Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

The Generative Models have gained considerable attention in unsupervised learning via a
new and practical framework called Generative Adversarial Networks (GAN) due to their …

Abstract Generative Adversarial Networks (GANs) have been extremely successful in
various application domains such as computer vision, medicine, and natural language …

Generative Adversarial Networks (GANs) have received wide attention in the machine
learning field for their potential to learn high-dimensional, complex real data distribution …

Binary classifiers are employed as discriminators in GAN-based unsupervised style transfer
models to ensure that transferred sentences are similar to sentences in the target domain …

Abstractive summarization has been studied using neural sequence transduction methods
with datasets of large, paired document-summary examples. However, such datasets are …

This paper has four main contributions. First, we calculate the predicted difference
distribution of Speck32/64 with one specific input difference under the Markov assumption …

Generative adversarial networks (GANs) have achieved significant success in generating
real-valued data. However, the discrete nature of text hinders the application of GAN to text …

Introduction to Machine Learning with Applications in Information Security, Second Edition
provides a classroom-tested introduction to a wide variety of machine learning and deep …

In this work, we address the problem of musical timbre transfer, where the goal is to
manipulate the timbre of a sound sample from one instrument to match another instrument …