Machine learning approaches to drug response prediction: challenges and recent progress
Cancer is a leading cause of death worldwide. Identifying the best treatment using
computational models to personalize drug response prediction holds great promise to …
computational models to personalize drug response prediction holds great promise to …
A survey of handwritten character recognition with mnist and emnist
This paper summarizes the top state-of-the-art contributions reported on the MNIST dataset
for handwritten digit recognition. This dataset has been extensively used to validate novel …
for handwritten digit recognition. This dataset has been extensively used to validate novel …
Deep learning with convolutional neural networks for EEG decoding and visualization
RT Schirrmeister, JT Springenberg… - Human brain …, 2017 - Wiley Online Library
Deep learning with convolutional neural networks (deep ConvNets) has revolutionized
computer vision through end‐to‐end learning, that is, learning from the raw data. There is …
computer vision through end‐to‐end learning, that is, learning from the raw data. There is …
A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets
VH Phung, EJ Rhee - Applied Sciences, 2019 - mdpi.com
Research on clouds has an enormous influence on sky sciences and related applications,
and cloud classification plays an essential role in it. Much research has been conducted …
and cloud classification plays an essential role in it. Much research has been conducted …
Tree-CNN: a hierarchical deep convolutional neural network for incremental learning
Over the past decade, Deep Convolutional Neural Networks (DCNNs) have shown
remarkable performance in most computer vision tasks. These tasks traditionally use a fixed …
remarkable performance in most computer vision tasks. These tasks traditionally use a fixed …
Ternary neural networks for resource-efficient AI applications
The computation and storage requirements for Deep Neural Networks (DNNs) are usually
high. This issue limits their deployability on ubiquitous computing devices such as smart …
high. This issue limits their deployability on ubiquitous computing devices such as smart …
Evolutionary convolutional neural networks: An application to handwriting recognition
Convolutional neural networks (CNNs) have been used over the past years to solve many
different artificial intelligence (AI) problems, providing significant advances in some domains …
different artificial intelligence (AI) problems, providing significant advances in some domains …
Ensembles of deep learning models and transfer learning for ear recognition
The recognition performance of visual recognition systems is highly dependent on extracting
and representing the discriminative characteristics of image data. Convolutional neural …
and representing the discriminative characteristics of image data. Convolutional neural …
Deep convolutional neural networks for unconstrained ear recognition
This paper employs state-of-the-art Deep Convolutional Neural Networks (CNNs), namely
AlexNet, VGGNet, Inception, ResNet and ResNeXt in a first experimental study of ear …
AlexNet, VGGNet, Inception, ResNet and ResNeXt in a first experimental study of ear …
[PDF][PDF] Transfer learning and fine tuning in breast mammogram abnormalities classification on CBIS-DDSM database
Breast cancer has an important incidence in women mortality worldwide. Currently,
mammography is considered the gold standard for breast abnormalities screening …
mammography is considered the gold standard for breast abnormalities screening …