In recent years, there has been an exponential growth in the number of complex documents
and texts that require a deeper understanding of machine learning methods to be able to …

Rapid developments in hardware, software, and communication technologies have
facilitated the emergence of Internet-connected sensory devices that provide observations …

Cross-entropy is a widely used loss function in applications. It coincides with the logistic loss
applied to the outputs of a neural network, when the softmax is used. But, what guarantees …

Recently great strides have been made in the field of automatic speech recognition (ASR) by
using various deep learning techniques. In this study, we present a thorough comparison …

“Any AI smart enough to pass a Turing test is smart enough to know to fail it.”–*** Ian
McDonald Neural networks were developed to simulate the human nervous system for …

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 …

In many applications of classifier learning, training data suffers from label noise. Deep
networks are learned using huge training data where the problem of noisy labels is …

Principles of typical motion planning algorithms are investigated and analyzed in this paper.
These algorithms include traditional planning algorithms, classical machine learning …

Recent works have demonstrated that deep learning algorithms were efficient to conduct
security evaluations of embedded systems and had many advantages compared to the other …

Increasingly large document collections require improved information processing methods
for searching, retrieving, and organizing text. Central to these information processing …