An autoencoder is a specific type of a neural network, which is mainly designed to encode
the input into a compressed and meaningful representation and then decode it back such …

Deep learning techniques, in particular generative models, have taken on great importance
in medical image analysis. This paper surveys fundamental deep learning concepts related …

Disentangled representations, where the higher level data generative factors are reflected in
disjoint latent dimensions, offer several benefits such as ease of deriving invariant …

The success of existing multi-view clustering relies on the assumption of sample integrity
across multiple views. However, in real-world scenarios, samples of multi-view are partially …

We introduce a deep appearance model for rendering the human face. Inspired by Active
Appearance Models, we develop a data-driven rendering pipeline that learns a joint …

In recent years, deep learning (DL) has attracted increasing attention in hyperspectral
unmixing (HU) applications due to its powerful learning and data fitting ability. The …

In this work, we propose a machine learning approach to generate novel molecules starting
from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features …

While anomaly detection is relatively well-studied, it remains a topic of ongoing interest and
challenge, as our society becomes increasingly interconnected and digitalized. In this paper …

Artificial intelligence, deep convolutional neural networks, and deep learning are all niche
terms that are increasingly appearing in scientific presentations as well as in the general …

A longstanding problem in machine learning is to find unsupervised methods that can learn
the statistical structure of high dimensional signals. In recent years, GANs have gained much …