the first time, a simplified approach to constructing Bayesian neural networks is proposed, combining computational efficiency with the ability to analyze the learning process.
A. S. Vatian +4 more
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Scaling Up Bayesian Neural Networks with Neural Networks
, 2023 25 ...
Moslemi, Zahra +3 more
openaire +2 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Poststroke fatigue (PSF) and frailty share substantial overlap in their manifestations, yet previous research has yielded conflicting results due to the use of heterogeneous frailty assessment tools. Objective To evaluate the independent impact of frailty on PSF using a unified measurement system (Tilburg Frailty Indicator, TFI ...
Chuan‐Bang Chen +6 more
wiley +1 more source
Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis
EPJ Web of Conferences, 2017 Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular.
Chernoded Andrey +3 more
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, 2019 Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior distribution.
Ashukha, Arsenii +4 more
core +1 more source
Integrative Approaches for DNA Sequence‐Controlled Functional Materials
Advanced Functional Materials, EarlyView.DNA is emerging as a programmable building block for functional materials with applications in biomimicry, biochemical, and mechanical information processing. The integration of simulations, experiments, and machine learning is explored as a means to bridge DNA sequences with macroscopic material properties, highlighting current advances and providing ...
Aaron Gadzekpo +4 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
Journal of the Brazilian Computer Society, 1997 Bayesian techniques have been developed over many years in a range of different fields, but have only recently been applied to the problem of learning in neural networks. As well as providing a consistent framework for statistical pattern recognition, the Bayesian approach offers a number of practical advantages including a solution to the problem of ...
openaire +3 more sources
Advanced Functional Materials, EarlyView.This study establishes a materials‐driven framework for entropy generation within standard CMOS technology. By electrically rebalancing gate‐oxide traps and Si‐channel defects in foundry‐fabricated FDSOI transistors, the work realizes in‐materia control of temporal correlation – achieving task adaptive entropy optimization for reinforcement learning ...
Been Kwak +14 more
wiley +1 more source
Advanced Functional Materials, EarlyView.Permanent magnets derive their extraordinary strength from deep, universal electronic‐structure principles that control magnetization, anisotropy, and intrinsic performance. This work uncovers those governing rules, examines modern modeling and AI‐driven discovery methods, identifies critical bottlenecks, and reveals electronic fingerprints shared ...
Prashant Singh
wiley +1 more source