Results 11 to 20 of about 117,260 (178)
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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Universal Approximation in Dropout Neural Networks
Journal of Machine Learning Research, 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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Regularization of deep neural networks with spectral dropout [PDF]
Neural Networks, 2019 The big breakthrough on the ImageNet challenge in 2012 was partially due to the `dropout' technique used to avoid overfitting. Here, we introduce a new approach called `Spectral Dropout' to improve the generalization ability of deep neural networks. We cast the proposed approach in the form of regular Convolutional Neural Network (CNN) weight layers ...
Khan, Salman +2 more
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MOOC Dropout Prediction Using FWTS-CNN Model Based on Fused Feature Weighting and Time Series
IEEE Access, 2020 High dropout rates have been a major problem affecting the development of Massive Open Online Courses (MOOCs). Student dropout prediction can help teachers identify students who are tending to fail and provide extra help in a timely manner, helping to ...
Yafeng Zheng +3 more
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Augmenting Recurrent Neural Networks Resilience by Dropout
IEEE Transactions on Neural Networks and Learning Systems, 2020 This brief discusses the simple idea that dropout regularization can be used to efficiently induce resiliency to missing inputs at prediction time in a generic neural network. We show how the approach can be effective on tasks where imputation strategies often fail, namely, involving recurrent neural networks and scenarios where whole sequences of ...
Bacciu, Davide, CRECCHI, FRANCESCO
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Robustness of Sparsely Distributed Representations to Adversarial Attacks in Deep Neural Networks
Entropy, 2023 Deep learning models have achieved an impressive performance in a variety of tasks, but they often suffer from overfitting and are vulnerable to adversarial attacks.
Nida Sardar +3 more
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Uncertainty propagation for dropout-based Bayesian neural networks
Neural Networks, 2021 Uncertainty evaluation is a core technique when deep neural networks (DNNs) are used in real-world problems. In practical applications, we often encounter unexpected samples that have not seen in the training process. Not only achieving the high-prediction accuracy but also detecting uncertain data is significant for safety-critical systems.
Yuki Mae +2 more
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A primer on deep learning and convolutional neural networks for clinicians
Insights into Imaging, 2021 Deep learning is nowadays at the forefront of artificial intelligence. More precisely, the use of convolutional neural networks has drastically improved the learning capabilities of computer vision applications, being able to directly consider raw data ...
Lara Lloret Iglesias +7 more
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A General Approach to Dropout in Quantum Neural Networks
Advanced Quantum Technologies, 2023 AbstractIn 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 reducing the risk of ...
Scala, Francesco +3 more
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A CONCRETE DROPOUT NEURAL NETWORK FOR SHEAR SONIC LOG PREDICTION [PDF]
Romanian Journal of Petroleum & Gas TechnologyAssessing the risk associated with drilling and wellbore stability studies requires the shear sonic log. These logs apart from distinguishing formation fluid from lithology are needed to obtain geo-mechanical rock parameters required for the safe design ...
Ephraim Ojoajogu Enemali +3 more
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