The aquaculture sector in New Zealand is experiencing rapid expansion, w...
Extracorporeal membrane oxygenation (ECMO) is an essential life-supporti...
Loss function learning is a new meta-learning paradigm that aims to auto...
Convolutional neural networks (CNNs) have constantly achieved better
per...
Deep convolutional neural networks have proven their effectiveness, and ...
Major postoperative complications are devastating to surgical patients. ...
In this paper, we develop upon the emerging topic of loss function learn...
Over recent years, there has been a rapid development of deep learning (...
Manifold learning methods are an invaluable tool in today's world of
inc...
Neural architecture search (NAS), which automatically designs the
archit...
Using evolutionary computation algorithms to solve multiple tasks with
k...
Character-level convolutional neural networks (char-CNN) require no know...
Deep Neural Networks (DNNs) have achieved great success in many applicat...
Hyperspectral images (HSIs) are susceptible to various noise factors lea...
Deep convolutional neural networks have demonstrated promising performan...
A common claim of evolutionary computation methods is that they can achi...
Data visualisation is a key tool in data mining for understanding big
da...
A common problem machine learning developers are faced with is overfitti...
Manifold learning techniques have become increasingly valuable as data
c...
Graph matching plays a central role in such fields as computer vision,
p...
Clustering is a difficult and widely-studied data mining task, with many...
Image classification is an essential task in computer vision, which aims...
De novo peptide sequencing algorithms have been widely used in proteomic...
Deep Convolutional Neural Networks (CNNs) have been widely used in image...
In recent years, convolutional neural networks (CNNs) have become deeper...
Image classification is a difficult machine learning task, where
Convolu...
Exploratory data analysis is a fundamental aspect of knowledge discovery...
Proteomics is the large-scale analysis of the proteins. The common metho...
The performance of Convolutional Neural Networks (CNNs) highly relies on...
Convolutional Neural Networks (CNNs) have demonstrated their superiority...
Convolutional Neural Networks (CNNs) have demonstrated their superiority...
Convolutional Neural Networks (CNNs) have gained a remarkable success on...
Convolutional neural networks (CNNs) are one of the most effective deep
...
Recently, feature selection has become an increasingly important area of...
Convolutional auto-encoders have shown their remarkable performance in
s...
Evolutionary computation methods have been successfully applied to neura...