Sequential recommendation models the dynamics of a user's previous behaviors in order to
forecast the next item, and has drawn a lot of attention. Transformer-based approaches …

Unsupervised domain adaptation (UDA) enables the transfer of models trained on source
domains to unlabeled target domains. However, transferring complex time series models …

The Wasserstein distance and its variations, eg, the sliced-Wasserstein (SW) distance, have
recently drawn attention from the machine learning community. The SW distance …

The reemergence of Deep Neural Networks (DNNs) has lead to high-performance
supervised learning algorithms for the Electro-Optical (EO) domain classification and …

Sliced Wasserstein distances preserve properties of classic Wasserstein distances while
being more scalable for computation and estimation in high dimensions. The goal of this …

Optimal transport (OT) methods seek a transformation map (or plan) between two probability
measures, such that the transformation has the minimum transportation cost. Such a …

Crucial for healthcare and biomedical applications, respiration monitoring often employs
wearable sensors in practice, causing inconvenience due to their direct contact with human …

Existing methods for unsupervised domain adaptation often rely on minimizing some
statistical distance between the source and target samples in the latent space. To avoid the …

Despite significant advancements of deep learning-based forgery detectors for
distinguishing manipulated deepfake images, most detection approaches suffer from …

Deep neural networks have been successfully utilized in the mechanical fault diagnosis,
however, a large number of them have been based on the same assumption that training …