Cellular sheaves equip graphs with a``geometrical''structure by assigning vector spaces and
linear maps to nodes and edges. Graph Neural Networks (GNNs) implicitly assume a graph …

Graph Convolutional Networks (GCNs) are leading methods for learning graph
representations. However, without specially designed architectures, the performance of …

A tool that could suggest new personalized research directions and ideas by taking insights
from the scientific literature could profoundly accelerate the progress of science. A field that …

Recent advances in network embedding have revolutionized the field of graph and network
mining. However,(pre-) training embeddings for very large-scale networks is computationally …

Transformers were originally proposed as a sequence-to-sequence model for text but have
become vital for a wide range of modalities, including images, audio, video, and undirected …

Many important geometric estimation problems naturally take the form of synchronization
over the special Euclidean group: estimate the values of a set of unknown group elements x …

We estimate n phases (angles) from noisy pairwise relative phase measurements. The task
is modeled as a nonconvex least-squares optimization problem. It was recently shown that …

This paper outlines a program in what one might call spectral sheaf theory—an extension of
spectral graph theory to cellular sheaves. By lifting the combinatorial graph Laplacian to the …

The problem of matching not just two, but m different sets of objects to each other arises in a
variety of contexts, including finding the correspondence between feature points across …

The synchronization problem over the special orthogonal group SO (d) consists of
estimating a set of unknown rotations from noisy measurements of a subset of their pairwise …