… Probabilistic PCA adds a probabilistic structure by learning … Autoencoders (AE) minimize the
reconstruction error in a … Here, we introduce the Probabilistic Autoencoder (PAE) that learns …

… , probabilistic Principal Component Analysis (PCA), variational inference, and Variational
Autoencoder (… Probabilistic PCA is then explained, as a special case of factor analysis, and its …

… is a probabilistic autoencoder that consists of an encoder, a decoder, and a discriminator
network [25]. The probabilistic … This approach transforms a basic autoencoder into a generative …

… probabilistic autoencoder is constructed in two separate stages. First, we train a conditional
autoencoder … After the autoencoder is trained we construct a normalizing flow to map from the …

… This paper introduces a methodology for assessing bridge conditions using a probabilistic
temporal autoencoder (PTAE). The proposed approach effectively extracts features and …

… of the bad data, such as variational autoencoder [4] and generative … Therefore, it is essential
to propose new probabilistic … approach with a probabilistic deep autoencoder (PDAE) is …

… kind, we take a probabilistic approach and effectively compute … Second, we improve the
training of the autoencoder network. An … we learn an adversarial autoencoder network with two …

… The variational autoencoder (VAE) imposes a probabilistic distribution (typically Gaussian) …
In this paper, we propose to use the Wasserstein autoencoder (WAE) for probabilistic sen…

… In this paper, a novel framework for probabilistic wind speed forecasting (PWSF) from the
generative perspective based on variational recurrent autoencoders (VRAEs) was proposed. …

… a novel probabilistic model called latent adversarial regularized autoencoder, abbreviated
as … capability of autoencoders in extracting higher-level non-linear features. Through flexible …