1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2025
Beyond Diagonal Covariance: Flexible Posterior VAEs via Free-Form Injective Flows
Peter Sorrenson, Lukas Lührs, Hans Olischläger +1
Variational Autoencoders (VAEs) are powerful generative models widely used for learning interpretable latent spaces, quantifying uncertainty, and compressing data for downstream ge…
cs.LG2024★ 1 cited
Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension
Marek Herde, Lukas Lührs, Denis Huseljic +1
Training with noisy class labels impairs neural networks' generalization performance. In this context, mixup is a popular regularization technique to improve training robustness by…