Results 31 to 40 of about 129,225 (287)
Neural network channel estimator for time‐variant frequency‐selective fading channels
The next generations of wireless communications systems are pushing the limits of the channel estimation methods utilized in the orthogonal frequency division multiplexing receptors.
Vinicius Piro Barragam +2 more
doaj +1 more source
Evaluating Machine Learning Models for Predicting Hardness of AlCoCrCuFeNi High-Entropy Alloys
This study evaluates the predictive capabilities of various machine learning (ML) algorithms for estimating the hardness of AlCoCrCuFeNi high-entropy alloys (HEAs) based on their compositional variables.
Uma Maheshwera Reddy Paturi +5 more
doaj +1 more source
We show how a declarative functional programming specification of backpropagation yields a visual and transparent implementation within spreadsheets. We call our method Visual Backpropagation. This backpropagation implementation exploits array worksheet formulas, manual calculation, and has a sequential order of computation similar to the processing of
openaire +2 more sources
Heterogeneity of neural properties within a given neural class is ubiquitous in the nervous system and permits different sub-classes of neurons to specialize for specific purposes.
Sree I. Motipally +3 more
doaj +1 more source
Cancer is one of the deadliest diseases in the world that needs to be handled as early as possible. One of the methods to detect the presence of cancer cells early on is by using microarray data. Microarray data can store human gene expression and use it
Muhammad Naufal Mukhbit Amrullah +2 more
doaj +1 more source
In this presented communication, a novel design of intelligent Bayesian regularization backpropagation networks (IBRBNs) based on stochastic numerical computing is presented.
Tariq Mahmood +5 more
doaj +1 more source
Backpropagation training in adaptive quantum networks
We introduce a robust, error-tolerant adaptive training algorithm for generalized learning paradigms in high-dimensional superposed quantum networks, or \emph{adaptive quantum networks}.
A. Ferreira +17 more
core +1 more source
Learning in Feedforward Neural Networks Accelerated by Transfer Entropy
Current neural networks architectures are many times harder to train because of the increasing size and complexity of the used datasets. Our objective is to design more efficient training algorithms utilizing causal relationships inferred from neural ...
Adrian Moldovan +2 more
doaj +1 more source
Equivalence of Equilibrium Propagation and Recurrent Backpropagation
Recurrent Backpropagation and Equilibrium Propagation are supervised learning algorithms for fixed point recurrent neural networks which differ in their second phase.
Bengio, Yoshua, Scellier, Benjamin
core +1 more source
The human brain is arguably the most complex “machine” to ever exist. Its detailed functioning is yet to be fully understood, let alone modelled. Neurological processes have logical signal-processing and biophysical aspects, and both affect the brain’s ...
Luis Irastorza-Valera +3 more
doaj +1 more source

