no code implementations • 28 Feb 2024 • Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van Den Broeck, Julia E Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin
The field of deep generative modeling has grown rapidly and consistently over the years.
1 code implementation • 31 May 2023 • Moritz Vandenhirtz, Laura Manduchi, Ričards Marcinkevičs, Julia E. Vogt
We propose Signal is Harder (SiH), a variational-autoencoder-based method that simultaneously trains a biased and unbiased classifier using a novel, disentangling reweighting scheme inspired by the focal loss.
1 code implementation • 30 Jun 2022 • Alain Ryser, Laura Manduchi, Fabian Laumer, Holger Michel, Sven Wellmann, Julia E. Vogt
The introduced method takes advantage of the periodic nature of the heart cycle to learn three variants of a variational latent trajectory model (TVAE).
no code implementations • 24 Mar 2022 • Hanna Ragnarsdottir, Laura Manduchi, Holger Michel, Fabian Laumer, Sven Wellmann, Ece Ozkan, Julia Vogt
To the best of our knowledge, this is the first work for an automated assessment of PH in newborns using echocardiograms.
1 code implementation • 3 Mar 2022 • Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E. Vogt
We introduce reparameterizable gradients to learn the importance between groups and highlight the advantage of explicitly learning the size of subsets in two typical applications: weakly-supervised learning and clustering.
1 code implementation • NeurIPS 2021 • Laura Manduchi, Kieran Chin-Cheong, Holger Michel, Sven Wellmann, Julia E. Vogt
Constrained clustering has gained significant attention in the field of machine learning as it can leverage prior information on a growing amount of only partially labeled data.
1 code implementation • ICLR 2022 • Laura Manduchi, Ričards Marcinkevičs, Michela C. Massi, Thomas Weikert, Alexander Sauter, Verena Gotta, Timothy Müller, Flavio Vasella, Marian C. Neidert, Marc Pfister, Bram Stieltjes, Julia E. Vogt
In this work, we study the problem of clustering survival data $-$ a challenging and so far under-explored task.
no code implementations • 1 Jan 2021 • Laura Manduchi, Kieran Chin-Cheong, Holger Michel, Sven Wellmann, Julia E Vogt
Clustering with constraints has gained significant attention in the field of semi-supervised machine learning as it can leverage partial prior information on a growing amount of unlabelled data.
2 code implementations • 3 Oct 2019 • Laura Manduchi, Matthias Hüser, Julia Vogt, Gunnar Rätsch, Vincent Fortuin
We show that DPSOM achieves superior clustering performance compared to current deep clustering methods on MNIST/Fashion-MNIST, while maintaining the favourable visualization properties of SOMs.
no code implementations • 25 Sep 2019 • Laura Manduchi, Matthias Hüser, Gunnar Rätsch, Vincent Fortuin
There are very performant deep clustering models on the one hand and interpretable representation learning techniques, often relying on latent topological structures such as self-organizing maps, on the other hand.