This paper presents a novel deep learning framework for human trajectory prediction and
detecting social group membership in crowds. We introduce a generative adversarial …

Anomaly detection suffers from unbalanced data since anomalies are quite rare.
Synthetically generated anomalies are a solution to such ill or not fully defined data …

We address the problem of pulse shape discrimination (PSD) for radiation sources
characterization by leveraging a Gaussian mixture variational autoencoder (GMVAE). When …

While insects are the largest and most diverse group of terrestrial animals, constituting ca.
80% of all known species, they are difficult to study due to their small size and similarity …

We propose a novel approach based on variational autoencoder with Gaussian mixture
latent space (GMVAE) to address the challenging problem of pulse shape discrimination …

In this work, we propose a semi-supervised approach based on generative models to learn
both feature representations and categories in an end-to-end manner. The learning process …

In this paper, we proposed a model to learn both clusters and representations of our data in
an end-to-end manner. Our model is a modification of the stacked generative model M1+ M2 …

This paper addresses the complex problem of learning from unbalanced datasets due to
which traditional algorithms may perform poorly. Classification algorithms used for learning …

Unsupervised representation learning tends to produce generic and reusable latent
representations. However, these representations can often miss high-level features or …

With the rapid growth and increasing of performance of mobile devices such as mobile
phones and tablets, and the development of mobile internet such as 5G, the mobile game …