Medical imaging is crucial in modern clinics to provide guidance to the diagnosis and
treatment of diseases. Medical image reconstruction is one of the most fundamental and …

As one of the most popular approaches in artificial intelligence, deep learning (DL) has
attracted a lot of attention in the medical physics field over the past few years. The goals of …

Deep learning achieves state-of-the-art results in many tasks in computer vision and natural
language processing. However, recent works have shown that deep networks can be …

We describe the new field of the mathematical analysis of deep learning. This field emerged
around a list of research questions that were not answered within the classical framework of …

Abstract Mean-Field Game (MFG) serves as a crucial mathematical framework in modeling
the collective behavior of individual agents interacting stochastically with a large population …

Viewing Transformers as interacting particle systems, we describe the geometry of learned
representations when the weights are not time-dependent. We show that particles …

Well known to the machine learning community, the random feature model is a parametric
approximation to kernel interpolation or regression methods. It is typically used to …

Attention based models such as Transformers involve pairwise interactions between data
points, modeled with a learnable attention matrix. Importantly, this attention matrix is …

Diverse inverse problems in imaging can be cast as variational problems composed of a
task-specific data fidelity term and a regularization term. In this paper, we propose a novel …

Nonlinear differential equations rarely admit closed-form solutions, thus requiring numerical
time-stepping algorithms to approximate solutions. Further, many systems characterized by …