This paper reviews the current state of the art in artificial intelligence (AI) technologies and
applications in the context of the creative industries. A brief background of AI, and …

Recently, generative adversarial networks (GANs) have progressed enormously, which
makes them able to learn complex data distributions in particular faces. More and more …

In this work, we connect two distinct concepts for unsupervised domain adaptation: feature
distribution alignment between domains by utilizing the task-specific decision boundary and …

Optimal transport (OT) theory can be informally described using the words of the French
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …

High-throughput single-cell profiling provides an unprecedented ability to uncover the
molecular states of millions of cells. These technologies are, however, destructive to cells …

Comparing probability distributions is a fundamental problem in data sciences. Simple
norms and divergences such as the total variation and the relative entropy only compare …

Optimal transport has recently been reintroduced to the machine learning community thanks
in part to novel efficient optimization procedures allowing for medium to large scale …

Velocity-model building is a key step in hydrocarbon exploration. The main product of
velocity-model building is an initial model of the subsurface that is subsequently used in …

We propose in this paper a differentiable learning loss between time series, building upon
the celebrated dynamic time warping (DTW) discrepancy. Unlike the Euclidean distance …

The ability to compare two degenerate probability distributions, that is two distributions
supported on low-dimensional manifolds in much higher-dimensional spaces, is a crucial …