15 citations · 29 across the 11 of their papers we have counts for
8 papers · 1 filter
Low-dimensional embeddings of high-dimensional data
Cyril de Bodt, Alex Diaz-Papkovich, Michael Bleher +18
Large collections of high-dimensional data have become nearly ubiquitous across many academic fields and application domains, ranging from biology to the humanities. Since working…
DREAMS: Preserving both Local and Global Structure in Dimensionality Reduction
Noël Kury, Dmitry Kobak, Sebastian Damrich
Dimensionality reduction techniques are widely used for visualizing high-dimensional data in two dimensions. Existing methods are typically designed to preserve either local (e.g.,…
Node Embeddings via Neighbor Embeddings
Jan Niklas Böhm, Marius Keute, Alica Guzmán +3
Node embeddings are a paradigm in non-parametric graph representation learning, where graph nodes are embedded into a given vector space to enable downstream processing. State-of-t…
On the Importance of Embedding Norms in Self-Supervised Learning
Andrew Draganov, Sharvaree Vadgama, Sebastian Damrich +4
Self-supervised learning (SSL) allows training data representations without a supervised signal and has become an important paradigm in machine learning. Most SSL methods employ th…
Persistent Homology for High-dimensional Data Based on Spectral Methods
Sebastian Damrich, Philipp Berens, Dmitry Kobak
Persistent homology is a popular computational tool for analyzing the topology of point clouds, such as the presence of loops or voids. However, many real-world datasets with low i…
Geometric Autoencoders -- What You See is What You Decode
Philipp Nazari, Sebastian Damrich, Fred A. Hamprecht
Visualization is a crucial step in exploratory data analysis. One possible approach is to train an autoencoder with low-dimensional latent space. Large network depth and width can…
From -SNE to UMAP with contrastive learning
Sebastian Damrich, Jan Niklas Böhm, Fred A. Hamprecht +1
Neighbor embedding methods -SNE and UMAP are the de facto standard for visualizing high-dimensional datasets. Motivated from entirely different viewpoints, their loss functions…
On UMAP's true loss function
Sebastian Damrich, Fred A. Hamprecht
UMAP has supplanted t-SNE as state-of-the-art for visualizing high-dimensional datasets in many disciplines, but the reason for its success is not well understood. In this work, we…