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IEEE Access, 2022 Dropout is adopted in many state-of-the-art Deep Neural Networks (DNNs) to ease the overfitting problem by randomly removing features from feature maps.
Khanh-Binh Nguyen +2 more
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Brain serotonergic fibers suggest anomalous diffusion-based dropout in artificial neural networks
Frontiers in Neuroscience, 2022 Random dropout has become a standard regularization technique in artificial neural networks (ANNs), but it is currently unknown whether an analogous mechanism exists in biological neural networks (BioNNs).
Christian Lee +2 more
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Mixed-pooling-dropout for convolutional neural network regularization
Journal of King Saud University: Computer and Information Sciences, 2022 Deep neural networks are the most used machine learning systems in the literature, for they are able to train huge amounts of data with a large number of parameters in a very effective way.
Brahim Ait Skourt +2 more
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Advanced Science, 2020 Neural networks based on memristive devices have achieved great progress recently. However, memristive synapses with nonlinearity and asymmetry seriously limit the classification accuracy.
He‐Ming Huang +6 more
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A large scale statistical analysis of quantum and classical neural networks in the medical domain [PDF]
Scientific ReportsClassical neural networks (NNs) have shown strong performance in medical data analysis. However, they typically require large labeled datasets and may struggle in data-scarce scenarios, common in clinical practice.
Francesco Ghisoni +2 more
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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
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Neural Network Prediction for Ice Shapes on Airfoils Using iceFoam Simulations
Aerospace, 2022 In this article the procedure and method for the ice accretion prediction for different airfoils using artificial neural networks (ANNs) are discussed. A dataset for the neural network is based on the numerical experiment results—obtained through iceFoam
Sergei Strijhak +3 more
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Predictive Model for Taking Decision to Prevent University Dropout.
International Journal of Interactive Multimedia and Artificial Intelligence, 2022 Dropout is an educational phenomenon studied for decades due to the diversity of its causes, whose effects fall on society's development. This document presents an experimental study to obtain a predictive model that allows anticipating a university ...
Argelia Berenice Urbina Nájera +1 more
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A General Approach to Dropout in Quantum Neural Networks
Advanced Quantum Technologies, 2023 Abstract In classical machine learning (ML), “overfitting” is the phenomenon occurring when a given model learns the training data excessively well, and it thus performs poorly on unseen data. A commonly employed technique in ML is the so called “dropout,” which prevents computational units from becoming too specialized, hence ...
Scala, Francesco +3 more
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Universal Approximation in Dropout Neural Networks
J. Mach. Learn. Res., 2020 We prove two universal approximation theorems for a range of dropout neural networks. These are feed-forward neural networks in which each edge is given a random $\{0,1\}$-valued filter, that have two modes of operation: in the first each edge output is multiplied by its random filter, resulting in a random output, while in the second each edge output ...
Manita, Oxana A. +4 more
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