Results 21 to 30 of about 51,507 (261)

Bayesian Neural Networks

CoRR, 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.
Vikram Mullachery, Aniruddh Khera, Amir Husain   +2 more
openaire   +2 more sources

Bayesian Neural Networks for Aroma Classification [PDF]

Journal of Chemical Information and Computer Sciences, 2002
Bayesian Neural Networks (BNNs) are investigated to test their potential to distinguish between different aroma impressions. Special attention is thereby drawn on mixed aroma impressions, resulting from the flavor description of a single compound with more than one aroma quality.
Klocker, Johanna, Wailzer, Bettina, Buchbauer, Gerhard, Wolschann, Karl Peter   +3 more
openaire   +3 more sources

Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks

Nature Communications, 2022
Designing efficient Bayesian neural networks remains a challenge. Here, the authors use the cycle variation in the programming of the 2D memtransistors to achieve Gaussian random number generator-based synapses, and combine it with the complementary 2D ...
Amritanand Sebastian, Rahul Pendurthi, Azimkhan Kozhakhmetov, Nicholas Trainor, Joshua A. Robinson, Joan M. Redwing, Saptarshi Das   +6 more
doaj   +1 more source

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
Danil Kuzin, Olga Isupova, Lyudmila Mihaylova   +2 more
openaire   +2 more sources

Bayesian Neural Network Ensembles

CoRR, 2018
Ensembles of neural networks (NNs) have long been used to estimate predictive uncertainty; a small number of NNs are trained from different initialisations and sometimes on differing versions of the dataset. The variance of the ensemble's predictions is interpreted as its epistemic uncertainty.
Tim Pearce, Mohamed Zaki, Andy Neely
openaire   +2 more sources

Ship Target Identification via Bayesian-Transformer Neural Network

Journal of Marine Science and Engineering, 2022
Ship target identification is of great significance in both military and civilian fields. Many methods have been proposed to identify the targets using tracks information. However, most of existing studies can only identify two or three types of targets,
Zhan Kong, Yaqi Cui, Wei Xiong, Fucheng Yang, Zhenyu Xiong, Pingliang Xu   +5 more
doaj   +1 more source

Function Space Bayesian Pseudocoreset for Bayesian Neural Networks

Advances in Neural Information Processing Systems 36, 2023
A Bayesian pseudocoreset is a compact synthetic dataset summarizing essential information of a large-scale dataset and thus can be used as a proxy dataset for scalable Bayesian inference. Typically, a Bayesian pseudocoreset is constructed by minimizing a divergence measure between the posterior conditioning on the pseudocoreset and the posterior ...
Balhae Kim, Hyungi Lee, Juho Lee 0001
openaire   +3 more sources

Simple Direct Uncertainty Quantification Technique Based on Machine Learning Regression

Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022
Epistemic uncertainty quantification provides useful insight into both deep and shallow neural networks' understanding of the relationships between their training distributions and unseen instances and can serve as an estimate of classification ...
Katherine E. Brown, Douglas A. Talbert
doaj   +1 more source

Bayesian Recurrent Neural Networks

CoRR, 2017
In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks. Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by
Meire Fortunato, Charles Blundell, Oriol Vinyals   +2 more
openaire   +2 more sources

Bayesian Neural Networks: Essentials

CoRR, 2021
Bayesian neural networks utilize probabilistic layers that capture uncertainty over weights and activations, and are trained using Bayesian inference. Since these probabilistic layers are designed to be drop-in replacement of their deterministic counter parts, Bayesian neural networks provide a direct and natural way to extend conventional deep neural ...
openaire   +2 more sources