Search Results for author:
Found 9 papers, 2 papers with code
Pullback Flow Matching on Data Manifolds
We propose Pullback Flow Matching (PFM), a novel framework for generative modeling on data manifolds.
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM).
Can strong structural encoding reduce the importance of Message Passing?
This suggests that the importance of message passing is limited when the model can construct strong structural encodings.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
E(n) Equivariant Message Passing Simplicial Networks
This paper presents $\mathrm{E}(n)$ Equivariant Message Passing Simplicial Networks (EMPSNs), a novel approach to learning on geometric graphs and point clouds that is equivariant to rotations, translations, and reflections.
Interpretable ECG classification via a query-based latent space traversal (qLST)
Electrocardiography (ECG) is an effective and non-invasive diagnostic tool that measures the electrical activity of the heart.
PDE-based Group Equivariant Convolutional Neural Networks
We solve the PDE of interest by a combination of linear group convolutions and non-linear morphological group convolutions with analytic kernel approximations that we underpin with formal theorems.
Vesselness via Multiple Scale Orientation Scores
The multi-scale Frangi vesselness filter is an established tool in (retinal) vascular imaging.
A Multi-Orientation Analysis Approach to Retinal Vessel Tracking
This paper presents a method for retinal vasculature extraction based on biologically inspired multi-orientation analysis.
