Results 51 to 60 of about 51,507 (261)

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   +2 more sources

Advanced machine learning artificial neural network classifier for lithology identification using Bayesian optimization

Frontiers in Earth Science
Identifying lithology is crucial for geological exploration, and the adoption of artificial intelligence is progressively becoming a refined approach to automate this process.
Saâd Soulaimani, Saâd Soulaimani, Ayoub Soulaimani, Kamal Abdelrahman, Abdelhalim Miftah, Mohammed S. Fnais, Biraj Kanti Mondal   +6 more
doaj   +1 more source

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, Vincent Rat, Quentin Dubreuil, Patrice Valorge, Simon Goutier, Alain Denoirjean, Olivier Messe, Céline Ruelle, Jean‐Philippe Poli   +8 more
wiley   +1 more source

Flat Seeking Bayesian Neural Networks

Advances in Neural Information Processing Systems 36, 2023
Bayesian Neural Networks (BNNs) provide a probabilistic interpretation for deep learning models by imposing a prior distribution over model parameters and inferring a posterior distribution based on observed data. The model sampled from the posterior distribution can be used for providing ensemble predictions and quantifying prediction uncertainty.
Van-Anh Nguyen, Long Tung Vuong, Hoang Phan, Thanh-Toan Do, Dinh Q. Phung, Trung Le 0001   +5 more
openaire   +3 more sources

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, Wongyu Lee, Youjung Seo, Choongseop Lee, Woojoon Seok, Jongkil Park, Donggyu Sim, Cheolsoo Park   +7 more
doaj   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

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, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Functional Variational Bayesian Neural Networks

CoRR, 2019
ICLR ...
Shengyang Sun, Guodong Zhang 0006, Jiaxin Shi, Roger B. Grosse   +3 more
openaire   +3 more sources

Bayesian Neural Network Priors Revisited

, 2021
Isotropic Gaussian priors are the de facto standard for modern Bayesian neural network inference. However, it is unclear whether these priors accurately reflect our true beliefs about the weight distributions or give optimal performance. To find better priors, we study summary statistics of neural network weights in networks trained using stochastic ...
Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison   +7 more
openaire   +3 more sources

Agricultural Pest Image Recognition Algorithm Based on Convolutional Neural Network and Bayesian Method

IEEE Access
Aiming at the limitations of existing agricultural pest image recognition technology, a novel agricultural pest recognition algorithm based on convolutional neural network and Bayesian method is proposed. During the process, convolutional neural networks
Ling Zhang, Fahui Wu, Wensen Yu
doaj   +1 more source

A stochastic artificial neural network model for investigating street vendor behavior in a night market

International Journal of Distributed Sensor Networks, 2016
This article offers a hybrid computational approach that combines an artificial neural network with Bayesian probability to improve on the conventional artificial neural network model.
Pao-Kuan Wu, Tsung-Chih Hsiao, Ming Xiao
doaj   +1 more source