Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

This paper introduces ModelScopeT2V, a text-to-video synthesis model that evolves from a
text-to-image synthesis model (ie, Stable Diffusion). ModelScopeT2V incorporates spatio …

While efficient distribution learning is no doubt behind the groundbreaking success of
diffusion modeling, its theoretical guarantees are quite limited. In this paper, we provide the …

Classically, data interpolation with a parametrized model class is possible as long as the
number of parameters is larger than the number of equations to be satisfied. A puzzling …

Denoising diffusion models are a recent class of generative models exhibiting state-of-the-
art performance in image and audio synthesis. Such models approximate the time-reversal …

The last decade saw an enormous boost in the field of computational topology: methods and
concepts from algebraic and differential topology, formerly confined to the realm of pure …

Video synthesis has recently made remarkable strides benefiting from the rapid
development of diffusion models. However, it still encounters challenges in terms of …

Drug discovery based on artificial intelligence has been in the spotlight recently as it
significantly reduces the time and cost required for developing novel drugs. With the …

Manifold learning (ML), also known as nonlinear dimension reduction, is a set of methods to
find the low-dimensional structure of data. Dimension reduction for large, high-dimensional …