3 citations · 5 across the 7 of their papers we have counts for
6 papers · 1 filter
Rethinking Machine Unlearning: Models Designed to Forget via Key Deletion
Sonia Laguna, Jorge da Silva Goncalves, Moritz Vandenhirtz +3
Machine unlearning is rapidly becoming a practical requirement, driven by privacy regulations, data errors, and the need to remove harmful or corrupted training samples. Despite th…
From Pixels to Components: Eigenvector Masking for Visual Representation Learning
Alice Bizeul, Thomas Sutter, Alain Ryser +3
Predicting masked from visible parts of an image is a powerful self-supervised approach for visual representation learning. However, the common practice of masking random patches o…
From Logits to Hierarchies: Hierarchical Clustering made Simple
Emanuele Palumbo, Moritz Vandenhirtz, Alain Ryser +2
The hierarchical structure inherent in many real-world datasets makes the modeling of such hierarchies a crucial objective in both unsupervised and supervised machine learning. Whi…
Two Is Better Than One: Aligned Representation Pairs for Anomaly Detection
Alain Ryser, Thomas M. Sutter, Alexander Marx +1
Anomaly detection focuses on identifying samples that deviate from the norm. Discovering informative representations of normal samples is crucial to detecting anomalies effectively…
Tree Variational Autoencoders
Laura Manduchi, Moritz Vandenhirtz, Alain Ryser +1
We propose Tree Variational Autoencoder (TreeVAE), a new generative hierarchical clustering model that learns a flexible tree-based posterior distribution over latent variables. Tr…
Differentiable Random Partition Models
Thomas M. Sutter, Alain Ryser, Joram Liebeskind +1
Partitioning a set of elements into an unknown number of mutually exclusive subsets is essential in many machine learning problems. However, assigning elements, such as samples in…