6 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…
MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Models
Kacper Kapuśniak, Cristian Gabellini, Michael Bronstein +2
Molecular Dynamics (MD) is a powerful computational microscope for probing protein functions. However, the need for fine-grained integration and the long timescales of biomolecular…
Relaxed Equivariance via Multitask Learning
Ahmed A. Elhag, T. Konstantin Rusch, Francesco Di Giovanni +1
Incorporating equivariance as an inductive bias into deep learning architectures to take advantage of the data symmetry has been successful in multiple applications, such as chemis…
Metric Flow Matching for Smooth Interpolations on the Data Manifold
Kacper Kapuśniak, Peter Potaptchik, Teodora Reu +5
Matching objectives underpin the success of modern generative models and rely on constructing conditional paths that transform a source distribution into a target distribution. Des…
A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition
Nian Liu, Xiaoxin He, Thomas Laurent +3
Spectral graph convolution, an important tool of data filtering on graphs, relies on two essential decisions: selecting spectral bases for signal transformation and parameterizing…
Understanding Virtual Nodes: Oversquashing and Node Heterogeneity
Joshua Southern, Francesco Di Giovanni, Michael Bronstein +1
While message passing neural networks (MPNNs) have convincing success in a range of applications, they exhibit limitations such as the oversquashing problem and their inability to…