Results 101 to 110 of about 43,983 (273)
Agribusiness, EarlyView.ABSTRACT This study investigates the financial literacy (FL) of Swedish farmers, its linkages to farmer characteristics, management accounting practices and farm outcomes by surveying Swedish Farm Accountancy Data Network farmers. Using item response theory, we expand the existing FL measurement specifically to the farming context, assess measurement ...
Uliana Gottlieb, Helena Hansson
wiley +1 more source
V‐UNet: Medical Image Segmentation Based on Variational Attention Mechanism
CAAI Transactions on Intelligence TechnologyAccurate medical image segmentation plays a crucial role in improving the precision of computer‐aided diagnosis. However, complex boundary shapes, low contrast and blurred anatomical structures make fine‐grained segmentation a challenging task ...
Yang Zhang +6 more
doaj +1 more source
An R package VIGoR for joint estimation of multiple linear learners with variational Bayesian inference. [PDF]
Bioinformatics, 2022 Onogi A, Arakawa A.
europepmc +1 more source
AI in chemical engineering: From promise to practice
AIChE Journal, EarlyView.Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew +4 more
wiley +1 more source
Variational Sequential Monte Carlo
, 2018 Many recent advances in large scale probabilistic inference rely on variational methods. The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member
Blei, David M. +3 more
core
BAR: Bayesian Activity Recognition using variational inference
CoRR, 2018 Uncertainty estimation in deep neural networks is essential for designing reliable and robust AI systems. Applications such as video surveillance for identifying suspicious activities are designed with deep neural networks (DNNs), but DNNs do not provide uncertainty estimates.
Ranganath Krishnan +2 more
openaire +2 more sources
Computational study of permeability in cardboard coating layers
AIChE Journal, EarlyView.Abstract We develop a virtual material structure model based on a combination of tessellations and Gaussian random fields for a coating layer of paperboard used for packaging and designed to facilitate printing on the surface. To fit the model to tomographic image data acquired using combined focused ion beam and scanning electron microscopy (FIB‐SEM),
Sandra Barman +6 more
wiley +1 more source
Variational Bayesian inference for association over phylogenetic trees for microorganisms. [PDF]
J Appl Stat, 2022 Hao X, Eskridge KM, Wang D.
europepmc +1 more source
A trust-region method for stochastic variational inference with applications to streaming data
, 2015 Stochastic variational inference allows for fast posterior inference in complex Bayesian models. However, the algorithm is prone to local optima which can make the quality of the posterior approximation sensitive to the choice of hyperparameters and ...
Hoffman, Matthew D., Theis, Lucas
core
Advanced Intelligent Discovery, EarlyView.A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour +5 more
wiley +1 more source