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[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
A review of generalized zero-shot learning methods
Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples
under the condition that some output classes are unknown during supervised learning. To …
under the condition that some output classes are unknown during supervised learning. To …
The internet of medical things and artificial intelligence: trends, challenges, and opportunities
High quality and efficient medical service is one of the major factors defining living
standards. Developed countries strive to make their healthcare systems as efficient and cost …
standards. Developed countries strive to make their healthcare systems as efficient and cost …
Prevalence and early prediction of diabetes using machine learning in North Kashmir: a case study of district bandipora
Diabetes is one of the biggest health problems that affect millions of people across the
world. Uncontrolled diabetes can increase the risk of heart attack, cancer, kidney damage …
world. Uncontrolled diabetes can increase the risk of heart attack, cancer, kidney damage …
Joint coding-modulation for digital semantic communications via variational autoencoder
Semantic communications have emerged as a new paradigm for improving communication
efficiency by transmitting the semantic information of a source message that is most relevant …
efficiency by transmitting the semantic information of a source message that is most relevant …
[HTML][HTML] A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction
Predicting electricity demand data is considered an essential task in decisions taking, and
establishing new infrastructure in the power generation network. To deliver a high-quality …
establishing new infrastructure in the power generation network. To deliver a high-quality …
Alzheimer's patient analysis using image and gene expression data and explainable-AI to present associated genes
There are more than 10 million new cases of Alzheimer's patients worldwide each year,
which means there is a new case every 3.2 s. Alzheimer's disease (AD) is a progressive …
which means there is a new case every 3.2 s. Alzheimer's disease (AD) is a progressive …
Flexconv: Continuous kernel convolutions with differentiable kernel sizes
When designing Convolutional Neural Networks (CNNs), one must select the size\break of
the convolutional kernels before training. Recent works show CNNs benefit from different …
the convolutional kernels before training. Recent works show CNNs benefit from different …
Wavemix: A resource-efficient neural network for image analysis
We propose a novel neural architecture for computer vision--WaveMix--that is resource-
efficient and yet generalizable and scalable. While using fewer trainable parameters, GPU …
efficient and yet generalizable and scalable. While using fewer trainable parameters, GPU …
A comparative study of cosmological constraints from weak lensing using Convolutional Neural Networks
Weak Lensing (WL) surveys are reaching unprecedented depths, enabling the investigation
of very small angular scales. At these scales, nonlinear gravitational effects lead to higher …
of very small angular scales. At these scales, nonlinear gravitational effects lead to higher …