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 …

Molecular discovery is a multi-objective optimization problem that requires identifying a
molecule or set of molecules that balance multiple, often competing, properties. Multi …

Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

The last few years have seen the development of numerous deep learning-based protein–
ligand docking methods. They offer huge promise in terms of speed and accuracy. However …

Personalized genome sequencing has revealed millions of genetic differences between
individuals, but our understanding of their clinical relevance remains largely incomplete. To …

In this paper, we introduce the Layer-Peeled Model, a nonconvex, yet analytically tractable,
optimization program, in a quest to better understand deep neural networks that are trained …

We study the scaling limits of stochastic gradient descent (SGD) with constant step-size in
the high-dimensional regime. We prove limit theorems for the trajectories of summary …

Abstract Generative Adversarial Networks (GANs) have established themselves as a
prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study …

Generalizability of algorithms for binary cancer vs. no cancer classification is unknown for
clinically more significant multi-class scenarios where intermediate categories have different …

Searching for new molecules in areas like drug discovery often starts from the core
structures of known molecules. Such a method has called for a strategy of designing …