3 papers
cs.CV2025
Toward bilipshiz geometric models
Yonatan Sverdlov, Eitan Rosen, Nadav Dym
Many neural networks for point clouds are, by design, invariant to the symmetries of this datatype: permutations and rigid motions. The purpose of this paper is to examine whether…
cs.LG2023
G-invariant diffusion maps
Eitan Rosen, Xiuyuan Cheng, Yoel Shkolnisky
The diffusion maps embedding of data lying on a manifold has shown success in tasks such as dimensionality reduction, clustering, and data visualization. In this work, we consider…
cs.LG2023
The G-invariant graph Laplacian
Eitan Rosen, Paulina Hoyos, Xiuyuan Cheng +2
Graph Laplacian based algorithms for data lying on a manifold have been proven effective for tasks such as dimensionality reduction, clustering, and denoising. In this work, we con…