… In the similar perspective of deep neural networks, we define a deep Gaussian mixture model
(DGMM) … At each layer, the variables follow a mixture of Gaussian distributions. Thus, the …

… this paper we propose a new scalable deep generative model for images, called the Deep
… the deep Gaussian Mixture Model: a novel density estimation technique for modeling real …

… Deep Gaussian Mixture … as mixtures of Gaussians. We design a neural network that extracts
pose-invariant correspondences between raw point clouds and Gaussian Mixture Model (…

… architecture model-based clustering method called Mixed Deep Gaussian Mixture Model that
… Generalized Linear Latent Variable Models and Deep Gaussian Mixture Models. We also …

… uses Gaussian mixture models for protein structure representation and deep neural networks
for conformation space embedding. By integrating information from molecular models into …

… deep Gaussian mixture model (DGMM) is a framework directly inspired by the finite mixture
of factor analysers model (MFA) and the deep … The MFA is a generative model that considers …

… Gaussian mixture model (GMM) with unsupervised deep learning techniques. In the standard
GMM the pixel values with each sub-region are modelled by a Gaussian … to the deep image …

… a Gaussian mixture latent space. We describe learning and inference for not only the traditional
mixture model but also Dirichlet Process mixtures … with deep gaussian mixture models. In …

… Deep Autoencoding Gaussian Mixture Model (DAGMM) for unsupervised anomaly detection.
Our model utilizes a deep … is further fed into a Gaussian Mixture Model (GMM). Instead of …

… Traditionally, deep regression employ the L2 loss function, known to be sensitive … deep
regression model that is robust to outliers thanks to the use of a Gaussian-uniform mixture model. …