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аналитика - Geometric Deep Learning
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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