Despite not being designed for this purpose, the use of variational autoencoders (VAEs) has
proven remarkably effective for disentangled representation learning (DRL). Recent …

This thesis is interested in unsupervised statistical learning methods and their applications
to health monitoring of aircraft engines at an industrial scale. Our first objective is to make …

Abstract Like Gaussian distribution, Laplacian distribution is also a location-scale family of
distributions. So we can choose the standard Laplace distribution (with location= 0, scale …

Unsupervised representation learning is an important task in machine learning that identifies
and models underlying explanatory factors hidden in the observed data. In recent years …

For medical applications, trustworthiness, interpretability, and robustness are necessary
properties of (deep) neural networks. For generative models, one approach towards this …

Nowadays, high-dimensional models, where the number of parameters or features can even
be larger than the number of observations are encountered on a fairly regular basis due to …

Representation learning, the task of extracting meaningful representations of high-
dimensional data, lies at the very core of artificial intelligence research. Be it via implicit …

ABSTRACT β-VAE is a follow-up technique to variational autoencoders that proposes
special weighting of the KL divergence term in the VAE loss to obtain disentangled …

In representation learning, a common approach is to seek representations which
disentangle the underlying factors of variation. Eastwood & Williams (2018) proposed three …

ABSTRACT Harmful Algal Blooms (HAB) are typically described as blooms of phytoplankton
species that can not only cause harm to the environment but also humans. Some species …