Results 81 to 90 of about 117,260 (178)
Survey of Dropout Methods for Deep Neural Networks
, 2019 Dropout methods are a family of stochastic techniques used in neural network training or inference that have generated significant research interest and are widely used in practice. They have been successfully applied in neural network regularization, model compression, and in measuring the uncertainty of neural network outputs.
Labach, Alex +2 more
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A Hybrid of Deep CNN and Bidirectional LSTM for Automatic Speech Recognition
Journal of Intelligent Systems, 2019 Deep neural networks (DNNs) have been playing a significant role in acoustic modeling. Convolutional neural networks (CNNs) are the advanced version of DNNs that achieve 4–12% relative gain in the word error rate (WER) over DNNs.
Passricha Vishal, Aggarwal Rajesh Kumar
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ComputationAcademic dropout is a persistent challenge in higher education, particularly in contexts with socio-economic disparities and diverse learning conditions.
Juan-Carlos Briñez-De-León +3 more
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IEEE AccessDiabetic Retinopathy (DR) is a severe condition that affects diabetic patients, potentially leading to irreversible vision loss if not addressed in its early stage.
M. Rizvana, Sathiya Narayanan
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Optical Phase Dropout in Diffractive Deep Neural Network
, 2020 Unitary learning is a backpropagation that serves to unitary weights update in deep complex-valued neural network with full connections, meeting a physical unitary prior in diffractive deep neural network ([DN]2). However, the square matrix property of unitary weights induces that the function signal has a limited dimension that could not generalize ...
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Probabilistic Bayesian Neural Networks for olive phenology prediction in precision agriculture
Ecological InformaticsPlant phenology is the study of cyclical events in a plant life cycle such as leaf bud burst, flowering, and fruiting. In this article the problem of olive phenology prediction is addressed through the use of Deep Learning.
A. Nappa +6 more
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School Dropout Screening through Artificial Neural Networks based Systems
, 2014 School dropout is one of the major concerns of our society. Indeed, it is a complex phenomenon, resulting in economic and social losses, either to the individual, family or the community to which the person belongs. Academic difficulty and failure, poor attendance, retention, disengagement from school together with family and socio-economic reasons can
Figueiredo, Margarida +3 more
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Critical evaluation of the theory and practice of feed-forward neural networks for genomic prediction. [PDF]
G3 (Bethesda)Kusmec A, Negus KL, Yu J.
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Interpreting artificial neural networks to detect genome-wide association signals for complex traits. [PDF]
NAR Genom BioinformYelmen B +5 more
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UncerTrans: uncertainty-aware temporal transformer for early action prediction. [PDF]
Sci RepZhai X, Liu Y.
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