… MIXTURE OF NETWORKS Inspired by the mixture models, we propose a combined generative
and clustering algorithm. The learning process is completely unsupervised. Components …

… Generative Adversarial Networks have a surprising ability to … In this paper, we propose a
new GAN variant called Mixture … , using the resulting Gaussian mixture as a likelihood function …

… propose a generative model that is a mixture of k generative artificial neural networks (ANNs…
each cluster is given to a separate generative neural network, such as Restricted Boltzmann …

This paper describes bidirectional recurrent mixture density net (cid: 173) works, which can
model multi-modal distributions of the type P (Xt Iyf) and P (Xt lXI, X2,..., Xt-l, yf) without any …

… that combines generative adversarial networks (GAN) [27] and mixture density networks (MDN)
[… Then, we can utilize MDN, a neural network attempting to learn one-to-many nonlinear …

… To overcome the above challenges, we propose a framework that combines generative
adversarial networks (GAN) [5] and mixture density networks (MDN) [6] for inverse modeling. …

… Generative Adversarial Networks include a family of methods for learning generative models
… In this work, we propose to use a mixture of Gaussians as a multi-modal prior distribution. …

… In this work, we address these problems by developing a novel mixture of … network architecture
design for the VAE mixture model. Unlike in other mixture models using deep networks for …

… In [20], another unsupervised generative model (known … generative model that combines
the advantages of both VaDE and VAE-GAN. In this work, we propose a novel deep generative …

… the performance of generative networks given two sets of … the featurized images using
Gaussian mixture models (GMMs) and … more accurately evaluate generative network performance. …