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Found 8 papers, 7 papers with code
Persistent homology for high-dimensional data based on spectral methods
As a remedy, we find that spectral distances on the $k$-nearest-neighbor graph of the data, such as diffusion distance and effective resistance, allow persistent homology to detect the correct topology even in the presence of high-dimensional noise.
Geometric Autoencoders -- What You See is What You Decode
Visualization is a crucial step in exploratory data analysis.
From $t$-SNE to UMAP with contrastive learning
We exploit this new conceptual connection to propose and implement a generalization of negative sampling, allowing us to interpolate between (and even extrapolate beyond) $t$-SNE and UMAP and their respective embeddings.
Directed Probabilistic Watershed
We propose the "Directed Probabilistic Watershed", an extension of the Probabilistic Watershed algorithm to directed graphs.
On UMAP's true loss function
As a consequence, we show that UMAP does not aim to reproduce its theoretically motivated high-dimensional UMAP similarities.
Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder
Motivation: Single cell RNA sequencing (scRNA-seq) data makes studying the development of cells possible at unparalleled resolution.
MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons
Instance segmentation of overlapping objects in biomedical images remains a largely unsolved problem.
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning
The seeded Watershed algorithm / minimax semi-supervised learning on a graph computes a minimum spanning forest which connects every pixel / unlabeled node to a seed / labeled node.
