Results 21 to 30 of about 3,905,952 (289)

Interpreting the neural code with formal concept analysis [PDF]

, 2012
This contribution is in volume 1 of 3 for this title.We propose a novel application of Formal Concept Analysis (FCA) to neural decoding: instead of just trying to figure out which stimulus was presented, we demonstrate how to explore the semantic ...
Endres, D, Foldiak, Peter
core   +2 more sources

Computational inference of neural information flow networks [PDF]

, 2006
This research was supported by a Packard Foundation grant and a US National Science Foundation (NSF) Waterman Award to EDJ, an NSF CAREER grant and an Alfred P.
Tom V. Smulders, Hartemink Alexander J, Hartemink, Alexander J, V. Anne Smith, Smith, Victoria Anne, Alexander J Hartemink, Yu Jing, Hartemink, Alex J, Erich D. Jarvis, Tom V Smulders, Smith V. Anne, Alexander J. Hartemink, Jing Yu, Smulders Tom V, Yu, Jing, Smulders, Tom V, V Anne Smith, Smith, V Anne, Erich D Jarvis, Jarvis, Erich D, Jarvis Erich D   +20 more
core   +2 more sources

Bayesian Neural Networks for Reversible Steganography

IEEE Access, 2022
Recent advances in deep learning have led to a paradigm shift in the field of reversible steganography. A fundamental pillar of reversible steganography is predictive modelling which can be realised via deep neural networks.
Ching-Chun Chang
doaj   +1 more source

Comparative Study of Various Neural Network Types for Direct Inverse Material Parameter Identification in Numerical Simulations

Applied Sciences, 2022
Increasing product requirements in the mechanical engineering industry and efforts to reduce time-to-market demand highly accurate and resource-efficient finite element simulations.
Paul Meißner, Tom Hoppe, Thomas Vietor
doaj   +1 more source

Reasoning over Bayesian Networks using Semantic Artificial Neural Networks

, 2021
Representation of application domains, related concepts and their dependencies is often achieved using Bayesian Networks. In Bayesian Networks nodes represent random variables and arcs represent their dependencies.
Batsakis, Sotirios, Sotirios Batsakis, Antoniou, G, Antoniou, Grigoris, Grigoris Antoniou, Batsakis, S   +5 more
core   +1 more source

On Sequential Bayesian Inference for Continual Learning

Entropy, 2023
Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks.
Samuel Kessler, Adam Cobb, Tim G. J. Rudner, Stefan Zohren, Stephen J. Roberts   +4 more
doaj   +1 more source

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   +4 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
Danil Kuzin, Olga Isupova, Lyudmila Mihaylova   +2 more
openaire   +2 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 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