177 citations · 519 across the 32 of their papers we have counts for
7 papers · 1 filter
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…
Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources
Peter Lippmann, Enrique Fita Sanmartín, Fred A. Hamprecht
Branched Optimal Transport (BOT) is a generalization of optimal transport in which transportation costs along an edge are subadditive. This subadditivity models an increase in tran…
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…
Learning Steerable Filters for Rotation Equivariant CNNs
Maurice Weiler, Fred A. Hamprecht, Martin Storath
In many machine learning tasks it is desirable that a model's prediction transforms in an equivariant way under transformations of its input. Convolutional neural networks (CNNs) i…
Active Learning with Distributional Estimates
Jens Roeder, Boaz Nadler, Kevin Kunzmann +1
Active Learning (AL) is increasingly important in a broad range of applications. Two main AL principles to obtain accurate classification with few labeled data are refinement of th…