Deep neural networks demonstrated their ability to provide remarkable performances on a
wide range of supervised learning tasks (eg, image classification) when trained on extensive …

Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled
data when labels are limited or expensive to obtain. SSL algorithms based on deep neural …

The present paper studies so-called deep image prior (DIP) techniques in the context of ill-
posed inverse problems. DIP networks have been recently introduced for applications in …

We show that k-means (Lloyd's algorithm) is obtained as a special case when truncated
variational EM approximations are applied to Gaussian mixture models (GMM) with isotropic …

In this research, 56 samples of pure honey have been mixed with different concentrations of
rice syrup simulating a set of adulterated samples. A thermographic camera was used to …

In this work, a thermographic camera and intelligent algorithms have been used to classify
five different types of rice (Oryza sativa L.) in grain or flour format and to detect mixtures of …

How can we efficiently find very large numbers of clusters C in very large datasets N of
potentially high dimensionality D? Here we address the question by using a novel …

We combine two popular optimization approaches to derive learning algorithms for
generative models: variational optimization and evolutionary algorithms. The combination is …

The unloading of containers is a tedious task that a decreasing number of workers is willing
to take on.(Semi-) autonomous systems are already available but limited to clearly defined …

Latent Variable Models (LVMs) are well established tools to accomplish a range of different
data processing tasks. Applications exploit the ability of LVMs to identify latent data structure …