Geometric Deep Learning

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Equivariant Convolution Frameworks for Representation Learning on Non-Euclidean Domains

Geometric deep learning models leveraging symmetry group equivariance can effectively represent and process non-Euclidean data like graphs and manifolds, achieving improved statistical efficiency, interpretability, and generalization.

MAgNET: A Novel Graph U-Net Architecture for Efficient Mesh-Based Simulations

MAgNET is a novel graph U-Net framework that extends convolutional neural networks to handle arbitrary graph-structured data, enabling efficient surrogate modeling for computationally expensive mesh-based simulations.

Simplicial Representation Learning with Neural k-Forms at ICLR 2024

Leveraging differential k-forms in Rn for efficient and interpretable geometric representation learning without message passing.

Simplicial Representation Learning with Neural k-Forms: A Geometric Deep Learning Approach

Leveraging differential k-forms in Rn for geometric deep learning without message passing.

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