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Found 11 papers, 2 papers with code
The fshape framework for the variability analysis of functional shapes
This article introduces a full mathematical and numerical framework for treating functional shapes (or fshapes) following the landmarks of shape spaces and shape analysis.
Metamorphoses of functional shapes in Sobolev spaces
In this paper, we describe in detail a model of geometric-functional variability between fshapes.
Optimization and Control Differential Geometry 49M25, 49Q20, 58B32, 58E50, 68U05, 68U10
A General Framework for Curve and Surface Comparison and Registration With Oriented Varifolds
This paper introduces a general setting for the construction of data fidelity metrics between oriented or non-oriented geometric shapes like curves, curve sets or surfaces.
White Matter Fiber Segmentation Using Functional Varifolds
We use it to cluster fibers with a dictionary learning and sparse coding-based framework, and present a preliminary analysis using HCP data.
Prediction of the progression of subcortical brain structures in Alzheimer's disease from baseline
We propose a method to predict the subject-specific longitudinal progression of brain structures extracted from baseline MRI, and evaluate its performance on Alzheimer's disease data.
Parallel transport in shape analysis: a scalable numerical scheme
The analysis of manifold-valued data requires efficient tools from Riemannian geometry to cope with the computational complexity at stake.
Kernel Operations on the GPU, with Autodiff, without Memory Overflows
The KeOps library provides a fast and memory-efficient GPU support for tensors whose entries are given by a mathematical formula, such as kernel and distance matrices.
Fast geometric learning with symbolic matrices
Geometric methods rely on tensors that can be encoded using a symbolic formula and data arrays, such as kernel and distance matrices.
IMODAL: Creating Learnable User-Defined Deformation Models
A natural way to model the evolution of an object (growth of a leaf for instance) is to estimate a plausible deforming path between two observations.
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice.
Identify ambiguous tasks combining crowdsourced labels by weighting Areas Under the Margin
We adapt the AUM to identify ambiguous tasks in crowdsourced learning scenarios, introducing the Weighted Areas Under the Margin (WAUM).
