We introduce $ r $-loopy Weisfeiler-Leman ($ r $-$\ell $ WL), a novel hierarchy of graph
isomorphism tests and a corresponding GNN framework, $ r $-$\ell $ MPNN, that can count …

Two main families of node feature augmentation schemes have been explored for
enhancing GNNs: random features and spectral positional encoding. Surprisingly, however …

Incorporating spectral information to enhance Graph Neural Networks (GNNs) has shown
promising results but raises a fundamental challenge due to the inherent ambiguity of …

We present a novel way to predict molecular conformers through a simple formulation that
sidesteps many of the heuristics of prior works and achieves state of the art results by using …

In this paper we tackle the problem of generating conformers of a molecule in 3D space
given its molecular graph. We parameterize these conformers as continuous functions that …

We present Manifold Diffusion Fields (MDF), an approach that unlocks learning of diffusion
models of data in general non-Euclidean geometries. Leveraging insights from spectral …

Deriving from image matching and understanding, semantic keypoint matching aims at
establishing correspondence between keypoint sets in images. As graphs are powerful tools …

Abstract Graph Neural Networks (GNNs) have emerged as powerful tools for molecular
graph analysis. Subgraph-based GNNs focus on learning high-level local patterns beyond …

Controlling a group of robots with diverse morphologies using a unified policy, known as
morphology generalizable control, is a challenging problem in robotic control. Existing graph …

Accurately predicting the spatio-temporal evolution trends and long-term dynamics of three-
dimensional ocean temperature and salinity plays a crucial role in monitoring climate system …