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 …

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 …

Prior work for articulated 3D shape reconstruction often relies on specialized multi-view and
depth sensors or pre-built deformable 3D models. Such methods do not scale to diverse sets …

Previous work shows that a better density map representation can improve the performance
of crowd counting. In this paper, we investigate learning the density map representation …

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 …

The accuracy of crowd counting in images has improved greatly in recent years due to the
development of deep neural networks for predicting crowd density maps. However, most …

Optimal transport distances have found many applications in machine learning for their
capacity to compare non-parametric probability distributions. Yet their algorithmic complexity …

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

Spatial transcriptomics (ST) measures mRNA expression across thousands of spots from a
tissue slice while recording the two-dimensional (2D) coordinates of each spot. We …

Building animatable 3D models is challenging due to the need for 3D scans, laborious
registration, and manual rigging. Recently, differentiable rendering provides a pathway to …