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 …

We present Bit Diffusion: a simple and generic approach for generating discrete data with
continuous state and continuous time diffusion models. The main idea behind our approach …

Multivariate time series usually contain a large number of missing values, which hinders the
application of advanced analysis methods on multivariate time series data. Conventional …

Although Generative Adversarial Networks (GANs) have shown remarkable success in
various tasks, they still face challenges in generating high quality images. In this paper, we …

Abstract Generative Adversarial Networks (GANs) are powerful generative models, but suffer
from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress …

We introduce Texygen, a benchmarking platform to support research on open-domain text
generation models. Texygen has not only implemented a majority of text generation models …

In recent years some researchers have explored the use of reinforcement learning (RL)
algorithms as key components in the solution of various natural language processing (NLP) …

This paper focuses on style transfer on the basis of non-parallel text. This is an instance of a
broad family of problems including machine translation, decipherment, and sentiment …

Deep learning methods possess many processing layers to understand the stratified
representation of data and have achieved state-of-art results in several domains. Recently …