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 …

Recently, Optimal Transport has been proposed as a probabilistic framework in Machine
Learning for comparing and manipulating probability distributions. This is rooted in its rich …

We introduce the Falcon series: 7B, 40B, and 180B parameters causal decoder-only models
trained on a diverse high-quality corpora predominantly assembled from web data. The …

We present ScanNet++, a large-scale dataset that couples together capture of high-quality
and commodity-level geometry and color of indoor scenes. Each scene is captured with a …

Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but
they have thus far been held back by limitations in their simulation-based maximum …

Solving transport problems, ie finding a map transporting one given distribution to another,
has numerous applications in machine learning. Novel mass transport methods motivated …

Diffusion models have emerged as powerful generative models in the text-to-image domain.
This paper studies their application as observation-to-action models for imitating human …

Normalizing flows are a class of deep generative models that are especially interesting for
modeling probability distributions in physics, where the exact likelihood of flows allows …

We focus on reconstructing high-fidelity radiance fields of human heads, capturing their
animations over time, and synthesizing re-renderings from novel viewpoints at arbitrary time …

Abstract Graph Neural Networks (GNNs) had been demonstrated to be inherently
susceptible to the problems of over-smoothing and over-squashing. These issues prohibit …