In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …

Computational chemistry is an indispensable tool for understanding molecules and
predicting chemical properties. However, traditional computational methods face significant …

Background: With the development of smart grids, accurate electric load forecasting has
become increasingly important as it can help power companies in better load scheduling …

Abstract The classical Universal Approximation Theorem holds for neural networks of
arbitrary width and bounded depth. Here we consider the natural 'dual'scenario for networks …

The application of deep learning approaches to finance has received a great deal of
attention from both investors and researchers. This study presents a novel deep learning …

A generative adversarial network (GAN) usually contains a generative network and a
discriminative network in competition with each other. The GAN has shown its capability in a …

Due to the advantages of deep learning, in this paper, a regularized deep feature extraction
(FE) method is presented for hyperspectral image (HSI) classification using a convolutional …

Kwang Gi Kim https://doi. org/10.4258/hir. 2016.22. 4.351 ing those who are beginning their
careers in deep learning and artificial intelligence research. The other target audience …

Classification is one of the most popular topics in hyperspectral remote sensing. In the last
two decades, a huge number of methods were proposed to deal with the hyperspectral data …

Hyperspectral data classification is a hot topic in remote sensing community. In recent years,
significant effort has been focused on this issue. However, most of the methods extract the …