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Found 6 papers, 1 papers with code
Resampling and averaging coordinates on data
We introduce algorithms for robustly computing intrinsic coordinates on point clouds.
Diffeomorphic interpolation for efficient persistence-based topological optimization
In particular, we show that our approach combines efficiently with subsampling techniques routinely used in TDA, as the diffeomorphism derived from the gradient computed on a subsample can be used to update the coordinates of the full input object, allowing us to perform topological optimization on point clouds at an unprecedented scale.
Topological data analysis identifies emerging adaptive mutations in SARS-CoV-2
By leveraging the stratification by time in sequence data, our method enables the high-resolution longitudinal analysis of topological signals of adaptation.
Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs
We showcase experimentally the potential of Topological Uncertainty in the context of trained network selection, Out-Of-Distribution detection, and shift-detection, both on synthetic and real datasets of images and graphs.
MREC: a fast and versatile framework for aligning and matching point clouds with applications to single cell molecular data
Comparing and aligning large datasets is a pervasive problem occurring across many different knowledge domains.
On the Metric Distortion of Embedding Persistence Diagrams into separable Hilbert spaces
Persistence diagrams are important descriptors in Topological Data Analysis.
