KeOps:
Kernel Operations on the GPU, with autodiff, without memory overflows
KeOps is a specialized library for fast, memory-efficient, and scalable computation of kernel operations on GPUs. It is useful for anyone working with large datasets in machine learning, signal processing, and statistical analysis, especially when kernel methods are involved. Pyding are available in R and Python.
KeOps is a specialized library for fast, memory-efficient, and scalable computation of kernel operations on GPUs. It is useful for anyone working with large datasets in machine learning, signal processing, and statistical analysis, especially when kernel methods are involved. Pyding are available in R and Python.
Implicit modules:
déformations non rigides via des contraintes implicites
Next-gen non-rigid deformation models with implicit modular definition.
Next-gen non-rigid deformation models with implicit modular definition.
Deformetrica:
statistical analysis of 2d and 3d shapes
A comprehensive software for the statistical treatment of shapes (curves, surfaces, images...) and longitudinal data. Developed in Python by the Aramis team.
A comprehensive software for the statistical treatment of shapes (curves, surfaces, images...) and longitudinal data. Developed in Python by the Aramis team.
Fshapes Toolkit:
functional shapes (fshapes) atlas estimation
A toolkit written in Matlab, C, and Cuda to analyze functional shapes (sets of landmarks, curves, or surfaces on which a scalar field is defined). A short movie illustrating an atlas estimation on the OCT dataset.
A toolkit written in Matlab, C, and Cuda to analyze functional shapes (sets of landmarks, curves, or surfaces on which a scalar field is defined). A short movie illustrating an atlas estimation on the OCT dataset.
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