Results 31 to 40 of about 236,656 (279)

Ensemble Kalman filter for neural network based one-shot inversion

, 2020
We study the use of novel techniques arising in machine learning for inverse problems. Our approach replaces the complex forward model by a neural network, which is trained simultaneously in a one-shot sense when estimating the unknown parameters from ...
Guth, Philipp A., Schillings, Claudia, Weissmann, Simon   +2 more
core   +1 more source

Bayesian Neural Networks

, 2018
This paper describes and discusses Bayesian Neural Network (BNN). The paper showcases a few different applications of them for classification and regression problems. BNNs are comprised of a Probabilistic Model and a Neural Network. The intent of such a design is to combine the strengths of Neural Networks and Stochastic modeling.
Mullachery, Vikram, Khera, Aniruddh, Husain, Amir   +2 more
openaire   +2 more sources

Using topological data analysis for building Bayesan neural networks

Научно-технический вестник информационных технологий, механики и оптики
For 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, N. F. Gusarova, D. A. Dobrenko, K. S. Pankova, I. V. Tomilov   +4 more
doaj   +1 more source

On the determination of probability density functions by using Neural Networks [PDF]

, 1998
It is well known that the output of a Neural Network trained to disentangle between two classes has a probabilistic interpretation in terms of the a-posteriori Bayesian probability, provided that a unary representation is taken for the output patterns ...
Aurelio Juste, Garrido, Hertz, Lluís Garrido, Müller, Papoulis, Ruck, Rumelhart, Rumelhart, Schmidt, Wan, Werbos   +11 more
core   +3 more sources

Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning

IJAIN (International Journal of Advances in Intelligent Informatics), 2021
The blast furnace is the principal method of producing cast iron. In the production of cast iron, the control of silicon is vital because this impurity is harmful to almost all steels.
Wandercleiton Cardoso, Renzo di Felice
doaj   +1 more source

Bayesian Policy Gradients via Alpha Divergence Dropout Inference

, 2011
Policy gradient methods have had great success in solving continuous control tasks, yet the stochastic nature of such problems makes deterministic value estimation difficult.
Henderson, Peter, Doan, Thang, Islam, Riashat, Meger, David   +3 more
core   +3 more sources

Spatial Bayesian neural networks

Spatial Statistics
35 pages, 21 ...
Andrew Zammit-Mangion, Michael D. Kaminski, Ba-Hien Tran, Maurizio Filippone, Noel Cressie   +4 more
openaire   +3 more sources

Bayesian Neural Networks for Sparse Coding [PDF]

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
Deep learning is actively used in the area of sparse coding. In current deep sparse coding methods uncertainty of predictions is rarely estimated, thus providing the results that lack the quantitative justification. Bayesian learning provides the way to estimate the uncertainty of predictions in neural networks (NNs) by imposing the prior distributions
Kuzin, D., Isupova, O., Mihaylova, L.
openaire   +2 more sources

Effects of Neural Assembles in Causal Inference Based on an Entropy-Maximization Bayesian Neural Network

IEEE Access
Causal inference is an important function of the nervous system. To explore causal inference, Bayesian inference performs as the possible framework, mapping neural implementation onto various cortical areas.
Weisi Liu, Xiaogang Pan
doaj   +1 more source

Scaling Up Bayesian Neural Networks with Neural Networks

, 2023
25 ...
Moslemi, Zahra, Meng, Yang, Lan, Shiwei, Shahbaba, Babak   +3 more
openaire   +2 more sources