2 citations · 2 across the 8 of their papers we have counts for
5 papers · 1 filter
Riemannian Metric Matching for Scalable Geometric Modeling of Distributions
Jacob Bamberger, Adam Gosztolai, Pierre Vandergheynst +2
High-dimensional datasets often concentrate near low-dimensional structures, but estimating their geometry from samples typically relies on graphs and kernels that scale poorly wit…
Diffusion Processes on Implicit Manifolds
Victor Kawasaki-Borruat, Clara Grotehans, Pierre Vandergheynst +1
High-dimensional data are often assumed to lie on lower-dimensional manifolds. We study how to construct diffusion processes on this data manifold using only point cloud samples an…
All-Atom GPCR-Ligand Dynamics Simulation via a Residual Latent Flow Mode
Jiying Zhang, Shuhao Zhang, Pierre Vandergheynst +1
G-protein-coupled receptors (GPCRs), which are targeted by over one-third of approved drugs, undergo intricate conformational transitions to transduce signals. While Molecular Dyna…
Geometry-Induced Diffusion on Graphs: A Learnable Weighted Laplacian for Spectral GNNs
Mia Zosso, Ali Hariri, Victor Kawasaki-Borruat +2
Long-range graph tasks are challenging for Graph Neural Networks (GNNs): global mechanisms such as attention or rewiring schemes can be computationally expensive, while deep local…
MEIDNet: Multimodal generative AI framework for inverse materials design
Anand Babu, Rogério Almeida Gouvêa, Pierre Vandergheynst +1
In this work, we present Multimodal Equivariant Inverse Design Network (MEIDNet), a framework that jointly learns structural information and materials properties through contrastiv…